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BUSINESS RESEARCH METHODS

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BUSINESS RESEARCH METHODS

Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States

William G. Zikmund

Barry J. Babin Max P. Watson Professor of Business

Louisiana Tech University Head, Department of Marketing & Analysis

Jon C. Carr M.J. Neeley School of Business

Texas Christian University Department of Management, Entrepreneurship, & Leadership

Mitch Griffin Professor of Marketing

Bradley University

N I N T H E D I T I O N

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Business Research Methods, Ninth Edition William G. Zikmund Barry J. Babin Jon C. Carr Mitch Griffin

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To our families.

Preface, xviii

PART ONE Introduction, 1 1 The Role of Business Research, 2 2 Information Systems and Knowledge Management, 17 3 Theory Building, 37 4 The Business Research Process: An Overview, 48 5 The Human Side of Business Research: Organizational and Ethical Issues, 73

PART TWO Beginning Stages of the Research Process, 105 6 Problem Definition: The Foundation of Business Research, 106 7 Qualitative Research Tools, 131 8 Secondary Data Research in a Digital Age, 159

PART THREE Research Methods for Collecting Primary Data, 183 9 Survey Research: An Overview, 184 10 Survey Research: Communicating with Respondents, 205 11 Observation Methods, 235 12 Experimental Research, 253

PART FOUR Measurement Concepts, 287 13 Measurement and Scaling Concepts, 288 14 Attitude Measurement, 310 15 Questionnaire Design, 333

PART FIVE Sampling and Fieldwork, 383 16 Sampling Designs and Sampling Procedures, 384 17 Determination of Sample Size: A Review of Statistical Theory, 409 18 Fieldwork, 441

PART SIX Data Analysis and Presentation, 457 19 Editing and Coding: Transforming Raw Data into Information, 458 20 Basic Data Analysis: Descriptive Statistics, 483 21 Univariate Statistical Analysis, 506 22 Bivariate Statistical Analysis: Differences Between Two Variables, 528 23 Bivariate Statistical Analysis: Measures of Association, 560 24 Multivariate Statistical Analysis, 582 25 Communicating Research Results: Report Generation, Oral Presentation,

and Follow-Up, 610

PART SEVEN Comprehensive Cases with Computerized Databases, 635

Glossary of Frequently Used Symbols, 645 Glossary, 646 Endnotes, 659 Index, 666

BRIEF CONTENTS

vii

CONTENTS

Preface, xviii

PART ONE Introduction

CHAPTER 1 The Role of Business Research, 2 Introduction, 3

Survey This!, 4

The Nature of Business Research, 4 Business Research Defined, 5 Applied and Basic Business Research, 5 The Scientific Method, 6

Managerial Value of Business Research, 7 Identifying Problems or Opportunities, 8 Diagnosing and Assessing Problems or Opportunities, 8 Selecting and Implementing a Course of Action, 8 Evaluating the Course of Action, 9

Research Snapshot Harley-Davidson Goes Abroad, 10

When Is Business Research Needed?, 10 Time Constraints, 10 Availability of Data, 10

Research Snapshot Business Class Success?, 11 Nature of the Decision, 11 Benefits versus Costs, 11

Business Research in the Twenty-First Century, 12 Communication Technologies, 12

Research Snapshot “Jacques” Daniels, 13 Global Business Research, 13

Overview, 14

Summary, 15 Key Terms and Concepts, 16

Questions for Review and Critical Thinking, 16 Research Activities, 16

CHAPTER 2 Information Systems and Knowledge Management, 17 Introduction, 18

Information, Data, and Intelligence, 18

The Characteristics of Valuable Information, 19 Relevance, 19

Survey This!, 20 Quality, 20 Timeliness, 20 Completeness, 20

Research Snapshot RFID Technology Gets Cheaper—Business Knowledge Grows, 21

Knowledge Management, 21

Research Snapshot Are Businesses Clairvoyant?, 22

Global Information Systems, 22

Decision Support Systems, 23 Databases and Data Warehousing, 24 Input Management, 24

Research Snapshot Staying Home at Home Depot, 26 Computerized Data Archives, 27 Networks and Electronic Data Interchange, 29

The Internet and Research, 29 What Exactly Is the Internet?, 29 How Is the Internet Useful in Research?, 30 Navigating the Internet, 31 Interactive Media and Environmental Scanning, 31 Information Technology, 32

Intranets, 33 Internet2, 33

Summary, 34 Key Terms and Concepts, 35 Questions for Review and Critical Thinking, 35 Research Activities, 35

CASE 2.1 Harvard Cooperative Society, 36

CHAPTER 3 Theory Building, 37 Introduction, 38

What Is a Theory?, 38 What Are the Goals of Theory?, 38

Survey This!, 39

Research Concepts, Constructs, Propositions, Variables, and Hypotheses, 39

Research Concepts and Constructs, 39

Research Snapshot Social Network Theory, 40 Research Propositions and Hypotheses, 41

Understanding Theory, 42 Verifying Theory, 43 Theory Building, 43

The Scientific Method, 44

Practical Value of Theories, 46

Summary, 46 Key Terms and Concepts, 47 Questions for Review and Critical Thinking, 47 Research Activities, 47

CHAPTER 4 The Business Research Process: An Overview, 48 Introduction, 49

Decision Making, 49 Certainty, 49

Survey This!, 50 Uncertainty, 50 Ambiguity, 50

Types of Business Research, 51

Research Snapshot Cute, Funny, or Sexy? What Makes a Mascot Tick?, 52

Exploratory Research, 52 Descriptive Research, 53

Research Snapshot Taking a Swing at Business Success, 54

Causal Research, 54 Uncertainty Influences the Type of Research, 58

Stages in the Research Process, 59 Alternatives in the Research Process, 60 Defining the Research Objectives, 60 Planning the Research Design, 64

Research Snapshot Rolling Rock, 66 Sampling, 66 Gathering Data, 67 Processing and Analyzing Data, 68 Drawing Conclusions and Preparing a Report, 68

The Research Program Strategy, 68

Summary, 69 Key Terms and Concepts, 70 Questions for Review and Critical Thinking, 70 Research Activities, 71

CASE 4.1 A New “Joe” on the Block, 71

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues, 73 Introduction, 74

Survey This!, 76

Organizational Structure of Business Research, 76 Business Research Jobs, 77 The Director of Research as a Manager, 79 Sources of Conflict between Senior Management

and Research, 79

Research Snapshot The True Power of Research, 80

Research Snapshot When Your Brain “Trips Up,” 81 Reducing the Conflict between Management

and Researchers, 82 Cross-Functional Teams, 84

Research Suppliers and Contractors, 85 Syndicated Service, 85 Standardized Research Services, 85

Research Snapshot Finding Häagen-Dazs in China, 86 Limited Research Service Companies and

Custom Research, 86 Largest Research Organizations, 86

Ethical Issues in Business Research, 87 Ethical Questions Are Philosophical Questions, 88 General Rights and Obligations of Concerned Parties, 88 Rights and Obligations of the Research Participant, 89

Research Snapshot Crazy Good! Have Fun, Play Games (and Buy Pop-Tarts)!, 92

Rights and Obligations of the Researcher, 93

Research Snapshot Is It Right, or Is It Wrong?, 95 Rights and Obligations of the Client Sponsor (User), 99 Privacy, 100 Privacy on the Internet, 100 A Final Note on Ethics, 100

Summary, 101 Key Terms and Concepts, 102 Questions for Review and Critical Thinking, 102 Research Activities, 103

CASE 5.1 Global Eating, 103 CASE 5.2 Big Brother Is Watching?, 104

viii Contents

PART TWO Beginning Stages of the Research Process

CHAPTER 6 Problem Definition: The Foundation of Business Research, 106 Introduction, 107

Importance of Starting with a Good Problem Definition, 107

Survey This!, 108 Problem Complexity, 108

Research Snapshot Good Answers, Bad Questions?, 109

The Problem-Definition Process, 111 Problems Mean Gaps, 111 The Problem-Definition Process Steps, 111

Research Snapshot Poor Questions Result in Poor Research in Japan!, 112

Understand the Business Decision, 112

Research Snapshot Opportunity Is a “Fleeting” Thing, 115

Identifying the Relevant Issues from the Symptoms, 116 Writing Managerial Decision Statements and

Corresponding Research Objectives, 116 Determine the Unit of Analysis, 118 Determine Relevant Variables, 118 Write Research Objectives and Questions, 120

Research Snapshot Pricing Turbulence, 121

Clarity in Research Questions and Hypotheses, 121

How Much Time Should Be Spent on Problem Definition?, 123

The Research Proposal, 123 The Proposal As a Planning Tool, 123 The Proposal as a Contract, 125 Anticipating Outcomes, 126

Summary, 128 Key Terms and Concepts, 128 Questions for Review and Critical Thinking, 129 Research Activities, 129

CASE 6.1 E-ZPass, 130 CASE 6.2 Cane’s Goes International, 130 CASE 6.3 Deland Trucking, 130

CHAPTER 7 Qualitative Research Tools, 131 Introduction, 132

What Is Qualitative Research?, 132 Uses of Qualitative Research, 132

Survey This!, 133

Qualitative “versus” Quantitative Research, 133

Research Snapshot Discoveries at P&G!, 134 Contrasting Qualitative and Quantitative Methods, 134 Contrasting Exploratory and Confirmatory Research, 135

Orientations to Qualitative Research, 136 Phenomenology, 136

Research Snapshot “When Will I Ever Learn?,” 137 Ethnography, 138 Grounded Theory, 139 Case Studies, 139

Research Snapshot A Sensory Safari Provides Play Time (and Good Research) for Time Warner Cable, 140

Common Techniques Used in Qualitative Research, 140

What Is a Focus Group Interview?, 141 Depth Interviews, 149 Conversations, 150 Free-Association/Sentence Completion Method, 151 Observation, 152 Collages, 152 Projective Research Techniques, 152

Exploratory Research in Science and in Practice, 153 Misuses of Exploratory and Qualitative Research, 153

Summary, 155 Key Terms and Concepts, 156 Questions for Review and Critical Thinking, 157 Research Activities, 157

CASE 7.1 Disaster and Consumer Value, 157 CASE 7.2 Edward Jones, 158

CHAPTER 8 Secondary Data Research in a Digital Age, 159 Introduction, 160

Advantages of Secondary Data, 160 Disadvantages of Secondary Data, 160

Survey This!, 161

Typical Objectives for Secondary Data Research Designs, 162

Fact-Finding, 162

Research Snapshot Does It Matter?, 165 Model Building, 165 Data Mining, 168

Research Snapshot Mining Data from Blogs, 169 Database Marketing and Customer Relationship

Management, 170

Sources of Secondary Data, 170 Sources of Internal and Proprietary Data, 171 External Data: The Distribution System, 171 Information As a Product and Its Distribution Channels, 171

Research Snapshot What’s That Buzzing Sound?, 175

Single-Source Data-Integrated Information, 177

Sources for Global Research, 177

Contents ix

Research Snapshot Around the World of Data, 178

Summary, 180 Key Terms and Concepts, 180 Questions for Review and Critical Thinking, 181 Research Activities, 181

CASE 8.1 Demand for Gas Guzzlers, 182

PART THREE Research Methods for Collecting Primary Data

CHAPTER 9 Survey Research: An Overview, 184 Introduction, 185

Using Surveys, 185

Survey This!, 186 Advantages of Surveys, 186

Research Snapshot Intuit Gets Answers to Satisfy Customers, 187

Errors in Survey Research, 187 Random Sampling Error, 188 Systematic Error, 188

Research Snapshot Overestimating Patient Satisfaction, 189

Respondent Error, 189 Administrative Error, 193 Rule-of-Thumb Estimates for Systematic Error, 194 What Can Be Done to Reduce Survey Error?, 194

Classifying Survey Research Methods, 194 Structured/Unstructured and Disguised/Undisguised

Questionnaires, 194

Research Snapshot The “Mere-Measurement” Effect, 195 Temporal Classification, 195

Total Quality Management and Customer Satisfaction Surveys, 197

What Is Quality?, 198 Internal and External Customers, 198 Implementing Total Quality Management, 198

Summary, 201 Key Terms and Concepts, 202 Questions for Review and Critical Thinking, 203 Research Activities, 203

CASE 9.1 SAT and ACT Writing Tests, 204 CASE 9.2 The Walker Information Group, 204

CHAPTER 10 Survey Research: Communicating with Respondents, 205 Introduction, 206

Interviews as Interactive Communication, 206 Noninteractive Media, 206

Personal Interviews, 206

Survey This!, 207 Advantages of Personal Interviews, 207

Research Snapshot The Challenge of Assessing Adult Literacy, 208

Disadvantages of Personal Interviews, 209 Door-to-Door Interviews and Shopping Mall Intercepts, 210

Research Snapshot Being Good Neighbors Means Learning about Them First, 211

Global Considerations, 212

Telephone Interviews, 212 Mobile Phone Interviews, 213 Phone Interview Characteristics, 213 Central Location Interviewing, 215

Research Snapshot Automated Phone Surveys of Teens, 216

Computer-Assisted Telephone Interviewing, 216 Computerized Voice-Activated Telephone Interview, 216 Global Considerations, 217

Self-Administered Questionnaires, 217 Mail Questionnaires, 217 Response Rates, 219 Increasing Response Rates for Mail Surveys, 220 Global Considerations, 223

Self-Administered Questionnaires Using Other Forms of Distribution, 223

Fax Surveys, 223 E-Mail Surveys, 224 Internet Surveys, 225

Research Snapshot Mixed Mode Data Collection: The Case of Web and Telephone Surveys, 226

Kiosk Interactive Surveys, 228 Survey Research That Mixes Modes, 229 Text-Message Surveys, 229

Selecting the Appropriate Survey Research Design, 229

Pretesting, 231

Ethical Issues in Survey Research, 231

Summary, 231 Key Terms and Concepts, 232 Questions for Review and Critical Thinking, 232 Research Activities, 233

CASE 10.1 National Do Not Call Registry, 233 CASE 10.2 Royal Bee Electric Fishing Reel, 234

CHAPTER 11 Observation Methods, 235 Introduction, 236

Observation in Business Research, 236 What Can Be Observed?, 236

Survey This!, 237 The Nature of Observation Studies, 237

Research Snapshot This Trend Brought to You by DDB SignBank, 238

x Contents

Observation of Human Behavior, 238 Complementary Evidence, 239 Direct Observation, 240 Combining Direct Observation and Interviewing, 241 Ethical Issues in the Observation of Humans, 241

Observation of Physical Objects, 242

Content Analysis, 243

Mechanical Observation, 244 Television Monitoring, 244

Research Snapshot ATTI, Inc. Shadows the Fleet, 245 Monitoring Website Traffic, 246 Scanner-Based Research, 246 Measuring Physiological Reactions, 247

Research Snapshot Neuroco Peers into the Consumer’s Brain, 248

Summary, 249 Key Terms and Concepts, 250 Questions for Review and Critical Thinking, 250 Research Activities, 251

CASE 11.1 Mazda and Syzygy, 251 CASE 11.2 Texas Instruments and E-Lab, 252

CHAPTER 12 Experimental Research, 253 Introduction, 254

Creating an Experiment, 254 An Illustration: Can a Self-Efficacy Intervention Enhance

Job Attitude?, 254

Survey This!, 255

Designing an Experiment to Minimize Experimental Error, 257

Manipulation of the Independent Variable, 257

Research Snapshot Talking While Driving: Are Cell Phone Conversations Different from Passenger Conversations?, 259

Selection and Measurement of the Dependent Variable, 260 Selection and Assignment of Test Units, 260

Demand Characteristics, 263 What Are Demand Characteristics?, 263 Experimenter Bias and Demand Effects, 263 Hawthorne Effect, 264 Reducing Demand Characteristics, 265

Establishing Control, 266 Problems Controlling Extraneous Variables, 266

Ethical Issues in Experimentation, 267

Practical Experimental Design Issues, 267 Basic versus Factorial Experimental Designs, 267 Laboratory Experiments, 267 Field Experiments, 268

Research Snapshot The Hidden in Hidden Valley Ranch, 269

Within-Subjects and Between-Subjects Designs, 270

Issues of Experimental Validity, 271 Internal Validity, 271 External Validity, 273 Trade-offs between Internal and External

Validity, 274

Classification of Experimental Designs, 274 Symbolism for Diagramming Experimental

Designs, 274 Three Examples of Quasiexperimental Designs, 274 Three Alternative Experimental Designs, 276 Time Series Designs, 278 Complex Experimental Designs, 278

Summary, 282 Key Terms and Concepts , 283 Questions for Review and Critical Thinking, 284 Research Activities, 284

CASE 12.1 Tooheys, 285

PART FOUR Measurement Concepts

CHAPTER 13 Measurement and Scaling Concepts, 288 Introduction, 289

What Do I Measure?, 289

Survey This!, 290

Research Snapshot Peer Pressure and Investing Behavior, 292

Concepts, 292 Operational Definitions, 292

Levels of Scale Measurement, 293 Nominal Scale, 293 Ordinal Scale, 295 Interval Scale, 297 Ratio Scale, 297 Mathematical and Statistical Analysis

of Scales, 298

Index Measures, 299 Indexes and Composites, 299 Computing Scale Values, 300

Research Snapshot Recoding Made Easy, 301

Three Criteria for Good Measurement, 301 Reliability, 301 Validity, 303 Reliability versus Validity, 305 Sensitivity, 305

Summary, 306 Key Terms and Concepts, 306 Questions for Review and Critical Thinking, 307 Research Activities, 307

CASE 13.1 FlyAway Airways, 308

Contents xi

CHAPTER 14 Attitude Measurement, 310 Introduction, 311

Attitudes as Hypothetical Constructs, 311

Survey This!, 312 Importance of Measuring Attitudes, 312

Techniques for Measuring Attitudes, 312

Research Snapshot Is It Positive Emotionality, or Is It LOVE?, 313

Attitude Rating Scales, 313 Simple Attitude Scales, 313

Research Snapshot Students Ask—Are You Responsible?, 314 Category Scales, 314 Method of Summated Ratings: The Likert Scale, 315 Semantic Differential, 317

Research Snapshot Measuring Website Usability, 319 Numerical Scales, 319 Stapel Scale, 319 Constant-Sum Scale, 320 Graphic Rating Scales, 321

Research Snapshot How Much Is a Healthy Home Worth?, 322

Thurstone Interval Scale, 322

Measuring Behavioral Intention, 323 Behavioral Differential, 324

Ranking, 324 Paired Comparisons, 324 Sorting, 325

Other Methods of Attitude Measurement, 325

Selecting a Measurement Scale: Some Practical Decisions, 326

Ranking, Sorting, Rating, or Choice Technique?, 326 Monadic or Comparative Scale?, 326 What Type of Category Labels, If Any?, 327 How Many Scale Categories or Response Positions?, 327 Balanced or Unbalanced Rating Scale?, 327 Even or Odd Number of Scale Points?, 328 Use a Scale That Forces a Choice among Predetermined

Options?, 328 Single Measure or an Index Measure?, 329

Summary, 329 Key Terms and Concepts, 330 Questions for Review and Critical Thinking, 330 Research Activity, 331

CASE 14.1 Roeder-Johnson Corporation, 331 CASE 14.2 Attitudes toward Technology and Lifestyle, 331

CHAPTER 15 Questionnaire Design, 333 Introduction, 334

Questionnaire Quality and Design: Basic Considerations, 334

What Should Be Asked?, 334

Survey This!, 335 Questionnaire Relevancy, 335 Questionnaire Accuracy, 335

Wording Questions, 336 Open-Ended Response versus Fixed-Alternative

Questions, 336

Research Snapshot Corporate Reputations: Consumers Put Johnson & Johnson, Microsoft, and Google on Top, 337

Types of Fixed-Alternative Questions, 338 Phrasing Questions for Self-Administered, Telephone, and

Personal Interview Surveys, 340

Guidelines for Constructing Questions, 341 Avoid Complexity: Use Simple, Conversational

Language, 341 Avoid Leading and Loaded Questions, 342 Avoid Ambiguity: Be as Specific as Possible, 343 Avoid Double-Barreled Items, 344 Avoid Making Assumptions, 345 Avoid Burdensome Questions That May Tax the

Respondent’s Memory, 345 Make Certain Questions Generate Variance, 346

What Is the Best Question Sequence?, 347

Research Snapshot What Citizens Think about Climate Change, 348

What Is the Best Layout?, 350 Traditional Questionnaires, 350 Internet Questionnaires, 355

Research Snapshot Pretesting the CAHPS Hospital Survey, 359

How Much Pretesting and Revising Are Necessary?, 360

Designing Questionnaires for Global Markets, 361

Summary, 362 Key Terms and Concepts, 363 Questions for Review and Critical Thinking, 363 Research Activity, 364

CASE 15.1 Agency for Healthcare Research and Quality, 364 CASE 15.2 Canterbury Travels, 369 CASE 15.3 McDonald’s Spanish Language Questionnaire, 371 CASE 15.4 Schönbrunn Palace in Vienna, 372

APPENDIX 15A Question Wording and Measurement Scales for Commonly Researched Topics, 373 Questions about Advertising, 373

Awareness, 373 Unaided Recall/ Top of the Mind Recall, 374 Aided Recall, 374 Recognition, 374

xii Contents

Message Communication/Playback (Sales Point Playback), 374

Attitude toward the Advertisement, 375 Attitude toward Advertised Brand (Persuasion), 375 Readership/Viewership, 375

Questions about Ownership and Product Usage, 376

Ownership, 376 Purchase Behavior, 376 Regular Usage, 376

Questions about Goods and Services, 376 Ease of Use, 376 Uniqueness, 377 Attribute Ratings/Importance of Characteristics, 377 Interest, 377 Like/Dislike, 378 Preference, 378 Expectations, 378 Satisfaction, 378 Quality, 379 Problems, 379 Benefits, 379 Improvements, 379 Buying Intentions for Existing Products, 379 Buying Intentions Based on Product Concept, 380 Reason for Buying Intention, 380

Questions about Demographics, 380 Age, 380

Education, 381

Marital Status, 381

Children, 381

Income, 381 Occupation, 382

PART FIVE Sampling and Fieldwork

CHAPTER 16 Sampling Designs and Sampling Procedures, 384 Introduction, 385

Sampling Terminology, 385

Why Sample?, 385 Pragmatic Reasons, 385

Survey This!, 386 Accurate and Reliable Results, 386 Destruction of Test Units, 386

Research Snapshot Finding Out about Work Is a Lot of Work!, 387

Practical Sampling Concepts, 387 Defining the Target Population, 387 The Sampling Frame, 388 Sampling Units, 390

Random Sampling and Nonsampling Errors, 390

Random Sampling Error, 391 Systematic Sampling Error, 391 Less Than Perfectly Representative Samples, 391

Probability versus Nonprobability Sampling, 392

Nonprobability Sampling, 392 Convenience Sampling, 393

Research Snapshot How Much Does Your Prescription Cost? It Depends on Who You Buy It From, 393

Judgment Sampling, 393

Research Snapshot American Kennel Club Tries to Keep Pet Owners out of the Doghouse, 394

Quota Sampling, 394 Snowball Sampling, 395

Probability Sampling, 395 Simple Random Sampling, 396 Systematic Sampling, 396 Stratified Sampling, 397 Proportional versus Disproportional Sampling, 397 Cluster Sampling, 398

Research Snapshot Who’s at Home? Different Ways to Select Respondents, 399

Multistage Area Sampling, 399

What Is the Appropriate Sample Design?, 401

Degree of Accuracy, 402 Resources, 402

Research Snapshot New on Campus: Student Adjustment to College Life, 403

Time, 403 Advance Knowledge of the Population, 403 National versus Local Project, 403

Internet Sampling Is Unique, 404 Website Visitors, 404 Panel Samples, 404 Recruited Ad Hoc Samples, 405 Opt-in Lists, 405

Summary, 406 Key Terms and Concepts, 406 Questions for Review and Critical Thinking, 407 Research Activities, 407

CASE 16.1 Who’s Fishing?, 408 CASE 16.2 Scientific Telephone Samples, 408

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory, 409 Introduction, 410

Descriptive and Inferential Statistics, 410 Sample Statistics and Population Parameters, 410

Making Data Usable, 411 Frequency Distributions, 411

Contents xiii

Survey This!, 412 Proportions, 412

Research Snapshot The Well-Chosen Average, 413 Measures of Central Tendency, 413 Measures of Dispersion, 415

The Normal Distribution, 418

Population Distribution, Sample Distribution, and Sampling Distribution, 422

Central-Limit Theorem, 424

Estimation of Parameters, 427 Point Estimates, 427 Confidence Intervals, 427

Research Snapshot Sampling the World, 428

Sample Size, 430 Random Error and Sample Size, 430

Research Snapshot Target and Walmart Shoppers Really Are Different, 431

Factors in Determining Sample Size for Questions Involving Means, 431

Estimating Sample Size for Questions Involving Means, 432 The Influence of Population Size on Sample Size, 433 Factors in Determining Sample Size for Proportions, 433 Calculating Sample Size for Sample Proportions, 435 Determining Sample Size on the Basis of Judgment, 436 Determining Sample Size for Stratified and Other

Probability Samples, 437 Determining Level of Precision after Data Collection, 437

A Reminder about Statistics, 437

Summary, 438 Key Terms and Concepts, 438 Questions for Review and Critical Thinking, 439 Research Activities, 440

CASE 17.1 Pointsec Mobile Technologies, 440

CHAPTER 18 Fieldwork, 441 Introduction, 442

The Nature of Fieldwork, 442

Who Conducts the Fieldwork?, 442

Survey This!, 443

In-House Training for Inexperienced Interviewers, 443

Making Initial Contact and Securing the Interview, 443

Research Snapshot Interviewing for Horizon Research Services, 444

Asking the Questions, 445

Research Snapshot Why Is “Why” Important?, 446 Probing When No Response Is Given, 446 Recording the Responses, 447

Research Snapshot Probing for Deeper Meaning at Olson Zaltman Associates, 448

Terminating the Interview, 449

Principles of Good Interviewing, 449 The Basics, 449 Required Practices, 450

Fieldwork Management, 451 Briefing Session for Experienced Interviewers, 451 Training to Avoid Procedural Errors in Sample

Selection, 451

Research Snapshot Total Quality Management for Interviewing, 452

Supervision of Fieldworkers, 452 Sampling Verification, 453 Interviewer Cheating, 453 Verification by Reinterviewing, 454

Summary, 454 Key Terms and Concepts, 455 Questions for Review and Critical Thinking, 455 Research Activity, 456

CASE 18.1 Thomas and Dorothy Leavey Library, 456 CASE 18.2 Margaret Murphy O’Hara, 456

PART SIX Data Analysis and Presentation

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information, 458 Introduction, 459

Stages of Data Analysis, 459

Survey This!, 460

Editing, 460 Field Editing, 461 In-House Editing, 461

Research Snapshot Do You Have Integrity?, 462 Editing for Completeness, 463 Editing Questions Answered Out of Order, 464 Facilitating the Coding Process, 464 Pitfalls of Editing, 465 Pretesting Edit, 465

Coding, 465 Coding Qualitative Responses, 465 The Data File, 468

Research Snapshot Building a Multipetabyte Data System, 469

Code Construction, 469 Precoding Fixed-Alternative Questions, 470 More on Coding Open-Ended Questions, 471 Devising the Coding Scheme, 472 Code Book, 475 Editing and Coding Combined, 475

Research Snapshot Coding Data “On-the-Go,” 476

xiv Contents

Computerized Survey Data Processing, 476 Error Checking, 476

Summary, 477 Key Terms and Concepts, 478 Questions for Review and Critical Thinking, 478 Research Activities, 479

CASE 19.1 U.S. Department of the Interior Heritage Conservation and Recreation Service, 479 CASE 19.2 Shampoo 9–10, 480

CHAPTER 20 Basic Data Analysis: Descriptive Statistics, 483 Introduction, 484

The Nature of Descriptive Analysis, 484

Survey This!, 485

Tabulation, 486

Cross-Tabulation, 486 Contingency Tables, 487 Percentage Cross-Tabulations, 488

Research Snapshot Our Four-Legged Family Members, 489 Elaboration and Refinement, 490 How Many Cross-Tabulations?, 490 Quadrant Analysis, 491

Data Transformation, 491 Simple Transformations, 491 Problems with Data Transformations, 493 Index Numbers, 494

Calculating Rank Order, 494

Research Snapshot Twitter and the ReTweetability Index, 495

Tabular and Graphic Methods of Displaying Data, 496

Computer Programs for Analysis, 497 Statistical Packages, 497 Computer Graphics and Computer Mapping, 498

Interpretation, 500

Summary, 500 Key Terms and Concepts, 501 Questions for Review and Critical Thinking, 501 Research Activities, 503

CASE 20.1 Body on Tap, 503 CASE 20.2 Downy-Q Quilt, 503

CHAPTER 21 Univariate Statistical Analysis, 506 Introduction, 507

Hypothesis Testing, 507 The Hypothesis-Testing Procedure, 507

Survey This!, 508

Research Snapshot The “Freshman 7.8,” 509 An Example of Hypothesis Testing, 511 Type I and Type II Errors, 513

Research Snapshot The Law and Type I and Type II Errors, 514

Choosing the Appropriate Statistical Technique, 515

Type of Question to Be Answered, 515 Number of Variables, 515 Level of Scale of Measurement, 515

Research Snapshot Living in a Statistical Web, 516 Parametric versus Nonparametric Hypothesis Tests, 516

The t-Distribution, 517 Calculating a Confidence Interval Estimate Using the

t-Distribution, 519 Univariate Hypothesis Test Using the t-Distribution, 520

The Chi-Square Test for Goodness of Fit, 521

Research Snapshot Interested in Retirement? It Often Depends on Your Age, 523

Hypothesis Test of a Proportion, 524

Additional Applications of Hypothesis Testing, 525

Summary, 525 Key Terms and Concepts, 526 Questions for Review and Critical Thinking, 526 Research Activities, 526

CASE 21.1 Premier Motorcars, 527

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables, 528 Introduction, 529

What Is the Appropriate Test of Difference?, 529

Survey This!, 531

Cross-Tabulation Tables: The x2 Test for Goodness-of-Fit, 531

Research Snapshot Accurate Information? How About a Chi-Square Test?, 534

The t-Test for Comparing Two Means, 534 Independent Samples t-Test, 534

Research Snapshot Expert “T-eeze,” 538 Paired-Samples t-Test, 539

The Z-Test for Comparing Two Proportions, 540

Analysis of Variance (ANOVA), 542 What Is ANOVA?, 542 Simple Illustration of ANOVA, 542

Research Snapshot More Than One-Way, 543

Contents xv

Partitioning Variance in ANOVA, 544 The F-Test, 546 Practically Speaking, 547

Summary, 547 Key Terms and Concepts, 548 Questions for Review and Critical Thinking, 548 Research Activities, 551

CASE 22.1 Old School versus New School Sports Fans, 551

APPENDIX 22A Manual Calculation of an F-Statistic, 553

APPENDIX 22B ANOVA for Complex Experimental Designs, 556 Factorial Designs, 558

ANOVA for a Factorial Experiment, 558 Partitioning the Sum of Squares for a Two-Way ANOVA, 558

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association, 560 Introduction, 561

The Basics, 561

Simple Correlation Coefficient, 561

Survey This!, 562 An Example, 563 Correlation, Covariance, and Causation, 564 Coefficient of Determination, 564 Correlation Matrix, 565

Regression Analysis, 566

Research Snapshot What Makes Attractiveness?, 567 The Regression Equation, 568 Parameter Estimate Choices, 568 Visual Estimation of a Simple Regression Model, 569 Ordinary Least-Squares (OLS) Method of Regression

Analysis, 571

Research Snapshot Size and Weight, 576

Summary, 577 Key Terms and Concepts, 577 Questions for Review and Critical Thinking, 578 Research Activities, 579

CASE 23.1 International Operations at CarCare Inc., 579

APPENDIX 23A Arithmetic Behind OLS, 580

CHAPTER 24 Multivariate Statistical Analysis, 582 Introduction, 583

What Is Multivariate Data Analysis?, 583 The “Variate” in Multivariate, 583

Survey This!, 584

Classifying Multivariate Techniques, 584 Dependence Techniques, 584 Interdependence Techniques, 584 Influence of Measurement Scales, 585

Analysis of Dependence, 586 Multiple Regression Analysis, 586

Research Snapshot Too Much of a Good Thing!, 589 ANOVA (n-Way) and MANOVA, 591 Discriminant Analysis, 592

Research Snapshot How to Get MANOVA Results, 593

Analysis of Interdependence, 595 Factor Analysis, 595

Research Snapshot Getting Factor Results with SAS or SPSS, 597

Cluster Analysis, 599 Multidimensional Scaling, 601

Summary, 602 Key Terms and Concepts, 603 Questions for Review and Critical Thinking, 603 Research Activities, 604

CASE 24.1 The Utah Jazz, 607 CASE 24.2 How Do We Keep Them?, 609

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up, 610 Introduction, 611

Insights from the Communications Model, 611

Survey This!, 612

The Report in Context, 613

Report Format, 613 Tailoring the Format to the Project, 614 The Parts of the Report, 615

Research Snapshot Research ROI, 617

Research Snapshot How Do We Stack Up? The Value of Sba.gov, 618

xvi Contents

Basic Business Research Report, 619

Effective Use of Graphic Aids, 620 Tables, 620 Charts, 622

The Oral Presentation, 628

Research Snapshot The 10/20/30 Rule of PowerPoint, 629

Reports on the Internet or Intranet, 629

The Research Follow-Up, 630

Summary, 630 Key Terms and Concepts, 631 Questions for Review and Critical Thinking, 631 Research Activities, 631

CASE 25.1 Annenberg Public Policy Center, 632

PART SEVEN Comprehensive Cases with Computerized Databases

CASE 1: Running the Numbers: Does It Pay?, 636 CASE 2: Attiring Situation, 637 CASE 3: Values and the Automobile Market, 638 CASE 4: TABH, INC., Automotive Consulting, 641 CASE 5: The Atlanta Braves, 642 CASE 6: Knowing the Way, 642

Glossary of Frequently Used Symbols, 645

Glossary, 646

Endnotes, 659

Index, 666

Contents xvii

xviii

PREFACE

T he business world has never been changing faster! Businesses can’t assume that con- tinuing to operate their functional areas by the same old processes, with the same old technology, managed in the same old way will continue to produce the same old good result. The need for intelligence supported by a connection with customers, employees,

and other constituencies is more important than ever. Business researchers are challenged with the job of producing just this type of intelligence. The ninth edition of Business Research Methods addresses the dynamic nature of today’s business world while conveying the essential elements of the business research process.

Most readers understand that managers want answers to business questions. What is not so obvious is the complexity involved in specifying the correct research questions and determining the appropriate process for collecting, analyzing, and presenting information. This is the role of a business researcher, who must develop the skills to clarify the research objectives and possess the ability to manage the search for information. With the technological capabilities we have today, that means searching through terabyte after terabyte of existing information to pick out those elements which best hold the possibility of turning into useful intelligence. This informa- tion must be complemented by original data gathered by the researcher. All the intelligence must then be communicated in a way that helps managers make decisions. When it works right, business research is a win-win proposition. The process enables a company to identify its custom- ers, understand its employees, and design processes, products, and services that maximize value for all. In return, the company receives value as the customer spends their hard-earned money, employee engagement increases, and more effective business practices are implemented. As a result, everyone wins!

Trying to find just the right piece of business information via the Internet can be like searching for a needle in a haystack. This information may well be hidden beneath piles and piles of irrelevant stuff ! Or, how about trying to find a key piece of business information that may be hidden in the mind of a consumer or employee? A customer may not even be consciously aware of all his or her reasons for some preference or behavior and, consequently, can’t identify or talk about it. An employee may not even realize that he or she possesses information vital to the organization. How do you go about uncovering this information that could be so crucial to making a good business decision?

That’s where this text comes in: Business Research Methods equips students with the knowledge and skills needed in their search for business intelligence. The process we describe includes six steps. Researchers must first work together with decision makers to decide what they are look- ing for—that metaphorical needle in the haystack. The next two stages plot out the way to go about finding the needle. Next are two stages that focus on the actual search for the needle. The process concludes when the business researcher communicates the benefits of finding “pointed”

information that can help mend problems or create something really new and special for the deci- sion maker. Success in this process usually merits the researcher a reward that is a bit more valuable than that needle!

Enhancements in Business Research Methods To ensure that students are able to conduct business research with an understanding of all the latest theories and techniques available to them, the ninth edition is revised and updated. In response to reader feedback and changes in the research environment, two key enhancements have been made: (1) the ninth edition provides greater examples, illustrations, and applications of the global nature of business and (2) focuses more heavily on the role of technology in research. Without a doubt these issues—globalization and technology—continue to revolutionize business research. The Internet has affected all phases of research by altering information systems, ways of gathering secondary data, survey processes, sampling, questionnaire design, primary data collection, qualitative analysis, and communication, among other things. In the ninth edition, practically every chapter includes sig- nificant coverage of technology-related topics, and most chapters also include review questions and activities that get students involved with the Internet or other applications of technology in a relevant way. The Survey This! feature gets students and instructors directly involved with one important way that the Internet has changed research. This particular feature provides first-hand experience with the process, and illustrates the advantages and disadvantages, of using online questionnaires.

Overall, here is a summary of some of the key improvements and features of the ninth edition of Business Research Methods.

■ Increased Coverage of International Business Issues—The text makes greater use of examples from companies and countries around the globe. Readers of this book from outside North America will appreciate more familiar examples that are relevant to their daily life. The inter- national examples will also open up North American students to research issues beyond the domestic market. For all readers the examples illustrate global dynamics. This is a particularly important enhancement to the text since cultural and language barriers often present chal- lenges for the business researcher.

■ Survey This! Feature—First introduced in the eighth edition, Survey This! allows students to respond to an online questionnaire hosted with Qualtrics software. The questionnaire involves students’ opinions, activities, and interests regarding matters related to studying busi- ness and careers in business fields. In the early chapters, this feature is useful for critiquing the way questionnaires are constructed and how research hypotheses are addressed in a question- naire. New to the ninth edition is a populated data set for instructors based on the Survey This! questionnaire. This data can be used in later chapters as illustration of the statistical techniques discussed in the text. Students also get access to Qualtrics to design their own questionnaires.

■ Attention to Qualitative Research—Companies are increasingly realizing the benefits of qual- itative research. In response to this important phenomenon, Chapter 7, Qualitative Research Tools, provides a comprehensive overview of qualitative research techniques. The chapter focuses on various approaches to qualitative research and the corresponding interpretative techniques that turn qualitative data into business intelligence. Phenomenology, grounded theory, ethnography, and case study approaches are all covered. Qualitative research is also integrated into several other chapters. For example, the Internet is not just a way of collecting quantitative data. Qualitative research is being dramatically changed by the Internet as con- sumers leave more and more artifactual data behind on social networking websites, company chat rooms, blogs, microblogs (such as tweets left on Twitter), and more. Thus, qualitative research tools and approaches are highlighted throughout the text.

■ Breadth of Business Coverage—The ninth edition further broadens the spectrum of business activities and touches on practically all areas of business including marketing, management, finance, business ethics, and accounting. The examples reflect the diversity of today’s business world. Many of these examples are captured in the Research Snapshot features, which tie busi- ness research together with current events, ethics, technology, and topics of particular interest to the business research student.

Preface xix

■ Chapter Vignettes—The chapter vignettes were carefully examined and updated. Several are new to the ninth edition. Each vignette sets the stage for the chapter by introducing topics taken from both well-known and lesser-known companies, topical areas of interest in the current business literature, and slice-of-life business situations. The vignettes help frame the material included in each chapter and put core course concepts into a real-life and current context.

■ Comprehensive Cases—The ninth edition includes online access to comprehensive cases that allow the student to get real hands-on experience doing research. Selected cases also include data that can be downloaded from the Instructor’s Resource CD or the companion website for the text. The data are ready to be analyzed using Excel, SPSS, or SAS, or any other soft- ware capable of reading data from a spreadsheet. Shorter and more-to-the-point cases are included at the end of each chapter. Several of these cases involve simple data analyses and are accompanied by data also available on the instructor resource disk or the book website.

■ A Simplified Approach and Style—The Research Snapshots, chapter Learning Outcomes, and end-of-chapter materials are presented in a form that allows greater focus on the truly impor- tant information. The Learning Outcomes ensure an important coherence and structure to the chapters that culminate with the end-of-chapter materials.

■ Tagged End-of-Chapter Exercises—The end-of-chapter materials contain a number of ques- tions that pertain to either ethical issues in business research or exercises requiring students to get involved with research via the Internet. These items are each uniquely tagged with a visual symbol, making these particular exercises stand out with a clear, identifying mark.

Organization of the Book The organization of the ninth edition of Business Research Methods follows the logic of the business research process itself. The book is organized into seven parts, with each part presenting the basic research concepts for one of the stages in the research process. Each part also discusses how these concepts relate to decisions about conducting specific business research projects.

Part One: Introduction emphasizes the interplay between research and business and how the importance and scope of research varies with different business situations. Included in this discus- sion is an overview of computerized data management and information systems, an outline of the entire business research process, and an explanation of how all of this is changing due to technol- ogy. Chapter 3 is devoted to theory development and explains why theory is important to effective research, and how research is a way that theory is tested.

Without high ethical standards, no business is a “good business.” Thus, the introductory mate- rials also include an emphasis on business ethics and the special ethical problems associated with business research. Chapter 5 focuses exclusively on business ethics and the interplay between orga- nizational dynamics and research.

Part Two: Beginning Stages of the Research Process covers the essentials involved in starting to study business problems. This part emphasizes decision making, problem definition, and the process of how the business problem must be translated into research questions and/or research hypotheses. Research proposals are covered in some detail, and the reader is encouraged to see these as the written agreement that helps put the decision maker and the researcher on the same page.

Chapter 7 emphasizes qualitative research applications. One role played by qualitative research is helping to separate problem symptoms from true issues that can be attacked with business research. However, qualitative research extends far beyond problem definition; it allows greater potential for discovery as well as deeper and potentially more meaningful explanations in business research. Part Two concludes with Chapter 8, which provides detailed discussion of secondary data and emphasizes its increasing importance in a data-rich world.

Part Three: Research Methods for Collecting Primary Data examines the topics involved in collecting new data for the specific problem at hand. For example, the chapters describe issues related to planning, conducting, and administering surveys, which remain a mainstay for collecting consumer and employee opinions, attitudes, and behaviors.

xx Preface

Additionally, Chapter 12 focuses specifically on experimental research. Experiments allow the researcher to establish causal inferences. They can, however, be intricate to design and implement, and conducting experiments that provide valid results is no simple matter. When done correctly, experiments can provide extremely important business intelligence.

Part Four: Measurement Concepts describes key concepts of measurement that are funda- mental to conducting valid business research. This part of the text discusses the foundation of mea- surement theory. Key topics include descriptions of the different levels of scale measurement and how this affects analysis and the interpretation of results. Basic ways to measure human attitudes and practical matters dealing with questionnaire design are also discussed. An increased emphasis is placed on the use of new technologies for conducting interviews. For instance, how does asking a question in an electronic format expand the options for respondents and the researcher? Topics such as these are highlighted in Part Four.

Part Five: Sampling and Fieldwork explains the difference between a population and a sample. The reasons why sampling is needed and how it can be used to confidently allow predic- tions about larger numbers of people are covered. The fieldwork process is also discussed, including the importance of supervision of fieldwork. This section shows how to avoid sloppy sampling and poor field supervision, which can lead to error in the business research process.

Part Six: Data Analysis and Presentation covers important processes necessary in translat- ing raw data into business intelligence. Included among these topics are the editing and coding of the data. The coded data are then ready for analysis, and this section presents the most commonly used methods for analyzing data.

Basic descriptive statistics are discussed as ways of portraying key results including central ten- dency and dispersion. Inferential statistics are discussed, including often-used univariate and bivari- ate approaches such as t-tests. Multivariate statistical approaches are also introduced so that the reader has an awareness of techniques that can analyze many variables simultaneously. Hands-on experience with basic multivariate procedures is also provided. The final chapter brings everything together by including a discussion of how to effectively communicate research results via a report and/or presentation.

Part Seven: Comprehensive Cases with Computerized Databases make up the last section of the book. These cases provide materials that challenge students to apply and integrate the concepts they have learned throughout the course. Instructors will find that these cases pro- vide flexibility to either expand or simplify the assignment to suit the demands of varying course assignments.

The cases provide greater variety than earlier editions, including some that involve analysis of internal organizational problems as well as an opportunity to use qualitative research. When quan- titative data are included, they can be easily analyzed with basic statistical tools like SPSS. Excel files are also included with the same data. These files can be read directly by statistical programs like SAS or other programs. A new comprehensive case has been added to the ninth edition.

Superior Pedagogy More than other research textbooks, the ninth edition of Business Research Methods addresses stu- dents’ need to comprehend all aspects of the business research process. The following features facilitate learning throughout the book:

■ Learning Outcomes. Each chapter begins with a concise list of learning outcomes that emphasize the major areas of competency that students should achieve before proceeding to the next chapter. The key is to avoid labeling everything a major learning outcome and to provide instructors with flexibility for emphasizing additional material as they see fit.

■ Headings Keyed to Learning Outcomes. First-level headings, with the exception of those labeled “Introduction,” are keyed to learning outcomes. This should be an aid in developing assessment rubrics and makes the book more user friendly in terms of identifying key material.

■ Research Snapshots. All of the box materials share a common title, Research Snapshots. Each chapter contains multiple Research Snapshots. The boxes explore business research

Preface xxi

processes in a variety of modern businesses situations, ranging from international consider- ations to research ethics. Some boxes also illustrate research techniques and applications in a step-by-step fashion. Every attempt is made to make the box material lively and relevant to the subject matter of the chapters.

■ Writing Style. An accessible, interesting writing style continues to be a hallmark of this book. With a careful balance between theory and practice and a sprinkling of interesting examples and anecdotes, the writing style clarifies and simplifies the business research process. In addi- tion, the text offers a comprehensive treatment of important and current topics.

■ Statistical Approach. A review of statistical theory provides students with an overview of the basic aspects of statistics. However, since this text stresses managerial applications more than statistical theory, students are given tools to perform the most common business research data analysis. More sophisticated data analysis approaches are left for further reference. Thus, the readers can learn how to test simple hypotheses involving differences between means or relationships among variables. Cross-tabulation, t-tests, ANOVA, and regression are covered in sufficient depth to allow students to use these techniques. In addition, easy-to-follow, click- through sequences can walk students through most basic approaches to producing statistical results.

■ Key Terms. Learning the vocabulary of business research is essential to understanding the topic, and Business Research Methods facilitates this with key terms. First, key concepts are boldfaced and completely defined when they first appear in the textbook. Second, all key terms and concepts are listed at the end of each chapter, and many terms are highlighted in a marginal glossary. Third, a glossary summarizing all key terms and definitions appears at the end of the book for handy reference. A glossary of frequently used symbols is also included.

■ Ethics Questions. Identified by a special icon, ETHICS, ethics questions are included in most chapters. Among the compelling issues students are asked to explore is redefining the right to privacy in light of new technology. The ethical issues also provide a great opportunity for building critical thinking skills.

■ Internet Questions. Internet questions also are identified by a special icon, ’NET. Nearly all chapters include multiple questions and research activities that illustrate advances in Internet applications common to business research.

■ Research Activities. The end-of-chapter materials include real-world research activities intended to provide actual research experience for the student. Most provide an opportunity for the student to gain experience in multiple content areas. Some involve ethical aspects of research, and some involve Internet usage.

■ Cases. Cases, often taken from real-life situations, illustrate business research concepts and build knowledge and research skills. These cases offer students the opportunity to participate actively in the decision-making process, one of the most effective forms of learning.

Comprehensive Instructor Resources We believe it is important for any text to develop comprehensive supplemental materials to sup- port instructors in their vital teaching function. Because of this pedagogical philosophy, there is an extensive learning package provided with Business Research Methods.

■ The Instructor’s Resource CD-ROM (ISBN: 9781133190967) contains valuable instruc- tor resources on one easy-to-use CD-ROM: the test bank, ExamView testing software, the instructor’s manual (includes comprehensive lecture outlines, solutions to all assignments, and case solutions), PowerPoint presentation slides, video instructor’s manual, and data sets for cases. The IRCD files were prepared by Laurie Babin of The University of Louisiana–Monroe.

■ Data sets are provided that match several end-of-chapter exercises and different case analyses. The data are provided both in Excel files, allowing great flexibility for use of the data with many programs, and in SPSS files, which are ready to use with either the student or full ver- sion of SPSS. Students may download the data sets at http://login.cengage.com by clicking on the textbook and then on “Data Sets.” Data sets are also available on the Instructor’s Resource CD-ROM.

xxii Preface

■ Web resources at http://login.cengage.com provide the latest information about what’s new and what’s cool in business research. The instructor’s manual, test bank, PowerPoint slides, and video cases can also be found here. The site also features links to other research- related sites.

■ The Qualtrics Research Suite was built for researchers by researchers. Enclosed with each new copy of Business Research Methods is an access code that provides access to an extremely elegant tool that makes survey creation easy enough for an intern while at the same time sophisticated enough for the most demanding academic or corporate researcher. Qualtrics allows students to create and deploy surveys, and provides data for analysis. A survey included in the book can be taken by students and they can access the data collected from adopters of Business Research Methods from around the globe. Qualtrics is also a perfect solution for instructors who have students conduct a survey project as part of a term project.

■ Preloaded with content and available free via PIN code when packaged with new texts, WebTutor ToolBox pairs all the content of the text’s rich companion website with all the sophisticated course management functionality of a Blackboard or WebCT product. Instruc- tors can assign materials (including online quizzes) and have the results flow automatically to their gradebooks.

Resources for Students To promote learning and competency, it is also important to provide students with well-crafted resources. In addition to covering the latest information technology (described above), the ninth edition includes the following student resources:

■ The student companion site is located at www.cengagebrain.com. At the CengageBrain.com home page, search for the ISBN of your textbook (from the back cover of your book) using the search box at the top of the page. This will take you to the product page where you will find chapter quizzes, flash cards of key terms, the very best online business research resources available, and much more.

■ The Qualtrics Research Suite is also available for students. Qualtrics allows students to create and deploy surveys, and provides data for analysis.

Acknowledgments Certainly, no list of acknowledgments will be complete. So many people have assisted in this project. Chief among these would be the late Bill Zikmund, who carried the weight of this proj- ect for the first seven editions of Business Research Methods. We are privileged to be able to carry the project along into what we hope will be many more editions as the premier business research text. Also, thanks go to members of our team—including graduate assistants Kevin James, David Shows, Melanie Gardner, and Christina Chung—who have helped with research for this text and helped share some of the workload on other endeavors, freeing up time to spend on this project. We would be remiss not to mention the support and patience of our families. All have contributed to the project and our kids are particularly helpful in judging relevance of vignettes and examples, as well as contributing photographs. Also, thanks go to the great faculty who mentored us during our days in the PhD program. Most notable among these are Joseph F. Hair, Jr. and the late Wil- liam R. Darden.

Thanks also go to all the good people at Cengage Learning who helped make this project pos- sible. A special thanks to our publisher Mike Roche, and to Emily Nesheim and Elizabeth Lowry. Also thanks to Charles Emmanuel and Karthik Kannan at diacriTech. They provided tremendous support and guidance through the writing and production process, including assistance with proof- ing, permissions, photos, and exhibits.

Preface xxiii

Many colleagues contributed ideas for this book. While they made suggestions that greatly enhanced this book, any shortcomings are ours alone. For their insightful reviews of the manu- script for the ninth or previous editions of Business Research Methods, we would like to thank the following:

Joe Adamo Cazenovia College

David W. Ahrend St. Joseph’s College

Gerald Albaum University of Oregon

Jill Attaway Illinois State University

Stephen Batory Bloomsburg University

William Bearden University of South Carolina

Joseph A. Bellizzi Arizona State University–West

Carol Bienstock Radford University

James A. Brunner University of Toledo

F. Anthony Bushman San Francisco State University

Thomas Buzas Eastern Michigan University

Roy F. Cabaniss Huston-Tillotson College

Steven V. Cates Averett University

Kathy Chudoba Utah State

Dr. Edward D. Conrad Henderson State University

Michael d’Amico University of Akron

Ron Eggers Barton College

H. Harry Friedman City University of New York–Brooklyn

Ron Goldsmith Florida State University

Larry Goldstein Iona College

Karen Goncalves Nichols College

David Gourley Arizona State University

Jim Grimm Illinois State University

Christopher Groening University of Pittsburgh

Al Gross Robert Morris College

Don Heinz University of Wisconsin

Nathan Heller Tarleton State University

Craig Hollingshead Texas A&M University–Kingsville

Victor Howe University of Kentucky

Roy Howell Texas Tech University

Michael R. Hyman New Mexico State University

Rhea Ingram Columbus State University–Georgia

Robert Jaross Florida International University

P. K. Kannan University of Maryland

Susan Kleine Arizona State University

David B. Klenosky Purdue University

C. S. Kohli California State University–Fullerton

Jerome L. Langer Assumption College

Bob Lauman Webster University

James H. Leigh Texas A&M University

Larry Lowe Bryant College

Karl Mann Tennessee Technological University

Charles R. Martin Wichita State University

Marlys Mason Oklahoma State University

xxiv Preface

Tom K. Massey University of Missouri–Kansas City

Sanjay Mishra University of Kansas

Mark Moore East Carolina University

G. M. Naidu University of Wisconsin–Whitewater

Stephanie Noble The University of Mississippi

Mike Parent Utah State University

Terry Paul The Ohio State University

Charles Prohaska Central Connecticut State University

Rick Saucier St. John’s University

Alan Sawyer University of Florida

Robert Schaffer California State University–Pomona

Leon G. Schiffman City University of New York–Baruch

David Shows Louisiana Tech University

K. Sivakumar Lehigh University

Mark Speece Central Washington University

Harlan Spotts Western New England College

Wilbur W. Stanton Old Dominion University

Bruce L. Stern Portland State University

James L. Taylor University of Alabama

Gail Tom California State University–Sacramento

Deborah Utter Boston College

Haibo Wang Texas A&M International University

David Wheeler Suffolk University

Richard Wilcox Carthage College

Natalie Wood St. Joseph’s University

Margaret Wright University of Colorado

Clifford E. Young University of Colorado–Denver

Jim Zemanek East Carolina University

Xin Zhao University of Utah

William Lee Ziegler Bethune-Cookman College

Thanks to all of the students who have inspired us and reinforced the fact that we made a great career decision about two decades ago. Thanks also to our close colleagues Rob Baer, Jim Boles, Ed Bond, Tara’ Lopez, and Dave Ortinau for their continued support and insight.

Barry J. Babin Louisiana Tech University

Mitch Griffin Bradley University

Jon C. Carr Texas Christian University December 2011

Preface xxv

P a r t O N E

Introduction

CHAPTER 1 The Role of Business Research

CHAPTER 2 Information Systems and Knowledge Management

CHAPTER 3 Theory Building

CHAPTER 4 The Business Research Process: An Overview

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues

The Role of Business Research

After studying this chapter, you should be able to

1. Understand how research contributes to business success

2. Know how to define business research

3. Understand the difference between basic and applied business research

4. Understand how research activities can be used to address business decisions

5. Know when business research should and should not be conducted

6. Appreciate the way that technology and internationalization are changing business research

C H A P T E R

T o many people, the abbreviation ESPN says it all when you are thinking about sports program- ming. The Entertainment and Sports Program- ming Network was launched in 1979, with its

famous SportsCenter broadcast followed by a presentation of a slow pitch softball game. Over time, ESPN has become a media juggernaut, expanding its sports content and programming globally, and is a media presence in every possible outlet, includ- ing television, video, and the Internet. In fact, ESPN.com has long been one of the most visited sites on the World Wide Web.

It is the passionate sports fan that makes ESPN’s success pos- sible. Over the years, information about the people who watch and interact with ESPN content had been slowly accumulating across their different media outlets. This information included not just web clicks and television viewership, but also purchases from ESPN and its affiliated advertising partners. Since this information was located in separate databases and across dif- ferent operating units, it had become difficult to know just who the fan was, and what they were truly interested in. What could be done with this considerable business information? Could ESPN become more knowledgeable (and more profitable) by learning more about the sports fans who use their content?

The answer was a resounding “Yes!” ESPN partnered with Quaero, a business research company that specializes in customer intelligence, to integrate their numerous databases and begin to learn more about how fans use their media, and what specifically they were looking for. They learned that enhancing the fan’s experience, regardless of the media, had bottom-line implications for their own revenue, and the revenue of their advertisers. ESPN realized that based upon their cus- tomer research, cross-network promotions and individualized

LEARNING OUTCOMES

Chapter Vignette:

ESPN Hits a Home Run by Leveraging the Power of its Business Research

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advertising content could be built for their sports fans, and that seeing the sports fan as a core asset of the company was criti- cal to success. In fact, the activities associated with the business research they conducted on their customers were not viewed as a cost, but in fact served as a revenue driver to their firm’s profitability.

It was business research that made this possible. Gaining intelligence on a critical business function, a function that had global implications, helped create a profitable solution for ESPN. The value of this research for ESPN and its millions of sports fans created a “win-win” for all. ESPN had, in fact, hit a “home run” through the use of business research.1

Introduction The recent history of ESPN demonstrates the need for information in making informed decisions addressing key issues faced by all competitive businesses. Research can provide that information. Without it, business decisions involving both tactics and strategies are made in the dark.

We open with two examples illustrating how business decisions require intelligence and how research can provide that intelligence. The following examples focus specifically on how research can lead to innovation in the form of new products, improvements in existing goods and services, or enhancements in employee relationships. Imagine yourself in the role of business manager as you read these examples and think about the information needs you may have in trying to build success for your company.

The coffee industry, after years of the “daily grind,” has proved quite dynamic over the past decade. After years of steady decline, research on consumers’ beverage purchases show that coffee sales began rebounding around 1995. Telephone interviews with American consumers estimated that there were 80 million occasional coffee drinkers and 7 million daily upscale coffee drinkers in 1995. By 2001, estimates suggested there were 161 million daily or occasional U.S. coffee drinkers and 27 million daily upscale coffee drinkers.2

Coffee drinking habits have also changed. In 1991 there were fewer than 450 coffeehouses in the United States. Today, it seems like places such as Starbucks, Second Cup, The Coffee Bean & Tea Leaf, and Gloria Jean’s are virtually everywhere in the United States and Canada. There are more than 17,000 Starbucks locations around the world with the majority of these being wholly owned stores.3 While locating these outlets requires significant formal research, Starbucks also is researching new concepts aimed at other ways a coffee shop can provide value to consumers. One concept that has survived testing thus far is the addition of free, in-store high-speed wireless Internet access. Thus, you can have hot coffee in a hotspot! After Starbucks baristas began reporting that customers were asking clerks what music was playing in the stores, Starbucks began testing the sales of CDs contain- ing their in-store music. In 2009, Starbucks began a bundled pricing promotion offering a breakfast sandwich or pastry and a tall coffee drink for $3.95 in response to the declining economy. The research that underlies the introduction of these value-added concepts could first include simply ask- ing a consumer or a small group of consumers for their reaction to the concept. Survey research and then actual in-store tests may follow. So, the research underlying such decisions can be multilayered.

Often, business research is directed toward an element of an organization’s internal operations. For example, DuPont utilizes research techniques to better understand their employees’ needs. DuPont has 94,000 employees worldwide and 54,000 in the United States.4 The company has conducted four comprehensive work/life needs assessment surveys of its employees since 1985. This business research provides the company with considerable insight into employee work/life behavior and allows DuPont to identify trends regarding employee needs.

The most recent survey found that, as the company’s work force is aging, employees’ child care needs are diminishing, but elder care needs are emerging. The survey found that 88 percent of respondents identified themselves as baby boomers. About 50 percent of the employees say that they have—or expect to have—elder care responsibilities in the next three to four years, up from 40 percent in 1995.

The surveys have shown that DuPont employees want to balance work and family responsibili- ties, feeling deeply committed to both aspects of their lives. The latest research shows that company efforts to satisfy these desires have been successful. Employee perception of support from manage- ment for work/life issues improved from the 1995 study and the results indicate employees feel

CHAPTER 1 The Role of Business Research • 3

less stress. Support from colleagues is rated high, and women indicated they now have more role models. The study also reported that the feeling of management support is directly connected to employees’ efforts to make the company successful. Employees who use the work/life programs are willing to “go the extra mile.”

These examples illustrate the need for information in making informed business decisions. The statistics about coffee demonstrate how research can track trends that may lead to new busi- ness opportunities. Starbucks’s research also illustrates how research can be used to examine new concepts in progressively more complex stages, setting the stage for a more successful product introduction. DuPont’s ability to track employee attitudes allows them to adjust employee benefit packages to maximize satisfaction and reduce employee turnover. These are only the tip of the iceberg when it comes to the types of business research that are conducted every day. This chapter introduces basic concepts of business research and describes how research can play a crucial role in creating and managing a successful business.

The Nature of Business Research Business research covers a wide range of phenomena. For managers, the purpose of research is to provide knowledge regarding the organization, the market, the economy, or another area of uncertainty. A financial manager may ask, “Will the environment for long-term financing be bet- ter two years from now?” A personnel manager may ask, “What kind of training is necessary for production employees?” or “What is the reason for the company’s high employee turnover?” A marketing manager may ask, “How can I monitor my retail sales and retail trade activities?” Each of these questions requires information about how the environment, employees, customers, or the economy will respond to executives’ decisions. Research is one of the principal tools for answering these practical questions.

Within an organization, a business researcher may be referred to as a marketing researcher, an organizational researcher, a director of financial and economic research, or one of many other titles. Although business researchers are often specialized, the term business research encompasses all of these functional specialties. While researchers in different functional areas may investigate different phenomena, they are similar to one another because they share similar research methods.

It’s been said that “every business issue ultimately boils down to an information problem.”5 Can the right information be delivered? The ultimate goal of research is to supply accurate information that reduces the uncertainty in managerial decision making. Very often, decisions are made with

SURVEY

THIS! As a user of this book, you can take part in a real business research survey. In each chapter, we’ll refer back to some aspect of this survey to illustrate key points about busi- ness research. For instance, we can easily illus trate different types of survey approaches by referring back to some question contained in the survey. In later chapters, your instructor will provide you with a way to access not only the data from your particular class, but also data from all users. This data can be used to illustrate some of the analytical approaches discussed in the closing chap ters of the book. For now, your instructor will provide you with instructions to access the questionnaire via the Internet. As a first step in this process, simply respond to the items in the questionnaire just as you would to any other research survey.

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little information for various reasons, including cost considerations, insufficient time to conduct research, or management’s belief that enough is already known. Relying on seat-of-the-pants decision making—decision making without research—is like betting on a long shot at the race- track because the horse’s name is appealing. Occasionally there are successes, but in the long run, intuition without research leads to losses. Business research helps decision makers shift from intui- tive information gathering to systematic and objective investigation.

Business Research Defined Business research is the application of the scientific method in searching for the truth about busi- ness phenomena. These activities include defining business opportunities and problems, generating and evaluating alternative courses of action, and monitoring employee and organizational perfor- mance. Business research is more than conducting surveys.6 This process includes idea and theory development, problem definition, searching for and collecting information, analyzing data, and communicating the findings and their implications.

This definition suggests that business research information is not intuitive or haphazardly gath- ered. Literally, research (re-search) means “to search again.” The term connotes patient study and sci- entific investigation wherein the researcher takes another, more careful look at the data to discover all that is known about the subject. Ultimately, all findings are tied back to the underlying theory.

The definition also emphasizes, through reference to the scientific method, that any infor- mation generated should be accurate and objective. The nineteenth-century American humorist Artemus Ward claimed, “It ain’t the things we don’t know that gets us in trouble. It’s the things we know that ain’t so.” In other words, research isn’t performed to support preconceived ideas but to test them. The researcher must be personally detached and free of bias in attempting to find truth. If bias enters into the research process, the value of the research is considerably reduced. We will discuss this further Chapter 12.

Our definition makes it clear that business research is designed to facilitate the managerial decision-making process for all aspects of the business: finance, marketing, human resources, and so on. Business research is an essential tool for management in virtually all problem-solving and decision-making activities. By providing the necessary information on which to base business decisions, research can decrease the risk of making a wrong decision in each area. However, it is important to note that research is an aid to managerial decision making, never a substitute for it.

Finally, this definition of business research is limited by one’s definition of business. Certainly, research regarding production, finance, marketing, and management in for-profit corporations like DuPont is business research. However, business research also includes efforts that assist non- profit organizations such as the American Heart Association, the San Diego Zoo, the Boston Pops Orchestra, or a parochial school. Further, governmental agencies such as the Federal Emergency Management Agency (FEMA) and the Department of Homeland Security (DHS) perform many functions that are similar, if not identical, to those of for-profit business organizations. For instance, the Food and Drug Administration (FDA) is an important user of research, employing it to address the way people view and use various food and drugs. One such study commissioned and funded research to address the question of how consumers used the risk summaries that are included with all drugs sold in the United States.7 Therefore, not-for-profits and governmental agencies can use research in much the same way as managers at Starbucks or DuPont. While the focus is on for- profit organizations, this book explores business research as it applies to all institutions.

Applied and Basic Business Research One useful way to describe research is based on the specificity of its purpose. Applied business research is conducted to address a specific business decision for a specific firm or organization. The opening vignette describes a situation in which ESPN used applied research to decide how to best create knowledge of its sports fans and their preferences.

Basic business research (sometimes referred to as pure research) is conducted without a specific decision in mind, and it usually does not address the needs of a specific organization. It attempts to expand the limits of knowledge in general, and as such it is not aimed at solving a particular pragmatic

business research

The application of the scientific method in searching for the truth about business phenomena. These activities include defining business opportunities and problems, generating and evaluating ideas, monitoring performance, and understanding the business process.

applied business research

Research conducted to address a specific business decision for a specific firm or organization.

basic business research

Research conducted without a specific decision in mind that usually does not address the needs of a specific organization. It attempts to expand the limits of knowledge in general and is not aimed at solving a particular pragmatic problem.

CHAPTER 1 The Role of Business Research • 5

problem. Basic research can be used to test the validity of a general business theory (one that applies to all businesses) or to learn more about a particular business phenomenon. For instance, a great deal of basic research addresses employee motivation. How can managers best encourage workers to dedicate themselves toward the organization’s goals? From such research, we can learn the factors that are most important to workers and how to create an environment where employees are most highly motivated. This basic research does not examine the problem from any single organization’s perspec- tive. However, Starbucks’ or DuPont’s management may become aware of such research and use it to design applied research studies examining questions about their own employees. Thus, the two types of research are not completely independent, as basic research often provides the foundation for later applied research.

While the distinction between basic and applied is useful in describing research, there are very few aspects of research that apply only to basic or only to applied research. We will use the term business research more generally to refer to either type of research. The focus of this text is more on applied research—studies that are undertaken to answer questions about specific problems or to make decisions about particular courses of action or policies. Applied research is emphasized in this text because most students will be oriented toward the day-to-day practice of management, and most students and researchers will be exposed to short-term, problem-solving research conducted for businesses or nonprofit organizations.

The Scientific Method All research, whether basic or applied, involves the scientific method. The scientific method is the way researchers go about using knowledge and evidence to reach objective conclusions about the real world. The scientific method is the same in social sciences, such as business, as in physical sciences, such as physics. In this case, it is the way we come to understand business phenomena.

Exhibit 1.1 briefly illustrates the scientific method. In the scientific method, there are mul- tiple routes to developing ideas. When the ideas can be stated in researchable terms, we reach the hypothesis stage. The next step involves testing the hypothesis against empirical evidence (facts from observation or experimentation). The results either support a hypothesis or do not support a hypothesis. From these results, new knowledge is generated.

EXHIBIT 1.1

A Summary of the Scientific Method

the scientific method

The way researchers go about using knowledge and evidence to reach objective conclusions

about the real world.

Conclusion (New Knowledge)

Prior Knowledge

Observation

Hypotheses

Hypothesis Test (Observation or

Experimentation)

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6 • PART ONE Introduction

In basic research, testing these prior conceptions or hypotheses and then making inferences and conclusions about the phenomena leads to the establishment of general laws about the phe- nomena. Use of the scientific method in applied research ensures objectivity in gathering facts and testing creative ideas for alternative business strategies. The essence of research, whether basic or applied, lies in the scientific method. Much of this book deals with scientific methodology. Thus, the techniques of basic and applied research differ largely in degree rather than in substance.

Managerial Value of Business Research In all of business strategy, there are only a few business orientations (see Exhibit 1.2). A firm can be product-oriented. A product-oriented firm prioritizes decision making in a way that emphasizes technical superiority in the product. Thus, research that gathers information from technicians and experts in the field is very important in making critical decisions. A firm can be production-oriented. A production-oriented firm prioritizes the efficiency and effectiveness of production processes in making decisions. Here, research providing input from workers, engineers, finance, and accounting becomes important as the firm seeks to drive costs down. Production-oriented firms are usually very large firms manufacturing products in very large quantities. The third orientation is marketing- oriented, which focuses more on how the firm provides value to customers than on the physical prod- uct or production process. With a marketing-oriented organization the majority of research focuses on the customer. Research addressing consumer desires, beliefs, and attitudes becomes essential.

We have argued that research facilitates effective management. For example, Yoplait Go-Gurt illustrates the benefit of business research. The company’s consumer research about eating regular yogurt at school showed that moms and kids in their “tweens” wanted convenience and portabil- ity. Some brands, like Colombo Spoon in a Snap, offered the convenience of having a utensil as part of the packaging/delivery system. However, from what Yoplait learned about consumers, they thought kids would eat more yogurt if they could “lose the spoon” and eat yogurt anywhere, any- time. Moms and kids participating in a taste test were invited to sample different types of on-the-go packaging shapes—long tubes, thin tubes, fat tubes, and other shapes—without being told how to handle the packaging. One of the company’s researchers said, “It was funny to see the moms fidget around, then daintily pour the product onto a spoon, then into their mouths. The kids instantly

product-oriented

Describes a firm that prioritizes decision making in a way that emphasizes technical superiority in the product.

production-oriented

Describes a firm that prioritizes efficiency and effectiveness of the production processes in making decisions.

marketing-oriented

Describes a firm in which all decisions are made with a conscious awareness of their effect on the customer.

EXHIBIT 1.2

Business OrientationsProduct-Oriented Firm Example

Prioritizes decision making that emphasizes physical product design, trendiness, or technical superiority

The fashion industry makes clothes in styles and sizes that few can adopt.

Research focuses on technicians and experts in the field.

Production-Oriented Firm Example

Prioritizes efficiency and effectiveness of production processes in making decisions

The U.S. auto industry’s assembly-line process is intent on reducing costs of production as low as possible.

Research focuses on line employees, engineers, accountants, and other efficiency experts.

Marketing-Oriented Firm Example

Focuses on how the firm provides value to customers

Well-known hotel chains are designed to address the needs of travelers, particularly business travelers.

Research focuses on customers.

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CHAPTER 1 The Role of Business Research • 7

jumped on it. They knew what to do.”8 Squeezing Go-Gurt from the tube was a big plus. The kids loved the fact that the packaging gave them permission to play with their food, something parents always tell them not to do. Based on their research, Yoplait introduced Go-Gurt in a three-sided tube designed to fit in kids’ lunchboxes. The results were spectacular, with more than $100 million in sales its first year on the market. Yoplait realized that knowledge of consumers’ needs, coupled with product research and development, leads to successful business strategies.

As the Yoplait example shows, the prime managerial value of business research is that it pro- vides information that improves the decision-making process. The decision-making process asso- ciated with the development and implementation of a business strategy involves four interrelated stages:

1. Identifying problems or opportunities 2. Diagnosing and assessing problems or opportunities 3. Selecting and implementing a course of action 4. Evaluating the course of action

Business research, by supplying managers with pertinent information, may play an important role by reducing managerial uncertainty in each of these stages.

Identifying Problems or Opportunities Before any strategy can be developed, an organization must determine where it wants to go and how it will get there. Business research can help managers plan strategies by determining the nature of situations or by identifying the existence of problems or opportunities present in the organiza- tion. Business research may be used as a scanning activity to provide information about what is occurring within an organization or in its environment. The mere description of some social or economic activity may familiarize managers with organizational and environmental occurrences and help them understand a situation. Consider these two examples:

■ The description of the dividend history of stocks in an industry may point to an attrac- tive investment opportunity. Information supplied by business research may also indicate problems.

■ Employee interviews undertaken to characterize the dimensions of an airline reservation clerk’s job may reveal that reservation clerks emphasize competence in issuing tickets over courtesy and friendliness in customer contact.

Once business research indicates a problem or opportunity, managers may feel that the alterna- tives are clear enough to make a decision based on their experience or intuition. However, often they decide that more business research is needed to generate additional information for a better understanding of the situation.

Diagnosing and Assessing Problems or Opportunities After an organization recognizes a problem or identifies a potential opportunity, business research can help clarify the situation. Managers need to gain insight about the underlying factors causing the situation. If there is a problem, they need to specify what happened and why. If an opportunity exists, they may need to explore, refine, and quantify the opportunity. If multiple opportunities exist, research may be conducted to set priorities.

Selecting and Implementing a Course of Action After the alternative courses of action have been clearly identified, business research is often con- ducted to obtain specific information that will aid in evaluating the alternatives and in selecting the best course of action. For example, suppose Harley-Davidson is considering establishing a dealer network in either China or India. In this case, business research can be designed to gather

8 • PART ONE Introduction

the relevant information necessary to determine which, if either, course of action is best for the organization.

Opportunities may be evaluated through the use of various performance criteria. For example, estimates of market potential allow managers to evaluate the revenue that will be generated by each of the possible opportunities. A good forecast supplied by business researchers is among the most useful pieces of planning information a manager can have. Of course, complete accuracy in fore- casting the future is not possible, because change is constantly occurring in the business environ- ment. Nevertheless, objective information generated by business research to forecast environmental occurrences may be the foundation for selecting a particular course of action.

Even the best plan is likely to fail if it is not properly implemented. Business research may be conducted to indicate the specific tactics required to implement a course of action.

Evaluating the Course of Action After a course of action has been implemented, business research may serve as a tool to tell manag- ers whether or not planned activities were properly executed and if they accomplished what they were expected to accomplish. In other words, managers may use evaluation research to provide feedback for evaluation and control of strategies and tactics.

Evaluation research is the formal, objective measurement and appraisal of the extent to which a given activity, project, or program has achieved its objectives or whether continuing programs are presently performing as projected. Evaluation research may also provide information about the major factors influencing the observed performance levels.

In addition to business organizations, nonprofit organizations and governmental agencies frequently conduct evaluation research. Every year thousands of federal evaluation studies are undertaken to systematically assess the effects of public programs. For example, the U.S. General Accounting Office has been responsible for measuring outcomes of the Employment Opportunity Act, the Job Corps program, and Occupational and Safety and Health Administration (OSHA) programs.

Performance-monitoring research is a specific type of evaluation research that regularly, per- haps routinely, provides feedback for the evaluation and control of recurring business activity. For example, most firms continuously monitor wholesale and retail activity to ensure early detection of sales declines and other anomalies. In the grocery and retail drug industries, sales research may use the Universal Product Code (UPC) for packages, together with computerized cash registers and electronic scanners at checkout counters, to provide valuable market-share information to store and brand managers interested in the retail sales volume of specific products.

United Airlines’ Omnibus in-flight survey provides a good example of performance- monitoring research for quality management. United routinely selects sample flights and administers a questionnaire about in-flight service, food, and other aspects of air travel. The Omni- bus survey is conducted quarterly to deter- mine who is flying and for what reasons. It enables United to track demographic changes and to monitor customer ratings of its services on a continuing basis, allow- ing the airline to gather vast amounts of information at low cost. The information relating to customer reaction to services can be compared over time. For example, suppose United decided to change its menu for in-flight meals. The results of the Omnibus survey might indicate that,

evaluation research

The formal, objective measurement and appraisal of the extent a given activity, project, or program has achieved its objectives or whether continuing programs are presently performing as projected.

performance-monitoring research

Refers to research that regularly, sometimes routinely, provides feedback for evaluation and control of business activity.

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Fun in the snow depends on weather trends, economic outlook, equipment, and clothing—all subjects for a business researcher.

CHAPTER 1 The Role of Business Research • 9

shortly after the menu changed, the customers’ rating of the airline’s food declined. Such informa- tion about product quality is extremely valuable, as it allows management to quickly spot trends among passengers in many aspects of air travel, such as airport lounges, gate-line waits, or cabin cleanliness. Then managers can rapidly take action to remedy such problems.

When Is Business Research Needed? The need to make intelligent, informed decisions ultimately motivates an organization to engage in business research. Not every decision requires research. Thus, when confronting a key decision, a manager must initially decide whether or not to conduct business research. The determination of the need for research centers on (1) time constraints, (2) the availability of data, (3) the nature of the decision to be made, and (4) the value of the research information in relation to costs.

Time Constraints Systematic research takes time. In many instances, management believes that a decision must be made immediately, allowing no time for research. Decisions sometimes are made without adequate information or thorough understanding of the business situation. Although making decisions without researching a situation is not ideal, sometimes the urgency of a situation precludes the use of research. The urgency with which managers usually want to make decisions conflicts with researchers’ desire for rigor in following the scientific method.

Availability of Data Often managers already possess enough data, or information, to make sound decisions without additional research. When they lack adequate information, however, research must be considered. This means that data need to be collected from an appropriate source. If a potential source of data exists, managers will want to know how much it will cost to get the data.

Before Harley-Davidson goes overseas, it must perform considerable research on that market. It may find that consumers in some countries, such as France or Italy, have a strong preference for more economical and practical motorbikes. There, people may prefer a Vespa Wasp to a Harley Hog! Other times, they may find that consumers have a favorable attitude toward Harley-Davidson and that it could even be a product viewed as very prestigious. Harley recently considered doing business in India based on trend analysis showing a booming economy. Favorable consumer opinion and a booming economy were insufficient to justify distributing Harleys in India. The problem? Luxury imports would be subject to very high duties that would make them cost-prohibitive to nearly all Indian consumers and India has strict emission rules for motorbikes. Thus, although research on the market was largely positive, Harley’s research on the political operating environment eventually determined its decision. Even after considerable negotiation, India refused

to budge on tariffs although they were willing to give on emission standards. Instead, Harley may direct its effort more toward the U.S. women’s market for bikes. Research shows that motorcycle ownership among U.S. women has nearly doubled since 1990 to approximately 10 percent. Product research suggests that Harley may need to design smaller and sportier bikes to satisfy this market’s desires. Perhaps these new products would also be easier to market in India. Research will tell.

Sources: “Harley Davidson Rules Out India Foray for Near Future,” Asia-Africa Intelligence Wire (September 2, 2005); “Women Kick It into Gear,” Akron Beacon Journal (May 22, 2005); “No Duty Cut on Harley Davidson Bikes, India to US,” The Financial Express (February 24, 2008), www.financialexpress.com/news/No-duty-cut-on-Harley-Davidson-bikes- India-to-US/276635, accessed August 2, 2011.

Harley-Davidson Goes Abroad

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‘‘The secret of success is to know something nobody else knows.’’—ARISTOTLE ONASSIS

RESEARCH SNaPSHOt

If the data cannot be obtained, or it cannot be obtained in a timely fashion, this particular research project should not be conducted. For example, many African nations have never con- ducted a population census. Organizations engaged in international business often find that data about business activity or population characteristics that are readily available in the United States are nonexistent or sparse in developing countries. Imagine the problems facing researchers who wish to investigate market potential in places like Uzbekistan, Macedonia, or Rwanda.

Nature of the Decision The value of business research will depend on the nature of the managerial decision to be made. A routine tactical decision that does not require a substantial investment may not seem to warrant a substantial expenditure for research. For example, a computer company must update its operator’s instruction manual when it makes minor product modifications. The research cost of determining the proper wording to use in the updated manual is likely to be too high for such a minor deci- sion. The nature of the decision is not totally independent of the next issue to be considered: the benefits versus the costs of the research. In general, however, the more strategically or tactically important the decision, the more likely it is that research will be conducted.

Benefits versus Costs Earlier we discussed some of the managerial benefits of business research. Of course, conduct- ing research to obtain these benefits requires an expenditure of money. In any decision- making situation, managers must identify alternative courses of action and then weigh the value of each

RESEARCH SNaPSHOt

If you’ve ever checked the price of business-class airfare on a flight overseas, you were probably surprised at the price. A discounted round-trip coach ticket from Atlanta to Paris in peak season often costs just over one thousand dollars. That same business-class ticket would often cost between five and ten thousand-dollars! Typically, these flights take place in larger passenger aircraft such as a Boeing 747 or a Boeing 777. A Boeing 777 can seat up to 450 passengers. However, by including three dozen business-class seats, the capacity drops to under 400 passengers.

Thus, it is easy to see that a great deal of research must assess both the product design (what service and product attri- butes make up a business-class experience) and pricing (in both coach and business class) to determine the best configuration of the aircraft. Research shows that business-class travelers pri- oritize the comfort of the seat and the ability to be able to lie flat during the flight, the quality of food, and the convenience of boarding as attributes that make up the business-class experience.

In the past few years, a few start-up airlines have been trying to capitalize on this concept by starting “discount” business-class-only airlines. Maxjet estimated that consumers will exchange a little comfort for a reduction in price. They con- figured Boeing 737s (smaller than typical trans-ocean carriers) with 102 business-class seats that will not quite lie flat—and no

coach seats! The result is a business-class-only airline with cross- Atlantic fares ranging between $1,600 and $3,800, less than half of traditional business-class fares. Taking the concept to an even smaller scale, Eos configured Boeing 757s into 48-seat all- business-class planes.

Both Maxjet and Eos received positive reviews, along with some criticisms. For example, Maxjet did not provide power outlets for laptops at their seats, considered by some to be a “fatal flaw” as far as business-class service is considered. Despite the apparent appeal, both Maxjet (December 2007) and Eos (April 2008) declared bankruptcy.

Could more effective business research have determined these were not feasible business ventures? Or, could Maxjet’s “fatal flaw” of a lack of power outlets been identified? Sound business research may have enhanced the chance of success of these airlines.

Sources: McCarnety, Scott, “Start-Up Airlines Fly Only Business Class,” The Wall Street Journal (September 20, 2005), D1; Pitock, Todd, “ Getting There,” Forbes 176 (September 2005), 30–32; Robertson, David, “Eos Bankruptcy Filing Signals End to Cheap Executive Travel,” The Times (April 28, 2008).

Business Class Success?

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alternative against its cost. Business research can be thought of as an investment alternative. When deciding whether to make a decision without research or to postpone the decision in order to conduct research, managers should ask three questions:

1. Will the payoff or rate of return be worth the investment? 2. Will the information gained by business research improve the quality of the managerial deci-

sion enough to warrant the expenditure? 3. Is the proposed research expenditure the best use of the available funds?

For example, TV-Cable Week was not test-marketed before its launch. Although the magazine had articles and stories about television personalities and events, its main feature was program listings, channel by channel, showing the exact programs a particular subscriber could receive. To produce a custom magazine for each individual cable television system in the country required developing a costly computer system. Because that development necessitated a substantial expen- diture, one that could not be scaled down by research, conducting research was judged to be an unwise investment. The value of the potential research information was not positive because its cost exceeded its benefits. Unfortunately, pricing and distribution problems became so compelling after the magazine was launched that the product was a failure. Nevertheless, without the luxury of hindsight, managers made a reasonable decision not to conduct research. They analyzed the cost of the information relative to the potential benefits of the information. Exhibit 1.3 outlines the criteria for determining when to conduct business research.

Business Research in the Twenty-First Century Business research, like all business activity, continues to change. Changes in communication tech- nologies and the trend toward an ever more global marketplace have played a large role in many of these changes.

Communication Technologies Virtually everyone is “connected” today. Increasingly, many people are “connected” nearly all the time. Within the lifetime of the typical undergraduate college senior, the way information is exchanged, stored, and gathered has been revolutionized completely. Today, the amount of information formally contained in an entire library can rest easily in a single personal computer.

The speed with which information can be exchanged has also increased tremendously. During the 1970s, exchanging information overnight through a courier service from anywhere in

Time Constraints

Availability of Data

Nature of the Decision

Benefits versus Costs

Is sufficient time available before a decision will be made?

Yes →

Is it feasible to obtain the data? Yes

→

Is the decision of considerable strategic or tactical importance?

Yes →

Does the value of the research information exceed the cost of conducting research?

Yes →

Conduct Business Research

No ↓ No ↓ No ↓ No ↓

Do Not Conduct Business Research

EXHIBIT 1.3

Determining When to Conduct Business Research

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12 • PART ONE Introduction

the continental United States was heralded as a near miracle of modern technology. Today, we can exchange information from nearly anywhere in the world to nearly anywhere in the world almost instantly. Internet connections are now wireless, so one doesn’t have to be tethered to a wall to access the World Wide Web. Our mobile phones and handheld data devices can be used not only to converse, but also as a means of communication that can even involve business research data. In many cases, technology also has made it possible to store or collect data at a lower cost than in the past. Electronic communications are usually less costly than regular mail—and certainly less costly than a face-to-face interview—and cost about the same amount no matter how far away a respondent is from a researcher. Thus, the expressions “time is collapsing” and “distance is disappearing” capture the tremendous revolution in the speed and reach of our communication technologies.

Changes in computer technology have made for easier data collection and data analysis. As we discuss in Chapter 10, many consumer household panels now exist and can be accessed via the Internet. Thus, there is less need for the time and expense associated with regular mail survey approaches. Furthermore, the computing power necessary to solve complicated statistical prob- lems is now easily accessible. Again, as recently as the 1970s, such computer applications required expensive mainframe computers found only in very large corporations, major universities, and large governmental/military institutions. Researchers could expect to wait hours or even longer to get results from a statistical program involving 200 respondents. Today, even the most basic laptop computers can solve complicated statistical problems involving thousands of data points in practi- cally a nanosecond.

Global Business Research Like all business activities, business research has become increasingly global as more and more firms operate with few, if any, geographic boundaries. Some companies have extensive international

RESEARCH SNaPSHOt

Sales of U.S. distilled spirits have declined over the last 10 to 15 years as more Americans turn to wine or beer as their beverage of choice. As a result, companies like Bacardi and Brown-Forman, producers of Jack Daniels, have pursued busi- ness development strategies involving increased efforts to expand into international markets. The Brown-Forman budget for international ventures includes a significant allocation for research. By doing research before launching the product, Brown-Forman can learn product usage patterns within a par- ticular culture. Some of the findings from this research indicate:

1. Japanese consumers use Jack Daniels (JD) as a dinner bever- age. A party of four or five consumers in a restaurant will order and drink a bottle of JD with their meal.

2. Australian consumers mostly consume distilled spirits in their homes. Also in contrast to Japanese consumers, Australians prefer to mix JD with soft drinks or other mixers. As a result of this research, JD launched a mix- ture called “Jack and Cola” sold in 12-ounce bottles all around Australia. The product has been very successful.

3. British distilled spirit consumers also like mixed drinks, but they usually partake in bars and restaurants.

4. In China and India, consumers more often choose coun- terfeit or “knock-offs” to save money. Thus, innovative research approaches have addressed questions related to the way the black market works and how they can better educate consumers about the differences between the real thing and the knock-offs.

The result is that Jack Daniels is now sold extensively, in various forms, and with different promotional campaigns, outside of the United States.

Sources: Swibel, Mathew, “How Distiller Brown-Forman Gets Rich by Exploiting the Greenback’s Fall—and Pushing Its Brands Abroad,” Forbes 175, no. 8 (2005), 152–155.

“Jacques” Daniels

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research operations. Upjohn conducts research in 160 different countries. ACNielsen International, known for its television ratings, is the world’s largest research company. Two-thirds of its business comes from outside the United States.9 Starbucks can now be found in nearly every developed country on the Earth. ESPN offers its programming on multiple continents. DuPont has a signifi- cant presence in all regions of the world.

Companies that conduct business in foreign countries must understand the nature of those particular markets and judge whether they require customized business strategies. For example, although the 15 nations of the European Union share a single formal market, research shows that Europeans do not share identical tastes for many consumer products. Business researchers have found no such thing as a “typical” European consumer; language, religion, climate, and centuries of tradition divide the nations of the European Union. Scantel Research, a British firm that advises companies on color preferences, found inexplicable differences in Europeans’ preferences in medi- cines. The French prefer to pop purple pills, but the English and Dutch favor white ones. Consum- ers in all three countries dislike bright red capsules, which are big sellers in the United States. This example illustrates that companies that do business in Europe must research throughout Europe to adapt to local customs and buying habits.10

Even companies that produce brands that are icons in their own country are now doing research internationally. The Research Snapshot “ ‘Jacques’ Daniels” discusses how Brown-Forman, the parent company of Jack Daniels (the classic American “sour mash” or Bourbon whiskey), is now interviewing consumers in the far corners of the world.11 The internationalization of research places greater demands on business researchers and heightens the need for research tools that allow us to cross-validate research results, meaning that the empirical findings from one culture also exist and behave similarly in another culture. The development and application of these international research tools are an important topic in basic business research.12

Overview The business research process is often presented as a linear, sequential process, with one specific step following another. In reality, this is not the case. For example, the time spent on each step varies, overlap between steps is common, some stages may be omitted, occasionally we need to backtrack, and the order sometimes changes. Nonetheless, some structure for the research process is necessary.

The book is organized to provide this structure, both within each chapter and in the order of the chapters. Each chapter begins with a set of specific learning objectives. Each chapter then opens with a Chapter Vignette—a glimpse of a business research situation that provides a basis of reference for that chapter. Each chapter also contains multiple Research Snapshots—specific busi- ness research scenarios that illustrate key points. Finally, each chapter concludes with a review of the learning objectives.

The book is organized into seven parts. Part One is the Introduction, which includes this chapter and four others. This chapter provided an introduction to business research. The next three chapters of the book give students a fuller understanding of the business research environ- ment. Part Two, Beginning Stages of the Research Process, provides the foundation for business research, discussing problem definition and qualitative and secondary research. The third section of the book, Research Designs for Collecting Primary Data, introduces survey research, discusses observation as a research technique, and provides an overview of experimental research. Mea- surement Concepts, Part Four of the book, discusses the measurement of research constructs and questionnaire design. Part Five, Sampling and Fieldwork, describes the process involved in selecting a research sample and collecting data. Part Six, Data Analysis and Presentation, explains the various approaches to analyzing the data and describes methods of presentation. The book concludes with Part Seven, Comprehensive Cases with Computerized Databases, which will be integrated throughout the first six parts. Exhibit 1.4 provides an overview of the book and the research process.

cross-validate

To verify that the empirical findings from one culture also

exist and behave similarly in another culture.

14 • PART ONE Introduction

SUMMARY There were six learning objectives in this chapter. After reading the chapter, the student should be competent in each area described by a learning objective.

1. Understand how research contributes to business success. While many business decisions are made “by the seat of the pants” or based on a manager’s intuition, this type of decision making carries with it a large amount of risk. By first researching an issue and gathering the appropriate information (from employees, customers, competitors, and the market) managers can make a more informed decision. The result is less risky decision making.

Research is the intelligence-gathering function in business. The intelligence includes infor- mation about customers, competitors, economic trends, employees, and other factors that affect business success. This intelligence assists in decisions ranging from long-range planning to near- term tactical decisions.

2. Know how to define business research. Business research is the application of the scientific method in searching for truth about business phenomena. The research must be conducted sys- tematically, not haphazardly. It must be objective to avoid the distorting effects of personal bias. Business research should be rigorous, but the rigor is always traded off against the resource and time constraints that go with a particular business decision.

3. Understand the difference between basic and applied business research. Applied business research seeks to facilitate managerial decision making. It is directed toward a specific managerial decision in a particular organization. Basic or pure research seeks to increase knowledge of theories and concepts. Both are important, but applied research is more often the topic in this text.

4. Understand how research activities can be used to address business decisions. Businesses can make more accurate decisions about dealing with problems and/or the opportunities to pursue and how to best pursue them. The chapter provides examples of studies involving several dimen- sions of managerial decision making. Thus, business research is useful both in a strategic and in a tactical sense.

EXHIBIT 1.4

An Overview of Business Research

Sharing the Results Co

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Analyzing the Data

Collecting the Data

Determining the Sample

Designing the Data Collection Instrument

Understanding Measurement of Research Constructs

Research Techniques: Observation and Experimentation

Problem Definition, Secondary and Qualitative Research

Understanding Business Research

Chapters 5, 6, & 7

Chapters 8, 9, 10, 11, & 12

Chapters 13 & 14

Chapter 15

Chapters 16 & 17

Chapter 18

Chapters 19–24

Chapter 25

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Chapters 1, 2, 3, & 4 1

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CHAPTER 1 The Role of Business Research • 15

5. Know when business research should and should not be conducted. Managers determine whether research should be conducted based on (1) time constraints, (2) availability of data, (3) the nature of the decision to be made, and (4) the benefit of the research information versus its cost.

6. Appreciate the way that technology and internationalization are changing business research. Technology has changed almost every aspect of business research. Modern computer and communications technology makes data collection, study design, data analysis, data reporting, and practically all other aspects of research easier and better. Furthermore, as more companies do business outside their own borders, companies are conducting research globally. This places a greater emphasis on research that can assess the degree to which research tools can be applied and interpreted the same way in different cultures. Thus, research techniques often must cross- validate results.

KEY TERMS AND CONCEPTS applied business research, 5 basic business research, 5 business research, 5 cross-validate, 14

evaluation research, 9 marketing-oriented, 7 performance-monitoring research, 9 product-oriented, 7

production-oriented, 7 the scientific method, 6

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Is it possible to make sound managerial decisions without busi-

ness research? What advantages does research offer to the deci- sion maker over seat-of-the-pants decision making?

2. Define a marketing orientation and a product orientation. Under which strategic orientation is there a greater need for business research?

3. Name some products that logically might have been developed with the help of business research.

4. Define business research and describe its task. 5. Which of the following organizations are likely to use business

research? Why? How? a. Manufacturer of breakfast cereals b. Manufacturer of nuts, bolts, and other fasteners c. The Federal Trade Commission d. A hospital e. A company that publishes business textbooks

6. An automobile manufacturer is conducting research in an attempt to predict the type of car design consumers will desire in the year 2020. Is this basic or applied research? Explain.

7. Comment on the following statements: a. Managers are paid to take chances with decisions. Research-

ers are paid to reduce the risk of making those decisions. b. A business strategy can be no better than the information on

which it is formulated. c. The purpose of research is to solve business problems.

8. List the conditions that help a researcher decide when research should or should not be conducted.

9. How have technology and internationalization affected business research?

10. ‘NEt How do you believe the Internet has facilitated research? Try to use the Internet to find the total annual sales for Starbucks and for DuPont.

11. What types of tools does the researcher need to use more given the ever increasing internationalization of business?

RESEARCH ACTIVITIES 1. ‘NEt Suppose you owned a jewelry store in Denton, Texas.

You are considering opening a second store just like your current store. You are undecided on whether to locate the new store in another location in Denton, Texas, or in Birmingham, Alabama. Why would you decide to have some research done before making the decision? Should the research be conducted? Go to http://www.census.gov. Do you think any of this information would be useful in the research?

2. ‘NEt Find recent examples of news articles involving the use of business research in making decisions about different aspects of business.

3. ‘NEt Find an article illustrating an example of an applied research study involving some aspect of technology. How does it differ from a basic research study also focusing on a similar aspect of technology?

16 Part 1: Introduction

After studying this chapter, you should be able to

1. Know and distinguish the concepts of data, information, and intelligence

2. Understand the four characteristics that describe data

3. Know the purpose of research in assisting business operations

4. Know what a decision support system is and does

5. Recognize the major categories of databases

LEARNING OUTCOMES

C H A P T E R

2 Chapter Vignette:

Delivery, Data, and UPS

Information Systems and Knowledge Management

W hen it comes to package delivery, United Parcel Service (UPS) beats them all. UPS is the largest package delivery company in the world, both in terms of the number

of packages it delivers (almost four billion packages per year), and the revenue that it generates (almost 45 billion dollars in 2010). While it may be easy to think about UPS as a company focused on the U.S. market, it is actually a global corpora tion that delivers more packages per day in international markets than in the U.S. How does a company of this size combine both global services with local delivery? The answer is through an intense investment in data man agement and integration, with the goal of leveraging data to meet its core global business goals, namely timely and accurate package delivery.

With a yearly technology investment of over one billion dollars a year, UPS succeeds by making sure data man agement is a key driver of its business

processes. Using a global ground and air network, the company has the only integrated data collection and management system that incorporates all levels of services, both global and domes tic, in one pickup and delivery system. With 3,200 network sites across the globe, and a presence in over 220 countries,

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Introduction UPS’s focus (and investment) in information illustrates the sometimes sophisticated way in which modern businesses integrate data into their decision processes. Many of the decisions that used to be made with guesswork are now supplemented with “intelligence” either automatically delivered by some computer software or drawn from a data warehouse.

Global delivery companies certainly aren’t alone in this effort. Imagine all the information that passes through a single Home Depot store each day. Every customer transaction, every empty shelf, every employee’s work schedule—right down to the schedule to clean restrooms—creates poten- tially valuable information that can be used by researchers and decision makers. Considering that Home Depot operates thousands of stores, obviously, Home Depot needs a data depot!

While UPS controls its data storage and management, Home Depot has outsourced the storage and management of data inventories. In this case, IBM manages the data, allowing it to be integrated into management strategy and tactics. Data from cash registers, time clocks, shelf counts, and much more are all compiled, analyzed, and either fed automatically into management systems or supplied in the form of a research report. In a way, this type of business research is automatic!2

This chapter discusses knowledge management and the role decision support systems play in helping firms make informed business decisions. The chapter also introduces the concept of global information systems and sources of data that exist beyond the walls of any business. Modern data technology allows businesses to more easily integrate research into strategy and operations.

Information, Data, and Intelligence In everyday language, terms like information and data are often used interchangeably. Research- ers use these terms in specific ways that emphasize how useful each can be. Data are simply facts or recorded measures of certain phenomena (things or events). Information is data formatted (structured) to support decision making or to define the relationship between two facts. Business intelligence is the subset of data and information that actually has some explanatory power enabling effective managerial decisions to be made. So, there is more data than information, and more infor- mation than intelligence.

Think again about the thousands upon thousands of unsummarized facts recorded by Home Depot each day. Each time a product is scanned at checkout, that fact is recorded and becomes data. Each customer’s transactions are simultaneously entered into the store’s computerized inventory system. The inventory system structures the data in such a way that a stock report can be generated and orders for that store can be placed. Thus, the automated inventory system turns data into infor- mation. Further, the information from each store’s sales and inventory records may be harvested by analysts. The analysts may analyze the trends and prepare reports that help Home Depot buyers get the right products into each store or even suggest places for new Home Depot locations. Thus, the analyst has now completed the transformation of data into intelligence. Exhibit 2.1 helps to illustrate the distinction between data, information, and intelligence.

data

Facts or recorded measures of certain phenomena (things).

information

Data formatted (structured) to support decision making

or define the relationship between two facts.

business intelligence

The subset of data and information that actually has

some explanatory power enabling effective decisions to

be made.

UPS leverages its data intelligence to create realworld, realtime information throughout its global delivery network.

It is important to remember that technology investment in data and information services never stops for a company such as UPS. From information and tracking applications for comput ers and smartphones, to the use of telematics (which incorpo rates global positioning systems with package information),

UPS sees information systems as a core component of its business success.

So, the next time you meet the UPS driver dropping off a package to you, realize that there is another “driver” with them—the data behind the driver that helps makes sure that you got the right package, at the right time.1

18 • PART ONE Introduction

The Characteristics of Valuable Information Not all data are valuable to decision makers. Useful data become information and help a business manager make decisions, and can also become intelligence. Four characteristics help determine how useful data may be: relevance, quality, timeliness, and completeness.

Relevance Relevance is a characteristic of data reflecting how pertinent these particular facts are to the situ- ation at hand. Put another way, the facts are logically connected to the situation. Unfortunately, irrelevant data and information often creep into decision making. One particularly useful way to distinguish relevance from irrelevance is to think about how things change. Relevant data are facts about things that can be changed, and if they are changed, it will materially alter the situation. So, this simple question becomes important:

Will a change in the data coincide with a change in some important outcome?

EXHIBIT 2.1

Data, Information, Intelligence

• Products purchased are recorded by the scanner forming data.

• Inventory systems use the data to create information. • The information tells managers what items need to be stocked. • The information also generates and can even place orders for more products to be trucked to the store.

• Analysts analyze the data statistically and write research reports addressing important questions such as

What types of trends exist in customer purchases, and are there regional differences?

Where should new stores be located?

relevance

A characteristic of data reflecting how pertinent these particular facts are to the situation at hand.

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CHAPTER 2 Information Systems and Knowledge Management • 19

For example, businesses across the globe are changing the amount and degree of information they need concerning their shipped packages, information trends that are very relevant to UPS. If companies become more Internet-focused with this information, than UPS must revise its own service offerings to incorporate these changing preferences in their business model.

Quality Data quality is the degree to which data represent the true situation. High-quality data are accu- rate, valid, and reliable, issues we discuss in detail in later chapters. High-quality data represent reality faithfully. If a consumer were to replace the product UPC from one drill at Home Depot with one from a different drill, not only would the consumer be acting unethically, but it would also mean that the data collected at the checkout counter would be inaccurate. Therefore, to the extent that the cash register is not actually recording the products that consumers take out of the stores, its quality is lowered. Sometimes, researchers will try to obtain the same data from multiple data sources as one check on its quality.3 Data quality is a critical issue in business research, and it will be discussed throughout this text.

Timeliness Business is a dynamic field in which out-of-date information can lead to poor decisions. Busi- ness information must be timely—that is, provided at the right time. Computerized information systems can record events and dispense relevant information soon after the event. A great deal of business information becomes available almost at the moment that a transaction occurs. Timeliness means that the data are current enough to still be relevant.

Computer technology has redefined standards for timely information. For example, if a busi- ness executive at Home Depot wishes to know the sales volume of any store worldwide, detailed information about any of thousands of products can be instantly determined. At Home Depot, the point-of-sale checkout system uses UPC scanners and satellite communications to link individual stores to the headquarters’ computer system, from which managers can retrieve and analyze up-to- the-minute sales data on all merchandise in each store.

Completeness Information completeness refers to having the right amount of information. Managers must have sufficient information about all aspects of their decisions. For example, a company considering establishing a production facility in Eastern Europe may plan to analyze four former Soviet-bloc countries. Population statistics, GDP, and information on inflation rates may be available on all four

information completeness

Having the right amount of information.

timeliness

Means that the data are current enough to still be

relevant.

data quality

The degree to which data represent the true situation.

SURVEY

THIS! Go back and review the questionnaire that you responded to last chapter. Later, you’ll be asked to analyze data with the hope of predicting and explaining some important outcomes with business implications. Now, which sec tions do you think would provide the most value to a headhunting firm that matches employers to potential employees? What kinds of information will this section of the survey yield and how might it help the headhunting firm?

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countries. However, information about unemployment levels may be available for only three of the countries. If information about unemployment or other characteristics cannot be obtained, the information is incomplete. Often incomplete information leads decision makers to conduct their own business research.

Knowledge Management Who has the best pizza in town? The answer to this question requires knowledge. Indeed, you, as a consumer, have stored knowledge about many products. You know the best restaurants, best the- aters, best bars, and so forth. All of this knowledge helps you make decisions as a consumer. Much of it is based on personal research involving product trials or searches for information. From an individual’s perspective, knowledge is simply what you have stored in memory. It helps you make decisions about a variety of things in your life.

Organizations can use knowledge in a similar way. Knowledge is accumulated not just from a single individual, however, but from many sources. Financial managers, human resource managers, sales managers, customer reports, economic forecasts, and custom-ordered research all contribute to an organization’s knowledge base. All of this data forms the organization’s memory. From a company’s perspective, knowledge is a blend of previous experience, insight, and data that forms organizational memory. It provides a framework that can be thoughtfully applied when assessing a business problem. Business researchers and decision makers use this knowledge to help create solutions to strategic and tactical problems. Thus, knowledge is a key resource and a potential competitive advantage.4

Knowledge management is the process of creating an inclusive, comprehensive, easily accessible organizational memory, which can be called the organization’s intellectual capital.5 The purpose of knowledge management is to organize the intellectual capital of an organization in a formally structured way for easy use. Knowledge is presented in a way that helps managers comprehend and

knowledge

A blend of previous experience, insight, and data that forms organizational memory.

knowledge management

The process of creating an inclusive, comprehensive, easily accessible organizational memory, which is often called the organization’s intellectual capital.

RESEARCH SNAPSHOT

Radio frequency identification (RFID) tags have been used by large organizations for several years now. The U.S. military makes great use of RFIDs in tracking the whereabouts of virtually all kinds of products both big and small. Logistics officers can instantly track the whereabouts of Humvees and MREs (Meals Ready to Eat). Information from the tag is transmitted to computer servers and then directly into a GTN (Global Tracking Network). Equipment and supplies can then be ordered and dispatched to needed locations with minimal human contact. Product consumption ( ammunition, food, water, computer printers, and so forth) can also be tracked in real time. The Marines can know in real time if personnel in a desert use more food and water than personnel in a jungle.

Walmart is pushing suppliers to adopt the technology. Not only can Walmart use RFIDs in logistical operations, but the potential exists to “go into” consumers’ homes and track how much and the way consumers actually consume products. Potentially, decision support systems (DSS) could tie ordering to

customer consumption. However, the costs of RFIDs make them impractical for many suppliers.

Alien Technology Corporation recently announced a drop in the price of RFID tags. Now, when a company orders a million or more, the unit cost for an RFID is 12.9¢. Although this is a “basic” RFID tag, it still can store 96 bits of information. Analysts predict that the price of RFID tags will continue to drop. By 2011, the cost for a passive RFID tag continues to range from 9¢ to 25¢, which points to the possibility that the use of RFID technology in business research and operations should continue to soar.

Sources: Clark, Don, “Alien Cuts Radio ID Tag Price to Spur Adoption by Retailers,” The Wall Street Journal (September 12, 2005), D4; Fergueson, R. B., “Marines Deploy RFID,” e-Week 21 (November 15, 2004), 37; >; http:// rfid.net/best-practices/43-best-practices/135-passive-rfid-smart-label-buyers- guide downloaded August 17, 2011.

RFID Technology Gets Cheaper—Business Knowledge Grows

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act on that information and make better decisions in all areas of business. Knowledge management systems are particularly useful in making data available across the functional areas of the firm. Thus, marketing, management, and financial knowledge can be integrated. Recent research demonstrates how knowledge management systems are particularly useful in new product development and introduction.6

The firm’s sales force plays a particularly useful role in the knowledge management process. Salespeople are in a key position to have a lot of knowledge about customers and the firm’s capabilities. Thus, they are tools both for accumulating knowledge and for turning it into useful information.7 Market-oriented organizations generally provide both formal and informal methods through which the knowledge gained by salespeople can be entered into a data warehouse to assist all decision makers, not just the sales force.

Global Information Systems Increased global competition and technological advances in interactive media have given rise to global information systems. A global information system is an organized collection of computer hardware, software, data, and personnel designed to capture, store, update, manipulate, analyze, and immediately display information about worldwide business activities. It is a tool for providing past, present, and projected information on internal operations and external activity. Using satellite communications, high-speed microcomputers, electronic data interchanges, fiber optics, data stor- age devices, and other technological advances in interactive media, global information systems are changing the nature of business.

Consider the chapter vignette presented earlier. At any moment, UPS can track the sta- tus of any shipment around the world. UPS drivers use handheld electronic clipboards called delivery information acquisition devices (DIADs) to record appropriate data about each pickup or delivery. The data are then entered into the company’s main computer for record-keeping and analysis. A satellite telecommunications system allows UPS to track any shipment for a customer.

Also, as noted in the Research Snapshot “RFID Technology Gets Cheaper—Business Knowl- edge Grows”, RFIDs can be used to track products and greatly assist a company’s global informa- tion system.8

With so much diverse information available in a global information system, organizations have found it necessary to determine what data, information, and knowledge are most useful to particular business units.

global information system

An organized collection of computer hardware,

software, data, and personnel designed to capture, store,

update, manipulate, analyze, and immediately display

information about worldwide business activity.

A business traveler checks into a Wyndham hotel and finds his favorite type of pillow, favorite snacks, and one of his favorite types of wine waiting upon arrival. Another customer daydreams of a recent golf vacation to Hawaii and wishes she could do it again. Later that day, an email from Travelocity arrives with a great package deal to visit the same resort. Yet another con sumer visits BarnesandNoble.com and a popup displays a new novel by his favorite author. Using a system called active data warehousing, the companies integrate data with research results that allow them to predict consumer preferences and even cycli cal usage patterns quite accurately. Modern technology gives these firms a big advantage in the marketplace. Firms that don’t

adapt the technology may have a much harder time serving their customers. The latest technologies even provide ways for cus tomers to voluntarily enter data or block certain data from being transmitted to the companies they do business with.

Sources: Dewey, A., Rudolf, P., Woolfork, M., Berman, R., Bee, S., Partridge, P., & Hotchkiss, D., “The Active Data Warehouse: Where Agile Retailers Win by Capitalizing on Time,” Teradata Corporation (EB-4923); Watson, Richard T., “I Am My Own Database,” Harvard Business Review 82 (November 2004), 18–19.

Are Businesses Clairvoyant?

RESEARCH SNAPSHOT

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Decision Support Systems Business research can be described in many ways. One way is to categorize research based on the four possible functions it serves in business:

1. Foundational—answers basic questions. What business should we be in? 2. Testing—addresses things like new product concepts or promotional ideas. How effective

will they be? 3. Issues—examines how specific issues impact the firm. How does organizational structure

impact employee job satisfaction and turnover? 4. Performance—monitors specific metrics including financial statistics like profitability and

delivery times. This type of research is critical in real-time management and in “what-if” types of analyses examining the potential impact of a change in policy.

Of these, it is the performance category that is of most interest to decision support systems. The metrics that are monitored can be fed into automated decision-making systems, or they can trigger reports that are delivered to managers. These form the basis of a decision support system and best typify the way business research assists managers with day-to-day operational decisions.

A decision support system (DSS) is a system that helps decision makers confront problems through direct interaction with computerized databases and analytical software programs. The purpose of a decision support system is to store data and transform them into organized informa- tion that is easily accessible to managers. Doing so saves managers countless hours so that decisions that might take days or even weeks otherwise can be made in minutes using a DSS.

Modern decision support systems greatly facilitate customer relationship management (CRM). A CRM system is the part of the DSS that addresses exchanges between the firm and its customers. It brings together information about customers including sales data, market trends, marketing pro- motions and the way consumers respond to them, customer preferences, and more. A CRM system describes customer relationships in sufficient detail so that financial directors, marketing manag- ers, salespeople, customer service representatives, and perhaps the customers themselves can access information directly, match customer needs with satisfying product offerings, remind customers of service requirements, and know what other products a customer has purchased.

Casinos track regular customers’ behavior via “players’ cards” that are swiped each time a con- sumer conducts a transaction. This information is fed automatically into a CRM system that creates tailor-made promotional packages. The promotion may be unique to a specific customer’s prefer- ences as tracked by their own pattern of behavior. You may notice when visiting certain websites that they seem to be able to predict your behavior. The Research Snapshot “Are Businesses Clairvoyant?” explains how a CRM may be behind this clairvoyance.

Exhibit 2.2 provides a basic illustration of a decision support system. Raw, unsummarized data are input to the DSS. Data collected in business research projects are a major source of this input, but the data may be purchased or collected by accountants, financial officers, sales managers, production managers, or company employees other than business researchers. Effective businesses spend a great deal of time and effort collecting information for input into the decision support

EXHIBIT 2.2

Decision Support System

decision support system (DSS)

A computerbased system that helps decision makers confront problems through direct interaction with databases and analytical software programs.

customer relationship management (CRM)

The part of the DSS that addresses exchanges between the firm and its customers.

Decision Support System

Database Software

Input Output

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CHAPTER 2 Information Systems and Knowledge Management • 23

system. Useful information is the output of a DSS. A decision support system requires both data- bases and software. For firms operating across national borders, the DSS becomes part of its global information system.

Databases and Data Warehousing A database is a collection of raw data arranged logically and organized in a form that can be stored and processed by a computer. A customer mailing list is one type of database. Population charac- teristics may be recorded by state, county, and city in another database. Production figures and costs can come from internal company records. Modern computer technology makes both the storage and retrieval of this information easy and convenient. Twenty years ago, retrieving the population data needed to do a retail site analysis may have required days, possibly weeks, in a library. Today, the information is just a few clicks away.

Data warehousing is the process allowing important day-to-day operational data to be stored and organized for simplified access. More specifically, a data warehouse is the multitiered com- puter storehouse of current and historical data. Data warehouse management requires that the detailed data from operational systems be extracted, transformed, placed into logical partitions (for example, daily data, weekly data, etc.), and stored in a consistent manner. Organizations with data warehouses may integrate databases from both inside and outside the company. Managing a data warehouse effectively requires considerable computing power and expertise. As a result, data ware- house companies exist that provide this service for companies in return for a fee.9 Data warehous- ing allows for sophisticated analysis, such as data mining, discussed more in Chapter 8.

Input Management How does data end up in a data warehouse where it can be used by a decision support system? In other words, how is the input managed? Input includes all the numerical, text, voice, and image data that enter the DSS. Systematic accumulation of pertinent, timely, and accurate data is essential to the success of a decision support system.

DSS managers, systems analysts, and programmers are responsible for the decision support sys- tem as a whole, but many functions within an organization provide input data. Business researchers, accountants, corporate librarians, personnel directors, salespeople, production managers, and many others within the organization help to collect data and provide input for the DSS. Input data can also come from external sources.

Exhibit 2.3 shows five major sources of data input: internal records, proprietary business research, salesperson input, behavioral tracking, and outside vendors and external distributors of data. Each source can provide valuable input.

Internal Records Internal records, such as accounting reports of production costs and sales figures, provide consider- able data that may become useful information for managers. An effective data collection system establishes orderly procedures to ensure that data about costs, shipments, inventory, sales, and other aspects of regular operations are routinely collected and entered into the computer.

Proprietary Business Research Business research has already been defined as a broad set of procedures and methods. To clarify the DSS concept, consider a narrower view of business research. Proprietary business research emphasizes the company’s gathering of new data. Few proprietary research procedures and meth- ods are conducted regularly or continuously. Instead, research projects conducted to study specific company problems generate data; this is proprietary business research. Providing managers with nonroutine data that otherwise would not be available is a major function of proprietary business research. Earlier, we discussed four categories of research. Proprietary research often involves either the testing and/or issues types of research.

proprietary business research

The gathering of new data to investigate specific problems.

database

A collection of raw data arranged logically and

organized in a form that can be stored and processed by a

computer.

data warehousing

The process allowing important daytoday operational data to

be stored and organized for simplified access.

data warehouse

The multitiered computer storehouse of current and

historical data.

24 • PART ONE Introduction

Salesperson Input Salespeople are typically a business’s boundary spanners, the link between the organization and external environments. Since they are in touch with these outside entities, they commonly pro- vide essential business data. Sales representatives’ reports frequently alert managers to changes in competitors’ prices and new product offerings. They also may involve the types of complaints salespeople are hearing from customers. As trends become evident, this data may become business intelligence, leading to a change in product design or service delivery.

EXHIBIT 2.3

Six Major Sources of Input for Decision Support Systems

Source:

Internal Records

Proprietary Marketing Research

Salesperson Input

Behavioral Tracking

Scanner data, click-through sequences, Global Positioning Satellite (GPS) System records, automated customer counts

Customer profiles, previous orders, inventory, product sales histories Survey findings, test market results, new product forecasts Customer complaints and comments, changes in competitors’ goods and services

Social networking sites, Internet blogs, chat

Web Tracking

Outside Vendors and External Distributors

Industry sales trends, competitors’ market shares, demographics

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CHAPTER 2 Information Systems and Knowledge Management • 25

Behavioral Tracking Modern technology provides new ways of tracking human behavior. Global positioning satellite (GPS) systems allow management to track the whereabouts of delivery personnel at all times. This is the same system that provides directions through an automobile’s navigation system. For example, if your delivery person takes a quick break for nine holes of golf at Weaver Ridge or decides to stop at Gorman’s Pub for a couple of beers mid-afternoon, management can spot these as deviations from the appropriate delivery route. Thus, it can help track which employees are doing their jobs well.

Technology also allows firms to track actual customer behavior. While it’s true that GPS tracking of customers physical location is also possible, as the photograph suggests, the Internet also greatly facilitates customer behavior tracking. For instance, Google tracks the “click-through” sequence of customers. Therefore, if a customer is searching for information on refrigerators, and then goes to BestBuy.com, Google can track this behavior and use the information to let Best Buy know how important it is to advertise on Google and even automate pricing for advertisers.10

Purchase behavior can also be tracked at the point of sale. Scanner data refers to the accumu- lated records resulting from point-of-sale data recordings. In other words, each time products are scanned at a checkout counter, the information can be stored. The term single-source data refers to a system’s ability to gather several types of interrelated data, such as type of purchase, use of a sales promotion, or advertising frequency data, from a single source in a format that will facilitate integration, comparison, and analysis.

Outside Vendors and External Distributors Outside vendors and external distributors market information as their products. Many organiza- tions specialize in the collection and publication of high-quality information. One outside vendor, Nielsen, provides television program ratings, audience counts, and information about the demo- graphic composition of television viewer groups. Other vendors specialize in the distribution of information. Public libraries have always purchased information, traditionally in the form of books, and they have served as distributors of this information.

Media representatives often provide useful demographic and lifestyle data about their audi- ences. Advertising Age, The Wall Street Journal, Sales and Marketing Management, and other

scanner data

The accumulated records resulting from pointofsale

data recordings.

The DSS of any organization is no better than the quality of the data input to its data warehouse. How can firms make sure that the input remains relevant and retains a “hightouch” compo nent in a “hightech” world?

Home Depot has always tried to make sure its execu tives “stay in touch” by requiring them to spend a substantial amount of time on the sales floor of a Home Depot store, which means that one of the folks in the bright orange apron helping you choose the right flush valve may well be a sixfigure execu tive. When Jack VanWoerkom was named new executive vice president, general counsel, and corporate secretary in 2007, his first tasks were not at the corporate headquarters in Atlanta, but rather working in the aisles of a store. Therefore, the people who decide what should go into the data warehouse and how the DSS will use it maintain an appreciation for the types of decisions faced by Home Depot store managers each and every day. Home Depot even asks outside suppliers who may be involved in information technology (IT) design to spend a

few days in an actual Home Depot store. Thus, as Home Depot implements key innovations in its data networks, the people helping it to do so understand what the information needs of employees really are. Even Home Depot’s outside directors meet with middle managers and conduct store visits so that they can provide more meaningful advice to senior executives. Part of this advice concerns the data needs of Home Depot managers.

Do you think such a plan would be similarly successful for a company like UPS? How?

Sources: Lublin, Joanne, “Home Depot Board Gains Insight from Trenches,” The Wall Street Journal (October 10, 2005), B3; Tucker, Katheryn Hayes, “New Home Depot GC Learns Ropes at Store” Fulton County Daily Report (July 18, 2007).

Staying Home at Home Depot

RESEARCH SNAPSHOT

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business-oriented publications are important sources of information. These publications keep managers up-to-date about the economy, competitors’ activi- ties, and other aspects of the business environment.

Companies called data specialists record and store certain business information. Computer technology has changed the way many of these organizations sup- ply data, favoring the development of computerized databases.

Computerized Data Archives Historically, collections of organized and readily retrievable data were available in printed form at libraries. The Statistical Abstract of the United States, which is filled with tables of statistical facts, is a typical example. As with many resources, the Statistical Abstract is now available electronically. Users can purchase it via CD-ROM or access it via the Internet. The entire 2000 U.S. Census, the 2007 Economic Census, as well as projections through the current year are available at http://www.census.gov. More and more data are available in digitized form every day.

Numerous computerized search and retrieval systems and electronic databases are available as subscription services or in libraries. Just as a student can query the school library to find infor- mation for a term paper without leaving home, data acquisition for businesses has also become far more convenient in recent years. Today, business people access online information search and retrieval services, such as Dow Jones News Retrieval and Bloomberg Financial Markets, without leaving their offices. In fact, an increasing range of information services can be accessed from remote locations via digital wireless devices.

Modern library patrons can command a computer to search indexes and retrieve databases from a range of vendors. Just as wholesalers collect goods from manufacturers and offer them for sale to retailers who then provide them to consumers, many information firms serve as data wholesalers. Data wholesalers put together consortia of data sources into packages that are offered to munici- pal, corporate, and university libraries for a fee. Information users then access the data through these libraries. Some of the better known databases include Wilson Business Center, Hoovers, PROQUEST, INFOTRAC, DIALOG (Dialog Information Services, Inc.), LEXIS-NEXIS, and Dow Jones News Retrieval Services. These databases provide all types of information including recent news stories and data tables charting statistical trends.

DIALOG, for example, maintains more than 600 databases. A typical database may have a million or more records, each consisting of a one- or two-paragraph abstract that summarizes the major points of a published article along with bibliographic information. One of the DIALOG databases, ABI/INFORM, abstracts significant articles in more than one thousand current business and management journals. Many computerized archives provide full-text downloads of published articles about companies and various research topics.

Exhibit 2.4 illustrates the services provided by two popular vendors of information services that elec- tronically index numerous databases. For a more extensive listing, see the Gale Directory of Databases.11

Several types of databases from outside vendors and external distributors are so fundamental to decision support systems that they deserve further explanation. The following sections discuss statistical databases, financial databases, and video databases in slightly more detail.

Statistical Databases Statistical databases contain numerical data for analysis and forecasting. Often demographic, sales, and other relevant business variables are recorded by geographical area. Geographic information systems use these geographical databases and powerful software to prepare computer maps of relevant

data wholesalers

Companies that put together consortia of data sources into packages that are offered to municipal, corporate, and university libraries for a fee.

In the past, data archives were only available in print form. Today, online data is becoming the most common way to access business research data.

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CHAPTER 2 Information Systems and Knowledge Management • 27

variables. Companies such as Claritas, Urban Decision Systems, and CACI all offer geographic/ demographic databases that are widely used in industry.

One source for these huge data warehouses is scanner data. Substituting electronic record- keeping like optical scanners for human record-keeping results in greater accuracy and more rapid feedback about store activity.

One weakness of scanner data is that not all points of sale have scanner technology. For instance, many convenience stores lack scanner technology, as do most vending machines. Thus, those purchases go unrecorded. The Universal Product Code, or UPC, contains information on the category of goods, the manufacturer, and product identification based on size, flavor, color, and so on. This is what the optical scanner actually reads. If a large percentage of a brand’s sales occur in environments without the ability to read the UPC code, the business should be aware that the scanner data may not be representative.

Financial Databases Competitors’ and customers’ financial data, such as income statements and balance sheets, are of obvious interest to business managers. These are easy to access in financial databases. CompuStat publishes an extensive financial database on thousands of companies, broken down by industry and other criteria. To illustrate the depth of this pool of information, CompuStat Global offers exten- sive data on 6,650 companies in more than 30 countries in Europe, the Pacific Rim, and North America.

Vendors Selected Databases Type of Data

DIALOG ABI/INFORM Summaries and citations from over 1,000 academic management, marketing, and general business journals with full text of more than 500 of these publications

ASI (American Statistics Index)

Abstracts and indexes of federal government statistical publications

PROMT (The Predicast Overview of Markets and Technologies)

Summaries and full text from 1,000 U.S. and international business and trade journals, industry newsletters, newspapers, and business research studies; information about industries and companies, including the products and technologies they develop and the markets in which they compete

Investext Full text of over 2 million company, industry, and geographic research reports written by analysts at more than 600 leading investment banks, brokerage houses, and consulting firms worldwide

Dow Jones News Retrieval Business Newsstand Articles from New York Times, Los Angeles Times, Washington Post, and other leading newspapers and magazines

Historical Data Center Historical data on securities, dividends, and exchange rates

Web Center Information obtained from searches of corporate, industry, government, and news websites

EXHIBIT 2.4

Vendors of Information Services and Electronic Indexing

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28 • PART ONE Introduction

Video Databases Video databases and streaming media are having a major impact on many goods and services. For example, movie studios provide clips of upcoming films and advertising agencies put television commercials on the Internet (see http://www.adcritic.com). McDonald’s maintains a digital archive of television commercials and other video footage to share with its franchisees around the world. The video database enables franchisees and their advertising agencies to create local advertising without the need for filming the same types of scenes already archived. Just imagine the value of digital video databases to advertising agencies’ decision support systems!

Networks and Electronic Data Interchange Individual personal computers can be connected through networks to other computers. Network- ing involves linking two or more computers to share data and software.

Electronic data interchange (EDI) systems integrate one company’s computer system directly with another company’s system. Much of the input to a company’s decision support system may come through networks from other companies’ computers. Companies such as GXS (GXS.com) allow corporations to exchange business information with suppliers or customers. For example, every evening Walmart transmits millions of characters of data about the day’s sales to its apparel suppliers. Wrangler, a supplier of blue jeans, shares the data and a model that interprets the data. Wrangler also shares software applications that act to replenish stock in Walmart stores. This DSS lets Wrangler’s managers know when to send specific quantities of specific sizes and colors of jeans to specific stores from specific warehouses. The result is a learning loop that lowers inven- tory costs and leads to fewer stockouts.

The Internet and Research When most readers of this book were born, the Internet had yet to enter the everyday vocabulary. In fact, few people outside of a small number of universities and the U.S. Department of Defense had any clue as to what the Internet might be. In the 1960s, mainframe computers revolutionized research by allowing researchers to use research techniques involving large numbers of math- ematical computations that previously would have been impossible or, at the least, impractical. In the 1980s, the mainframe computing power of the 1960s, which was available primarily in large universities, government agencies, and very large companies, was transformed into something that could go on nearly every businessperson’s desktop. The personal computer (PC) and simple operat- ing systems like DOS and eventually Windows revolutionized many business applications by mak- ing computing power relatively inexpensive and convenient. Today, the widespread usage of the Internet is perhaps the single biggest change agent in business research. Since most readers are no doubt experienced in using the Internet, we high- light a few terms and facts about the Internet that are especially useful in understanding business research.

In the following pages we discuss the World Wide Web and how to use the Internet for research. However, keep in mind that the Internet is constantly changing. The description of the Internet, especially home page addresses, may be out of date by the time this book is published. Be aware that the Internet of today will not be the Internet of tomorrow.

What Exactly Is the Internet? The Internet is a worldwide network of comput- ers that allows users access to data, information, and feedback from distant sources. It functions as the

electronic data interchange (EDI)

Type of exchange that occurs when one company’s computer system is integrated with another company’s system.

Electronic data storage is revolutionizing some research tasks. Thousands of television commercials and employee training films are available for analysis by just searching the right electronic video database.

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Internet

A worldwide network of computers that allows users access to information from distant sources.

CHAPTER 2 Information Systems and Knowledge Management • 29

world’s largest public library, providing access to a seemingly endless range of data. Many people believe the Internet is the most important communications medium since television.

The Internet began in the 1960s as an experimental connection between computers at Stanford University, the University of California at Santa Barbara, the University of California at Los Angeles, and the University of Utah, in conjunction with the Department of Defense.12 The Department of Defense was involved because it wanted to develop a communications network that could survive nuclear war. The Internet gradually grew into a nationwide network of connected computers, and now it is a worldwide network often referred to as the “information superhighway.”

The Internet has no central computer; instead, each message sent bears an address code that lets a sender forward a message to a desired destination from any computer linked to the Net. Many benefits of the Internet arise because the Internet is a collection of thousands of small networks, both domestic and foreign, rather than a single computer operation.

A domain is typically a company name, institutional name, or organizational name associated with a host computer. A host is where the content for a particular website physically resides and is accessed. For example, Forbes magazine’s Internet edition is located at http://forbes.com. The “com” indicates this domain is a commercial site. Educational sites end in “edu”—Louisiana Tech can be reached at http://www.latech.edu and Bradley University can be accessed at http://www.bradley .edu. The United States Marine Corps can be found at http://www.marines.mil (the “mil” indi- cating military) and many government sites, such as the U.S. House of Representatives, end with “gov,” as in http://www.house.gov and http://www.census.gov. Many nonprofit organizations end in “org” or “net” as in http://www.ams-web.org, the web home for the Academy of Marketing Science. Web addresses outside the United States often end in abbreviations for their country such as “ca,” “de,” or “uk” for Canada, Germany (Deutschland), and the United Kingdom, respectively.

How Is the Internet Useful in Research? The Internet is useful to researchers in many ways. In fact, more and more applications become known as the technology grows and is adopted by more and more users. The Internet is particu- larly useful as a source for accessing available data and as a way of collecting data.

Accessing Available Data The Internet allows instantaneous and effortless access to a great deal of information. Noncom- mercial and commercial organizations make a wealth of data and other resources available on the Internet. For example, the U.S. Library of Congress provides the full text of all versions of House and Senate legislation and the full text of the Congressional Record. The Internal Revenue Service makes it possible to obtain information and download a variety of income tax forms. Cengage Learning (www.cengage.com) and its college divisions (www.cengage.com/highered/) have online directories that allow college professors to access information about the company and its textbooks. The Gale Research Database provides basic statistics and news stories on literally thousands of com- panies worldwide. Thus, information that formerly took a great deal of time and effort to obtain is now available with a few clicks. Further, since it can often be electronically downloaded or copied, it isn’t necessary for a person to transcribe the data. Therefore, it is available in a more error-free form.

Collecting Data The Internet is also revolutionizing the way researchers collect data. Later in this text, we discuss in more detail the use of web-based surveys. In short, questionnaires can be posted on a web- site and respondents can be invited to go to the particular URL and participate in the survey. This cuts down on the expense associated with traditional mail surveys and also reduces error since the data can be automatically recorded rather than transcribed from a paper form into an electronic format.

Furthermore, when a consumer uses the World Wide Web, his or her usage leaves a record that can be traced and observed. For instance, Zappos.com can determine how many pages were visited at their shopping site before a purchase was made. They can see if products were abandoned

host

Where the content for a particular website physically

resides and is accessed.

30 • PART ONE Introduction

in the “virtual shopping cart” without a purchase being made. Online auctions provide another mechanism to track consumers’ behavior. Prototype products can be offered for sale in an online auction to help assist with product design, forecasting demand, and setting an appropriate price.13

Navigating the Internet The World Wide Web (WWW) refers specifically to that portion of the Internet made up of serv- ers that support a retrieval system that organizes information into documents called web pages. World Wide Web documents, which may include graphic images, video clips, and sound clips, are formatted in programming languages, such as HTML (HyperText Markup Language) and XML (Extensible Markup Language) that allow for displaying, linking, and sharing of information on the Internet.

Parties that furnish information on the World Wide Web are called content providers. Content providers maintain websites. A website consists of one or more web pages with related informa- tion about a particular topic; for example, Bradley University’s website includes pages about its mission, courses, athletics, admissions, and faculty (see http://www.bradley.edu). The introductory page or opening screen is called the home page because it provides basic information about the purpose of the document along with a menu of selections or links that lead to other screens with more specific information. Thus, each page can have connections, or hyperlinks, to other pages, which may be on any computer connected to the Internet. People using the World Wide Web may be viewing information that is stored on a host computer or on a machine halfway around the world.

Most web browsers also allow the user to enter a uniform resource locator (URL) into the pro- gram. The URL is really just a website address that web browsers recognize. Many websites allow any user or visitor access without previous approval. However, many commercial sites require that the user have a valid account and password before access is granted.

One of the most basic research tools available via the Internet is a search engine. A search engine is a computerized directory that allows anyone to search the World Wide Web for infor- mation based on a keyword search. A keyword search takes place as the search engine searches through millions of web pages for documents containing the keywords. Some of the most com- prehensive and accurate search engines are:

Yahoo! http://www.yahoo.com Google http://www.google.com Hotbot http://www.hotbot.com Go.com http://www.go.com Excite http://www.excite.com Bing! http://www.bing.com Ask.com http://www.ask.com WebCrawler http://www.webcrawler.com

Google revolutionized search engines by changing the way the search was actually conducted. It searches based on a mathematical theory known as graph theory.14 Google greatly improved the accuracy and usefulness of the search results obtained from a keyword search. In fact, “google” is now included as a word in many dictionaries, meaning “to search for information on the World Wide Web.” Exhibit 2.5 illustrates the Google interface and expanded Google options. For instance, if one clicks on Google Scholar, a search of citations for a particular author or basic research papers on any given topic indicated by the keywords can be performed.

Interactive Media and Environmental Scanning The Internet is an interactive medium because users click commands and often get customized responses. So the user and equipment can have a continuing conversation. Two or more individu- als who communicate one-to-one via e-mail using an Internet service provider are also using interactive media. So are individuals who communicate with many senders and receivers via bul- letin boards or chat rooms. Because of its vastness, the Internet is an especially useful source for

World Wide Web (WWW)

A portion of the Internet that is a system of computer servers that organize information into documents called web pages.

content providers

Parties that furnish information on the World Wide Web.

uniform resource locator (URL)

A website address that web browsers recognize.

search engine

A computerized directory that allows anyone to search the World Wide Web for information using a keyword search.

keyword search

Takes place as the search engine searches through millions of web pages for documents containing the keywords.

‘‘The Net is 10.5 on the Richter scale of economic change.’’—NICHOLAS NEGROPONTE

interactive medium

A medium, such as the Internet, that a person can use to communicate with and interact with other users.

CHAPTER 2 Information Systems and Knowledge Management • 31

scanning many types of environmental changes. Environmental scanning entails all information gathering designed to detect changes in the external operating environment of the firm. These things are usually beyond the control of the firm, but they still can have a significant impact on firm performance.

Ford Motor Company maintains an Internet-based relationship marketing program that, among other things, helps the automaker scan its environment using the Internet. Its dealer web- site creates a centralized communication service linking dealers via an Internet connection. Its buyer website allows prospective buyers to visit a virtual showroom and to get price quotes and financial information. Its owner website allows an owner who registers and supplies pertinent vehicle information to get free e-mail and other ownership perks. A perk might be a free Hertz upgrade or an autographed photo of one of the Ford-sponsored NASCAR drivers. In return, Ford collects data at all levels, which allow managers to scan for trends and apply what they learn at a local level.

Information Technology Data and information can be delivered to consumers or other end users via either pull technology or push technology. Conventionally, consumers request information from a web page and the browser then determines a response. Thus, the consumer is essentially asking for the data. In this case, it is said to be pulled through the channel. The opposite of pull is push. Push technology sends data to a user’s computer without a request being made. In other words, software is used to guess what information might be interesting to consumers based on the pattern of previous responses.

Smart information delivery (known by a variety of technical names, including push phase technology) allows a website, such as the Yahoo! portal, to become a one-on-one medium for each individual user. Today’s information technology uses “smart agents” or “intelligent agents” to deliver

EXHIBIT 2.5

The Google Web Interface

environmental scanning

Entails all information gathering designed to detect

changes in the external operating environment of

the firm.

pull technology

Consumers request information from a web page and the

browser then determines a response; the consumer is

essentially asking for the data.

push technology

Sends data to a user’s computer without a request

being made; software is used to guess what information

might be interesting to consumers based on the

pattern of previous responses.

Source: Used with permission from Google, Inc. Google brand features are trademarks of Google Technology, Inc.

32 • PART ONE Introduction

customized content to a viewer’s desktop. Smart agent software is capable of learning an Internet user’s preferences and automatically searching out information and distributing the information to a user’s computer. My Yahoo! and My Excite are portal services that personalize web pages. Users can get stock quotes relevant to their portfolios, news about favorite sports teams, local weather, and other personalized information. Users can customize the sections of the service they want delivered. With push technology, pertinent content is delivered to the viewer’s desktop without the user having to do the searching.

Cookies, in computer terminology, are small computer files that record a user’s web usage his- tory. If a person looks up a weather report by keying a zip code into a personalized web page, the fact that the user visited the website and the zip code entered are recorded in the cookie. This is a clue that tells where the person lives (or maybe where he or she may be planning to visit). Websites can then direct information to that consumer based on information in the cookie. So, someone in Hattiesburg, Mississippi, may receive pop-up ads for restaurants in Hattiesburg. Information tech- nology is having a major impact on the nature of business research. We will explore this topic in several places throughout this book.

Intranets An intranet is a company’s private data network that uses Internet standards and technology.15 The information on an intranet—data, graphics, video, and voice—is available only inside the organiza- tion or to those individuals whom the organization deems as appropriate participants. Thus, a key difference between the Internet and an intranet is that security software programs, or “firewalls,” are installed to limit access to only those employees authorized to enter the system. Intranets then serve as secure knowledge portals that contain substantial amounts of organizational memory and can integrate it with information from outside sources. For example, Caterpillar has an intranet that includes their knowledge network, a portal that provides Caterpillar employees and dealership personnel with a vast array of information about the company and its product offering. The chal- lenge in designing an intranet is making sure that it is capable of delivering relevant data to decision makers. Research suggests that relevance is a key in getting knowledge workers to actually make use of company intranets.16

An intranet can be extended to include key consumers as a source of valuable research. Their participation in an intranet can lead to new prod- uct developments. Texas Instruments successfully established an intranet that integrated communications between customers and researchers, leading to the introduction of new calculators and modification of exist- ing calculators.17 An intranet lets authorized users, possibly including key customers, look at product drawings, employee newsletters, sales figures, and other kinds of company information.

Internet2 As we mentioned earlier, information technology changes rapidly. As sophisticated as the Internet and intranets are today, new technologies, such as Internet2, will dramatically enhance researchers’ ability to answer business problems in the future.

Internet2 (http://www.internet2.edu/) is a collaborative effort involv- ing about 250 universities, government entities (including the military), and corporate organizations. The project hopes to recreate some of the cooperative spirit that created the Internet originally. Internet2 users are limited to those involved with the affiliate organizations. The hope is to create a faster, more powerful Internet by providing multimodal access, employing more wireless technologies, and building in global trading mechanisms. Internet2 began as a research tool for the universities and organizations involved in its development.18

smart agent software

Software capable of learning an Internet user’s preferences and automatically searching out information in selected websites and then distributing it.

Small computer files that a content provider can save onto the computer of someone who visits its website.

intranet

A company’s private data network that uses Internet standards and technology.

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The iPhone offers one example of how modern technology makes it possible to store and deliver information, providing cellular communication and contacts, e-mail capabilities, calendar functions, GPS mapping, and music downloads, among a host of other capabilities.

CHAPTER 2 Information Systems and Knowledge Management • 33

SUMMARY

1. Know and distinguish between the concepts of data, information, and intelligence. Increased global competition and technological advances in interactive media have spurred development of global information systems. A global information system is an organized collection of computer hardware, software, data, and personnel designed to capture, store, update, manipu- late, analyze, and immediately display information about worldwide business activity.

From a research perspective, there is a difference between data, information, and intelligence. Data are simply facts or recorded measures of certain phenomena (things); information is data for- matted (structured) to support decision making or define the relationship between two facts. Busi- ness intelligence is the subset of data and information that actually has some explanatory power enabling effective decisions to be made.

2. Understand the four characteristics that describe data. The usefulness of data to manage- ment can be described based on four characteristics: relevance, quality, timeliness, and complete- ness. Relevant data have the characteristic of pertinence to the situation at hand. The information is useful. The quality of information is the degree to which data represent the true situation. High-quality data are accurate, valid, and reliable. High-quality data represent reality faithfully and present a good picture of reality. Timely information is obtained at the right time. Computer- ized information systems can record events and present information as a transaction takes place, improving timeliness. Complete information is the right quantity of information. Managers must have sufficient information to relate all aspects of their decisions together.

3. Know the purpose of research in assisting business operations. A computer-based decision support system helps decision makers confront problems through direct interactions with databases and analytical models. A DSS stores data and transforms them into organized information that is easily accessible to managers.

4. Know what a decision support system is and does. A database is a collection of raw data arranged logically and organized in a form that can be stored and processed by a computer. Busi- ness data come from four major sources: internal records, proprietary business research, busi- ness intelligence, and outside vendors and external distributors. Each source can provide valuable input. Because most companies compile and store many different databases, they often develop data warehousing systems. Data warehousing is a process that allows important day-to-day opera- tional data to be stored and organized for simplified access. More specifically, a data warehouse is the multitiered computer storehouse of current and historical data. Data warehouse management requires that the detailed data from operational systems be extracted, transformed, and stored (warehoused) so that the various database tables from both inside and outside the company are consistent. All of this feeds into the decision support system that automates or assists business decision making.

Numerous database search and retrieval systems are available by subscription or in libraries. Computer-assisted database searching has made the collection of external data faster and easier. Managers refer to many different types of databases.

Although personal computers work independently, they can connect to other computers in networks to share data and software. Electronic data interchange (EDI) allows one company’s computer system to join directly to another company’s system.

5. Recognize the major categories of databases. The Internet is a worldwide network of com- puters that allows users access to information and documents from distant sources. It is a combi- nation of a worldwide communication system and the world’s largest public library. The World Wide Web is a system of thousands of interconnected pages or documents that can be easily accessed with web browsers and search engines.

An intranet is a company’s private data network that uses Internet standards and technology. The information on an intranet—data, graphics, video, and voice—is available only inside the organization. Thus, a key difference between the Internet and an intranet is that “firewalls,” or security software programs, are installed to limit access to only those employees authorized to enter the system.

A company uses Internet features to build its own intranet. Groupware and other technol- ogy can facilitate the transfer of data, information, and knowledge. In organizations that practice knowledge management, intranets function to make the knowledge of company experts more accessible throughout their organizations.

34 Part 1: Introduction

KEY TERMS AND CONCEPTS business intelligence, 18 content providers, 31 cookies, 33 customer relationship management

(CRM), 23 data quality, 20 data warehouse, 24 data warehousing, 24 data wholesalers, 27 data, 18 database, 24 decision support system (DSS), 23

electronic data interchange (EDI), 29

environmental scanning, 32 global information system, 22 host, 30 information completeness, 20 information, 18 interactive medium, 31 Internet, 29 intranet, 33 keyword search, 31 knowledge management, 21

knowledge, 21 proprietary business research, 24 pull technology, 32 push technology, 32 relevance, 19 scanner data, 26 search engine, 31 smart agent software, 33 timeliness, 20 uniform resource locator

(URL), 31 World Wide Web (WWW), 31

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is the difference between data, information, and

intelligence? 2. What are the characteristics of useful information? 3. What is the key question distinguishing relevant data from

irrelevant data? 4. Define knowledge management. What is its purpose within an

organization? 5. What types of databases might be found in the following

organizations? a. Holiday Inn b. A major university’s athletic department c. Anheuser-Busch

6. What type of operational questions could a delivery firm like FedEx expect to automate with the company’s decision support system?

7. What makes a decision support system successful? 8. What is data warehousing? 9. ’NET How does data warehousing assist decision making?

Visit http://www.kbb.com. While there, choose two cars that you might consider buying and compare them. Which do you like the best? What would you do now? What are at least three pieces of data that should be stored in a data

warehouse somewhere based on your interaction with Kelley Blue Book?

10. ’NET Give three examples of computerized databases that are available at your college or university library.

11. What is the difference between the Internet and an intranet? 12. Suppose a retail firm is interested in studying the effect of light-

ing on customer purchase behavior. Which of the following pieces of information is the least relevant and why? a. Amount of natural light in the store b. The compensation system for store salespeople c. The color of the walls in the store d. The type of lighting: fluorescent or incandescent

13. ’NET Imagine the data collected by eBay each day. List at least five types of data that are collected through its daily operations. Describe each in terms of it illustrating data, information, or intelligence. Make sure you list at least one of each.

14. How could New Balance, a maker of athletic shoes, use RFID technology to collect data?

15. ’NET The Spider’s Apprentice is a website that provides many useful tips about using search engines. Go to http://www .monash.com/spidap.html, then click on Search Engine FAQ to learn the ins and outs of search engines.

RESEARCH ACTIVITIES 1. ’NET To learn more about data warehousing, go to http://

www.datawarehousing.org. 2. ’NET Use the Internet to see if you can find information to

answer the following questions:

a. What is the weather in Angers, France, today? b. What are four restaurants in the French Quarter in New

Orleans? c. What is the population of Brazil?

Chapter 2: Information Systems and Knowledge Management 35

From his office window overlooking the main floor of the Harvard Cooperative Society, CEO Jerry Murphy can glance down and see customers

shopping.19 They make their way through the narrow aisles of the crowded department store, picking up a sweatshirt here, trying on a baseball cap there, checking out the endless array of merchandise that bears the Harvard University insignia.

Watching Murphy, you can well imagine the Co-op’s founders, who started the store in 1882, peering through the tiny window- panes to keep an eye on the shop floor. Was the Harvard Square store attracting steady traffic? Were the college students buying enough books and supplies for the Co-op to make a profit? Back then, it was tough to answer those questions precisely. The owners had to watch and wait, relying only on their gut feelings to know how things were going from minute to minute.

Now, more than a hundred years later, Murphy can tell you, down to the last stock-keeping unit, how he’s doing at any given moment. His window on the business is the PC that sits on his desk. All day

long it delivers up-to-the-minute, easy-to-read electronic reports on what’s selling and what’s not, which items are running low in inven- tory and which have fallen short of forecast. In a matter of seconds, the computer can report gross margins for any product or sup- plier, and Murphy can decide whether the margins are fat enough to justify keeping the supplier or product on board. “We were in the 1800s, and we had to move ahead,” he says of the $55 million business.

Questions 1. What is a decision support system? What advantages does a deci-

sion support system have for a business like the Harvard Coop- erative Society?

2. How would the decision support system of a business like the Harvard Cooperative Society differ from that of a major corporation?

3. Briefly outline the components of the Harvard Cooperative Society’s decision support system.

Harvard Cooperative Society

CASE 2.1

36 Part 1: Introduction

Chapter Vignette:

Theory and Practice

After studying this chapter, you should be able to

1. Define the meaning of theory

2. Understand the goals of theory

3. Understand the terms concepts, propositions, variables, and hypotheses

4. Discuss how theories are developed

5. Understand the scientific method

LEARNING OUTCOMES

Theory Building

C H A P T E R

W hat if you went home tonight and turned on the light switch and nothing happened? Most of us would immedi- ately start seeking a logical explanation:

“Is the bulb burnt out?” “Did my roommate forget to pay the electric bill?” “Is the electricity out?” “Did a fuse blow?” These are common thoughts that would race through our minds. The order would probably depend on our past experi- ence and we would try to determine the cause through a logical thought sequence. Attribution theory is one framework that helps us explain the world and determine the cause of an event (the light bulb not working) or behavior (why my girlfriend is mad at me). Simply put, this theory helps us make sense of events by providing a systematic method to assess and evaluate why things occur. Attribution theory is just one of many theoretical models that are useful to business researchers.

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Introduction The purpose of science concerns the expansion of knowledge and the search for truth. Theory building is the means by which basic researchers hope to achieve this purpose.

Students sometimes think their classes or course material are “too theoretical” or lacking “practical application.” However, this should certainly not be the case. Theories are simply gener alizations that help us better understand reality. Furthermore, theories allow us to understand the logic behind things we observe. If a theory does not hold true in practice, then that theory holds no value. This chapter will provide a fundamental knowledge of theory, theory development, and some terminology regarding theory necessary for business researchers.

What Is a Theory? Like all abstractions, the word “theory” has been used in many different ways, in many different

contexts, at times so broadly as to include almost all descriptive statements about a class of phenomena, and at other times so narrowly as to exclude everything but a series of terms and their relationships that satisfies certain logical requirements.1

A theory consists of a coherent set of general propositions that offer an explanation of some phe nomena by describing the way other things correspond to this phenomena. So, a theory is a formal, testable explanation of some events that includes explanations of how things relate to one another. A simpler way to think of a theory is to consider it a model of reality, a simplification that helps us better understand the logic and relationships among different factors.

You are probably already familiar with many business theories. For example, one of the most basic theories in the business world is the theory of supply and demand commonly discussed in economics classes. The differing amount of a product or service that a producer is willing to pro vide at different prices is termed supply. The quantity of the good or service that customers are willing to purchase at differing prices is termed demand. The law of demand states that demand for a product or service will decrease as price increases. While we accept this as common knowledge today, this is actually a theory largely attributed to Alfred Marshall, who recognized that customers played a role in the determination of prices, rather than simply relying on the classical approach of determining prices solely on costs.

A theory can be built through a process of reviewing previous findings of similar studies, simple logical deduction, and/or knowledge of applicable theoretical areas. For example, if a web designer is trying to decide what color background is most effective in increasing online sales, he may first consult previous studies examining the effects of color on package design and retail store design. He may also find theories that deal with the wavelength of different colors, affective response to colors, or those that explain retail atmospherics. This may lead to the specific prediction that blue is the most effective background color for a website.2

While it may seem that theory is only relevant to academic or basic business research, theory plays a role in understanding practical research as well. Before setting research objectives, the researcher must be able to describe the business situation in some coherent way. Without this type of explanation, the researcher would have little idea of where to start. Ultimately, the logical explanation helps the researcher know what variables need to be included in the study and how they may relate to one another. The Research Snapshot “Social Network Theory” illustrates how theory is applied to virtually every situation, including social networking.

What Are the Goals of Theory? Suppose a researcher investigating business phenomena wants to know what caused the financial crisis. Another person wants to know if organizational structure influences leadership style. Both of these individuals want to gain a better understanding of the environment and be able to predict behavior; to be able to say that if we take a particular course of action we can expect a specific out come to occur. These two issues—understanding and predicting—are the two purposes of theory.3

theory

A formal, logical explanation of some events that includes

predictions of how things relate to one another.

‘‘There is nothing so practical as a good theory.’’ —KURT LEWIN

‘‘Theories are nets cast to catch what we call “the world”: to rationalize, to explain, and to master it. We endeavour to make the mesh ever finer and finer.’’—KARL R. PoPPER,

THE LOGIC OF SCIENTIFIC DISCOVERY

38 • PART ONE Introduction

Accomplishing the first goal allows the theorist to gain an understanding of the relationship among various phenomena. For example, a financial advisor may believe, or theorize, that older investors tend to be more interested in investment income than younger investors. This theory, once verified, would then allow her to predict the importance of expected dividend yield based on the age of her customer. Thus a theory enables us to predict the behavior or characteristics of one phenomenon from the knowledge of another phenomenon. The value of understanding and anticipating future conditions in the environment or in an organization should be obvious. In most situations, of course, understanding and prediction go hand in hand. To predict phenomena, we must have an explanation of why variables behave as they do. Theories provide these explanations.

Research Concepts, Constructs, Propositions, Variables, and Hypotheses Theory development is essentially a process of describing phe nomena at increasingly higher levels of abstraction. In other words, as business researchers, we need to be able to think of things in a very abstract manner, but eventually link these abstract concepts to observable reality. To understand theory and the business research process, it will be useful to know different terminology and how these terms relate.

Research Concepts and Constructs A concept or construct is a generalized idea about a class of objects, attributes, occurrences, or processes that has been given a name. If you, as an organizational theorist, were to describe phenomena such as supervisory behavior or risk aversion, you would categorize empirical events or real things into concepts. Concepts are the building blocks of theory. In organizational theory, leadership, productivity, and morale are concepts. In the theory of finance, gross national product, risk aversion, and inflation are frequently used concepts. Account ing concepts include assets, liabilities, and depreciation. In marketing, customer satisfaction, market share, and loyalty are important concepts.

SURVEY

THIS! Go online to the Internet survey you completed for the Chapter 1 assign- ment. Please go back and review all the questions included on the survey. Following our discussion of theory in this chapter, you should have a solid foundation in understand ing theory development and the importance of theory in business research. Considering the questions asked in the survey, build a theory about the relationship among at least four questions. How do you think the responses to these questions should relate? Why? Provide a theoretical expla- nation for the relationships you are proposing.

concept (or construct)

A generalized idea about a class of objects that has been given a name; an abstraction of reality that is the basic unit for theory development.

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Concepts abstract reality. That is, concepts express in words various events or objects. Concepts, however, may vary in degree of abstraction. For example, the concept of an asset is an abstract term that may, in the concrete world of reality, refer to a wide variety of things, including a specific punch press machine in a production shop. The abstraction ladder in Exhibit 3.1 indicates that it is possible to discuss concepts at various levels of abstraction. Moving up the ladder of abstraction, the basic concept becomes more general, wider in scope, and less amenable to measurement.

The basic or scientific business researcher operates at two levels: on the abstract level of con cepts (and propositions) and on the empirical level of variables (and hypotheses). At the empirical level, we “experience” reality—that is, we observe, measure, or manipulate objects or events. For example, we commonly use the term job performance, but this is an abstract term that can mean different things to different people or in different situations. To move to the empirical level, we must more clearly define this construct and identify actual measures that we can assess and measure to represent job performance as shown in Exhibit 3.2. In research, we use the term latent construct to refer to a concept that is not directly observable or measurable, but can be estimated through proxy measures.4 Job performance, customer satisfaction, and risk aversion are just three examples of the many latent constructs in business research. While we cannot directly see these latent con structs, we can measure them, and doing so is one of the greatest challenges for business researchers.

Researchers have developed theories about the links and structure of social networks, complete with constructs and propositions about how linkages are formed. Each separate entity (individuals or organizations) is referred to as a node. The relationships among nodes are referred to as ties. When nodes become linked they yield social contacts.

What creates ties? How do the nodes become linked? Many factors have been indentified, including family relationships, friendship, professional association, common interest and activities, sexual relationships, and shared beliefs. When linked together with ties these nodes form a social network.

Network theory is used to examine social networks. The links between and among nodes can be simple or complex. They can be as simple as a single family or so complex as to operate on

a national or international level. The network can play an important role in the suc- cess of an individual, how a family functions, and how a business makes decisions. The value derived from the social network is termed social capital.

While electronic social networking is a relatively recent development, businesses are already examining ways to utilize social networks to spread information, better serve their cus- tomers, and grow profits. The next few years will be exciting as the theory of social networks develops and businesses continue to explore how they can gain social capital from engaging in social networking.

Source: http://www.facebook.com/pages/Social-network-theory/ 112159215470744, accessed August 3, 2011.

Social Network Theory

abstract level

In theory development, the level of knowledge expressing

a concept that exists only as an idea or a quality apart from an

object.

empirical level

Level of knowledge that is verifiable by experience or

observation.

latent construct

A concept that is not directly observable or measurable,

but can be estimated through proxy measures.

RESEARCH SNAPSHOT

EXHIBIT 3.1

A Ladder of Abstraction for Concepts Assets

Plant Machinery

Punch Press

Reality

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ladder of abstraction

organization of concepts in sequence from the most

concrete and individual to the most general.

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If an organizational researcher says, “Older workers prefer different rewards than younger work ers,” two concepts—age of worker and reward preference—are the subjects of this abstract statement. If the researcher wishes to test this relationship, Jason, age 25, Angela, age 42, and Ed, age 62—along with other workers—may be questioned about their preferences for salary, retirement plans, intrin sic job satisfaction, and so forth. Recording their ages and assessing their reward preferences are activities that occur at the empirical level. In this example, we can see that researchers have a much easier time assessing and measuring age than the latent construct of reward preference.

In the end, researchers are concerned with the observable world, or what we shall loosely term reality. Theorists translate their conceptualization of reality into abstract ideas. Thus, theory deals with abstraction. Things are not the essence of theory; ideas are.5 Concepts in isolation are not theo ries. To construct a theory we must explain how concepts relate to other concepts as discussed below.

Research Propositions and Hypotheses As we just mentioned, concepts are the basic units of theory development. However, theo ries require an understanding of the relationship among concepts. Thus, once the concepts of interest have been identified, a researcher is interested in the relationship among these concepts. Propositions are statements concerned with the relationships among concepts. A proposition explains the logical linkage among certain concepts by asserting a universal connection between concepts. For example, we might propose that treating our employees better will make them more loyal employees. This is certainly a logical link between managerial actions and employee reactions, but is quite general and not really testable in its current form.

A hypothesis is a formal statement explaining some outcome. In its simplest form, a hypothesis is a guess. A sales manager may hypothesize that the salespeople who are highest in product know ledge will be the most productive. An advertising manager may hypothesize that if consumers’ atti tudes toward a product change in a positive direction, there will be an increase in consumption of the product. A human resource manager may hypothesize that job candidates with certain majors will be more successful employees.

A hypothesis is a proposition that is empirically testable. In other words, when one states a hypothesis, it should be written in a manner that can be supported or shown to be wrong through an empirical test. For example, using the color of the background for a website discussed previously, the researcher may use theoretical reasoning to develop the following hypothesis:

H1: A website with a blue background will generate more sales than an otherwise identical website with a red background.

We often apply statistics to data to empirically test hypotheses. Empirical testing means that something has been examined against reality using data. The abstract proposition, “Treating our employees better will make them more loyal employees” may be tested empirically with a hypothesis. Exhibit 3.3 shows that the hypothesis, “Increasing retirement benefits will reduce inten tion to leave the organization” is an empirical counterpart of this proposition. Retirement benefits

propositions

Statements explaining the logical linkage among certain concepts by asserting a universal connection between concepts.

hypothesis

Formal statement of an unproven proposition that is empirically testable.

empirical testing

Examining a research hypothesis against reality using data.

Salesperson Job Performance

Abstract Level

Empirical Level Number of Sales Calls Number of Sales Dollar Value of Sales

‘‘Reality is merely an illusion, albeit a very persistent one.’’ —ALBERT EINSTEIN

EXHIBIT 3.2

Concepts Are Abstractions of Reality

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CHAPTER 3 Theory Building • 41

and intention to leave are variables, reflecting the concepts of employee treatment and employee loyalty. When the data are consistent with a hypothesis, we say the hypothesis is supported. When the data are inconsistent with a hypothesis, we say the hypothesis is not supported. We are often tempted to say that we prove a hypothesis when the data conform to the prediction, but this isn’t really true. Because our result is based on statistics, there is always the possibility that our conclusion is wrong. Now, at times we can be very, very confident in our conclusion, but from an absolute perspective, statistics cannot prove a hypothesis is true.

Because variables are at the empirical level, variables can be measured. In this case, retirement benefits might be measured quite easily and precisely (e.g., the actual percentage change in match ing retirement funds), while the latent construct of intention to leave would be more challenging for the researcher. This step is known as operationalizing our variables—the process of identifying the actual measurement scales to assess the variables of interest. We will discussion operationaliza tion in more detail in Chapter 13.

Thus, the scientific inquiry has two basic levels:

. . . the empirical and the abstract, conceptual. The empirical aspect is primarily concerned with the facts of the science as revealed by observation and experiments. The abstract or theoretical aspect, on the other hand, consists in a serious attempt to understand the facts of the science, and to integrate them into a coherent, i.e., a logical, system. From these observations and integrations are derived, directly or indirectly, the basic laws of the science.6

Understanding Theory Exhibit 3.4 is a simplified portrayal of a theory to explain voluntary job turnover—the movement of employees to other organizations. Two concepts—(1) the perceived desirability of movement to another organization and (2) the perceived ease of movement from the present job—are expected to be the primary determinants of intention to quit. This is a proposition. Further, the concept intention to quit is expected to be a necessary condition for the actual voluntary job turnover behavior to occur. This is a second proposition that links concepts together in this theory. In the more elaborate theory, job performance is another concept considered to be the primary deter minant influencing both perceived ease of movement and perceived desirability of movement. Moreover, perceived ease of movement is related to other concepts such as labor market conditions, number of organizations visible to the individual, and personal characteristics. Perceived desirabil ity of movement is influenced by concepts such as equity of pay, job complexity, and participation in decision making. A complete explanation of this theory is not possible; however, this example should help you understand the terminology used by theory builders.

operationalizing

The process of identifying the actual measurement scales to

assess the variables of interest.

Treat Employees Better

More Loyal Employees

Increase Retirement Benefits 5%

Reduce Annual Turnover

Hypothesis

Proposition

Abstract Level

Empirical Level

‘‘If facts conflict with a theory, either the theory must be changed or the facts.’’—BENEDICT SPINoZA

variables

Anything that may assume different numerical values;

the empirical assessment of a concept.

EXHIBIT 3.3

Hypotheses Are the Empirical Counterparts of Propositions

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42 • PART ONE Introduction

Verifying Theory In most scientific situations there are alternative theories to explain certain phenomena. To deter mine which is the better theory, researchers make observations or gather empirical data to verify the theories.

Maslow’s hierarchical theory of motivation offers one explanation of human behavior. Maslow theorizes that individuals will attempt to satisfy physiological needs before selfesteem needs. An alternative view of motivation is provided by Freudian (psychoanalytic) theory, which suggests that unconscious, emotional impulses are the basic influences on behavior. One task of science is to determine if a given theoretical proposition is false or if there are inconsistencies between compet ing theories. Just as records are made to be broken, theories are made to be tested.

It must be possible to demonstrate that a given proposition or theory is false. This may at first glance appear strange. Why “false” rather than “true”? Technically, there may be other untested theories which could account for the results we obtained in our study of a proposition. At the very least, there may be a competing explanation which could be the “real” explanation for a given set of research findings. Thus, we can never be certain that our proposition or theory is the correct one. The scientist can only say, “I have a theory which I have objectively tested with data and the data are consistent with my theory.” If the pos- sibility of proving an idea false or wrong is not inherent in our test of an idea, then we cannot put much faith in the evidence that suggests it to be true. No other evidence was allowed to manifest itself.9

Theory Building You may be wondering, “Where do theories come from?” Although this is not an easy question to answer in a short chapter on theory in business research, we will explore this topic briefly. In this chapter, theory has been explained at the abstract, conceptual level and at the empirical level. Theory generation may occur at either level.

At the abstract, conceptual level, a theory may be developed with deductive reasoning by going from a general statement to a specific assertion. Deductive reasoning is the logical process

deductive reasoning

The logical process of deriving a conclusion about a specific instance based on a known general premise or something known to be true.

‘‘Every genuine test of a theory is an attempt to falsify it, or to refute it.8’’—KARL PoPPER

EXHIBIT 3.4

A Basic Theory Explaining Voluntary Job Turnover7

Job performance Intention to quit

Voluntary job turnover (individual volition)

Labor market conditions, number of organizations,

personal characteristics, and other partial determinants

of ease of movement

Perceived ease of movement (e.g., expectation

of finding alternatives, unsolicited opportunities)

Perceived desirability of movement (e.g., job

satisfaction)

Equity of pay, job complexity, participation in decision making, and other partial

determinants of desirability of movement

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CHAPTER 3 Theory Building • 43

of deriving a conclusion about a specific instance based on a known general premise or something known to be true. For example, while you might occasionally have doubts, we know that all business professors are human beings. If we also know that Barry Babin is a business professor, then we can deduce that Barry Babin is a human being.

At the empirical level, a theory may be developed with inductive reasoning. Inductive reasoning is the logical process of establishing a general proposition on the basis of observation of particular facts. All business professors that have ever been seen are human beings; therefore, all business professors are human beings.

Suppose a stockbroker with 15 years’ experience trading on the New York Stock Exchange repeatedly notices that the price of gold and the price of gold stocks rise whenever there is a hijacking, terrorist bombing, or military skirmish. In other

words, similar patterns occur whenever a certain type of event occurs. The stockbroker may induce from these empirical observations the more general situation that the price of gold is related to political stability. Thus, the stockbroker states a proposition based on his or her experience or spe cific observations: “Gold prices will increase during times of political instability.” The stockbroker has constructed a basic theory.

Over the course of time, theory construction is often the result of a combination of deductive and inductive reasoning. Our experiences lead us to draw conclusions that we then try to verify empirically by using the scientific method.

The Scientific Method The scientific method is a set of prescribed procedures for establishing and connecting theoretical statements about events, for analyzing empirical evidence, and for predicting events yet unknown. It is useful to look at the analytic process of scientific theory building as a series of stages. While there is not complete consensus concerning exact procedures for the scientific method, we suggest seven operations may be viewed as the steps involved in the application of the scientific method:

1. Assessment of relevant existing knowledge of a phenomenon 2. Formulation of concepts and propositions 3. Statement of hypotheses 4. Design of research to test the hypotheses 5. Acquisition of meaningful empirical data 6. Analysis and evaluation of data 7. Proposal of an explanation of the phenomenon and statement of new problems raised by the

research10

An excellent overview of the scientific method is presented in Robert Pirsig’s book Zen and the Art of Motorcycle Maintenance:

Actually I’ve never seen a cycle-maintenance problem complex enough really to require full-scale formal scientific method. Repair problems are not that hard. When I think of formal scientific method an image sometimes comes to mind of an enormous juggernaut, a huge bulldozer—slow, tedious, lumbering, labori- ous, but invincible. It takes twice as long, five times as long, maybe a dozen times as long as informal mechanic’s techniques, but you know in the end you’re going to get it. There’s no fault isolation problem in motorcycle maintenance that can stand up to it. When you’ve hit a really tough one, tried everything, racked your brain and nothing works, and you know that this time Nature has really decided to be difficult, you say, “Okay, Nature, that’s the end of the nice guy,” and you crank up the formal scientific method.

For this you keep a lab notebook. Everything gets written down, formally, so that you know at all times where you are, where you’ve been, where you’re going and where you want to get. In scientific work and electronics technology this is necessary because otherwise the problems get so complex you get lost in

scientific method

A set of prescribed procedures for establishing and connecting

theoretical statements about events, for analyzing empirical evidence, and for

predicting events yet unknown; techniques or procedures used

to analyze empirical evidence in an attempt to confirm or disprove prior conceptions.

inductive reasoning

The logical process of establishing a general

proposition on the basis of observation of particular facts.

Noting a link between changes in gold prices and political instability could be the foundation for a basic theory.

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them and confused and forget what you know and what you don’t know and have to give up. In cycle maintenance things are not that involved, but when confusion starts it’s a good idea to hold it down by making everything formal and exact. Sometimes just the act of writing down the problems straightens out your head as to what they really are.

The logical statements entered into the notebook are broken down into six categories: (1) statement of the problem, (2) hypotheses as to the cause of the problem, (3) experiments designed to test each hypothesis, (4) predicted results of the experiments, (5) observed results of the experiments, and (6) conclusions from the results of the experiments. This is not different from the formal arrangement of many college and high- school lab notebooks but the purpose here is no longer just busywork. The purpose now is precise guidance of thoughts that will fail if they are not accurate.

The real purpose of scientific method is to make sure Nature hasn’t misled you into thinking you know something you don’t actually know. There’s not a mechanic or scientist or technician alive who hasn’t suffered from that one so much that he’s not instinctively on guard. That’s the main reason why so much scientific and mechanical information sounds so dull and so cautious. If you get careless or go romanticizing scientific information, giving it a flourish here and there, Nature will soon make a complete fool out of you. It does it often enough anyway even when you don’t give it opportunities. One must be extremely careful and rigidly logical when dealing with Nature: one logical slip and an entire scientific edifice comes tumbling down. One false deduction about the machine and you can get hung up indefinitely.

In Part One of formal scientific method, which is the statement of the problem, the main skill is in stat- ing absolutely no more than you are positive you know. It is much better to enter a statement “Solve Problem: Why doesn’t cycle work?” which sounds dumb but is correct, than it is to enter a statement “Solve Problem: What is wrong with the electrical system?” when you don’t absolutely know the trouble is in the electrical system. What you should state is “Solve Problem: What is wrong with cycle?” and then state as the first entry of Part Two: “Hypothesis Number One: The trouble is in the electrical system.” You think of as many hypotheses as you can, then you design experiments to test them to see which are true and which are false.

This careful approach to the beginning questions keeps you from taking a major wrong turn which might cause you weeks of extra work or can even hang you up completely. Scientific questions often have a surface appearance of dumbness for this reason. They are asked in order to prevent dumb mistakes later on.

Part Three, that part of formal scientific method called experimentation, is sometimes thought of by romantics as all of science itself because that’s the only part with much visual surface. They see lots of test tubes and bizarre equipment and people running around making discoveries. They do not see the experiment as part of a larger intellectual process and so they often confuse experiments with demonstrations, which look the same. A man conducting a gee-whiz science show with fifty thousand dollars’ worth of Franken- stein equipment is not doing anything scientific if he knows beforehand what the results of his efforts are going to be. A motorcycle mechanic, on the other hand, who honks the horn to see if the battery works is informally conducting a true scientific experiment. He is testing a hypoth- esis by putting the question to nature. The TV scientist who mutters sadly, “The experiment is a failure; we have failed to achieve what we had hoped for,” is suffering mainly from a bad scriptwriter. An experiment is never a failure solely because it fails to achieve predicted results. An experiment is a failure only when it also fails ade- quately to test the hypothesis in ques- tion, when the data it produces don’t prove anything one way or another.

Skill at this point consists of using experiments that test only the hypoth- esis in question, nothing less, noth- ing more. If the horn honks, and the mechanic concludes that the whole elec- trical system is working, he is in deep trouble. He has reached an illogical ©

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A motorcycle mechanic … who honks the horn to see if the battery works is informally conducting a true scientific experiment. He is testing a hypothesis by putting the question to nature.

— Robert M. Pirsig

CHAPTER 3 Theory Building • 45

conclusion. The honking horn only tells him that the battery and horn are working. To design an experiment properly he has to think very rigidly in terms of what directly causes what. This you know from the hierarchy. The horn doesn’t make the cycle go. Neither does the battery, except in a very indirect way. The point at which the electrical system directly causes the engine to fire is at the spark plugs, and if you don’t test here, at the output of the electrical system, you will never really know whether the failure is electrical or not.

To test properly the mechanic removes the plug and lays it against the engine so that the base around the plug is electrically grounded, kicks the starter lever and watches the spark-plug gap for a blue spark. If there isn’t any he can conclude one of two things: (a) there is an electrical failure or (b) his experiment is sloppy. If he is experienced he will try it a few more times, checking connections, trying every way he can think of to get that plug to fire. Then, if he can’t get it to fire, he finally concludes that (a) is correct, there’s an electrical failure, and the experiment is over. He has proved that his hypothesis is correct.

In the final category, conclusions, skill comes in stating no more than the experiment has proved. It hasn’t proved that when he fixes the electrical system the motorcycle will start. There may be other things wrong. But he does know that the motorcycle isn’t going to run until the electrical system is working and he sets up the next formal question: “Solve problem: What is wrong with the electrical system?”

He then sets up hypotheses for these and tests them. By asking the right questions and choosing the right tests and drawing the right conclusions the mechanic works his way down the echelons of the motorcycle hierarchy until he has found the exact specific cause or causes of the engine failure, and then he changes them so that they no longer cause the failure.

An untrained observer will see only physical labor and often get the idea that physical labor is mainly what the mechanic does. Actually the physical labor is the smallest and easiest part of what the mechanic does. By far the greatest part of his work is careful observation and precise thinking. That is why mechan- ics sometimes seem so taciturn and withdrawn when performing tests. They don’t like it when you talk to them because they are concentrating on mental images, hierarchies, and not really looking at you or the physical motorcycle at all. They are using the experiment as part of a program to expand their hierarchy of knowledge of the faulty motorcycle and compare it to the correct hierarchy in their mind. They are looking at underlying form.11

Practical Value of Theories As the above excerpt makes evident, theories allow us to generalize beyond individual facts or isolated situations. Theories provide a framework that can guide managerial strategy by providing insights into general rules of behavior. When different incidents may be theoretically comparable in some way, the scientific knowledge gained from theory development may have practical value. A good theory allows us to generalize beyond individual facts so that general patterns may be under stood and predicted. For this reason it is often said there is nothing so practical as a good theory.

SUMMARY

1. Define the meaning of theory. Theories are simply models designed to help us better under stand reality and to understand the logic behind things we observe. A theory is a formal, logical explanation of some events that includes predictions of how things relate to one another.

2. Understand the goals of theory. There are two primary goals of theory. The first is to under stand the relationships among various phenomena. A theory provides a picture of the linkages among different concepts, allowing us to better comprehend how they affect one another. The second goal is to predict. Once we have an understanding of the relationships among concepts, we can then predict what will happen if we change one factor. For example, if we understand the relationship between advertising expenditures and retail sales, we can then predict the impact of decreasing or increasing our advertising expenditures.

3. Understand the terms concepts, propositions, variables, and hypotheses. A concept or construct is a generalized idea about a class of objects, attributes, occurrences, or processes that has been given a name. Leadership style, employee turnover, and customer satisfaction are all

46 • PART ONE Introduction

concepts. Concepts express in words various events or objects. Propositions are statements con cerned with the relationships among concepts. A proposition explains the logical linkage among certain concepts by asserting a universal connection between concepts: “Leadership style is related to employee turnover.” A hypothesis is a formal statement explaining some outcome regard ing variables of interest. Variables are the empirical reflection of a concept and a hypothesis is a proposition stated in a testable format. So, concepts and propositions are at the abstract level, while variables and hypotheses are at the empirical level.

4. Discuss how theories are developed. A theory can be built through a process of reviewing previous findings of similar studies or knowledge of applicable theoretical areas. A theory may be developed with deductive reasoning by going from a general statement to a specific assertion. Deductive reasoning is the logical process of deriving a conclusion about a specific instance based on a known general premise or something known to be true. Inductive reasoning is the logical process of establishing a general proposition on the basis of observation of particular facts.

5. Understand the scientific method. The scientific method is a set of prescribed procedures for establishing and connecting theoretical statements about events, for analyzing empirical evi dence, and for predicting events yet unknown. It is useful to look at the analytic process of scien tific theory building as a series of stages. We mentioned that seven operations may be viewed as the steps involved in the application of the scientific method: (1) assessment of relevant existing knowledge of a phenomenon, (2) formulation of concepts and propositions, (3) statement of hypotheses, (4) design of research to test the hypotheses, (5) acquisition of meaningful empirical data, (6) analysis and evaluation of data, and (7) proposal of an explanation of the phenomenon and statement of new problems raised by the research. In sum, the scientific method guides us from the abstract nature of concepts and propositions, to the empirical variables and hypotheses, and to the testing and verification of theory.

KEY TERMS AND CONCEPTS abstract level, 40 concept (or construct), 39 deductive reasoning, 43 empirical level, 40 empirical testing, 41

hypothesis, 41 inductive reasoning, 44 ladder of abstraction, 40 latent construct, 40 operationalizing, 42

propositions, 41 scientific method, 44 theory, 38 variables, 42

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What are some theories offered to explain aspects of your field

of business? 2. Reflect on your own social network. How are the nodes in

your social network linked? What social capital do you gain from your social network?

3. How do propositions and hypotheses differ? 4. How do concepts differ from variables? 5. What does the statement, “There is nothing so practical as a

good theory” mean? Do you agree with this statement?

6. The seventeenthcentury Dutch philosopher Benedict Spinoza said, “If the facts conflict with a theory, either the theory must be changed or the facts.” What is the practical meaning of this statement?

7. Compare and contrast deductive logic with inductive logic. Give an example of both.

8. Find another definition of theory. How is the definition you found similar to this book’s definition? How is it different?

RESEARCH ACTIVITIES 1. ‘NET The Chapter Vignette briefly introduced attribution theory.

Do a Web search regarding attribution theory and identify the key characteristics of this theory.

2. ‘NET The MerriamWebster dictionary definition of theory can be found at http://www.merriam-webster.com/dictionary/ theory. What is the definition of theory given at this site? How does it compare to the definition given in this chapter?

3. ‘NET The Logic of Scientific Discovery is an important theoreti cal work. Visit The Karl Popper Web at http://elm.eeng.dcu .ie/~tkpw/ to learn about its author and his work.

Chapter 3: Theory Building 47

C H A P T E R

L ike most global companies, Hewlett-Packard (HP) must continue to innovate, develop new technology-based products and services, and engage end users, other businesses, and even

governmental institutions across the globe. And, it must do this in a continuously changing environment, with time pres- sures and other competitors seeking to exploit technology advances that they bring to these same customers. Finally, HP is hiring new sales staff, and changing their market territories, on a continuous basis. Business opportunities in informa- tion technology are hard-fought, and hard-won. How can HP help their sales staff with data and analyses to keep them up to speed with new companies, new territories, and new technologies?

The solution to HP’s data and analysis support problems was found with Hoovers, a division of the Dun & Bradstreet Corporation. Hoover’s provides detailed company infor- mation, including industry data and news, to HP’s sales staff in a real-time format. Hoover’s assists with account planning and engagement, executive information, and up-to-the-minute industry information that HP sales reps can use to quickly and efficiently engage existing and new customers around the globe. In some instances, Hoover’s

is an integral part of HP’s customer relationship manage- ment (CRM) strategy, with “data plug-in” capabilities that give sales reps real-time information to assist them in their decision-making.

Through the use of Hoover’s data and analysis tools, HP’s sales staff can now help manage their sales contacts in the intensely complicated and time-intensive information technol- ogy environment. Clearly, Hoovers helps HP get (and keep) up to speed.1

After studying this chapter, you should be able to

1. Define decision making and understand the role research plays in making decisions

2. Classify business research as either exploratory research, descriptive research, or causal research

3. List the major phases of the research process and the steps within each

4. Explain the difference between a research project and a research program

LEARNING OUTCOMES

The Business Research Process An Overview

Chapter Vignette:

Getting (and Keeping) Up to Speed: Hoover’s Helps HP

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Source: “Hewlett-Packard Makes Hoover’s Part of Global CRM Strategy and End-to-End Sales Process” (http://www.hoovers.com/about/case-studies/ 100000292-1.html).

Introduction This chapter focuses on the relationship between business research and managerial decision making. Business success is determined directly by the quality of decisions made by key personnel. Researchers contribute to decision making in several key ways. These include:

1. Helping to better define the current situation 2. Defining the firm—determining how consumers, competitors, and employees view the firm 3. Providing ideas for enhancing current business practices 4. Identifying new strategic directions 5. Testing ideas that will assist in implementing business strategies for the firm 6. Examining how correct a certain business theory is in a given situation

The chapter introduces the types of research that allow researchers to provide input to key decision makers. Causality and the conditions for establishing causality are presented. Last but not least, the chapter discusses stages in the business research process.

Decision Making Businesses, such as Hewlett-Packard, face decisions that shape the future of the organization, its employees, and its customers. In each case, the decisions are brought about as the firm either seeks to capitalize on some opportunity or to reduce any potential negative impacts related to some business problem. A business opportunity is a situation that makes some potential competitive advantage possible. The discovery of some underserved segment presents such an opportunity. For example, eBay capitalized on a business opportunity presented by technological advances to do much the same thing that is done at a garage sale but on a very, very large scale.

A business problem is a situation that makes some significant negative consequence more likely. A natural disaster can present a problem for many firms as they face potential loss of prop- erty and personnel and the possibility that their operations, and therefore their revenue, will be interrupted. Most business problems, however, are not nearly as obvious. In fact, many are not easily observable. Instead, problems are commonly inferred from symptoms, which are observable cues that serve as a signal of a problem because they are caused by that problem. An increase in employee turnover is generally only a symptom of a business problem, rather than the problem itself. Research may help identify what is causing this symptom so that decision makers can actu- ally attack the problem, not just the symptom. Patients don’t usually go to the doctor and point out their problem (such as an ulcer). Instead, they point out the symptoms (upset stomach) they are experiencing. Similarly, decision makers usually hear about symptoms and often need help from research to identify and attack problems. Whether facing an opportunity or a problem, businesses need quality information to deal effectively with these situations.

Formally defined, decision making is the process of developing and deciding among alternative ways of resolving a problem or choosing from among alternative opportunities. A decision maker must recog- nize the nature of the problem or opportunity, identify how much information is currently available and how reliable it is, and determine what additional information is needed to better deal with the situation. Every decision-making situation can be classified based on whether it best represents a problem or an opportunity and where the situation falls on a continuum from absolute ambiguity to complete certainty.

Certainty Complete certainty means that the decision maker has all information needed to make an optimal decision. This includes the exact nature of the business problem or opportunity. For example, an advertising agency may need to know the demographic characteristics of subscribers to magazines in which it may place a client’s advertisements. The agency knows exactly what information it needs and where to find the information. If a manager is completely certain about both the prob- lem or opportunity and future outcomes, then research may not be needed at all. However, perfect certainty, especially about the future, is rare.

business opportunity

A situation that makes some potential competitive advantage possible.

business problem

A situation that makes some significant negative consequence more likely.

symptoms

Observable cues that serve as a signal of a problem because they are caused by that problem.

decision making

The process of developing and deciding among alternative ways of resolving a problem or choosing from among alternative opportunities.

CHAPTER 4 The Business Research Process: An Overview • 49

Uncertainty Uncertainty means that the manager grasps the general nature of desired objectives, but the information about alternatives is incomplete. Predictions about forces that shape future events are educated guesses. Under conditions of uncertainty, effective managers recognize that spending additional time to gather data that clarify the nature of a decision is needed. For instance, a firm needing operating capital may consider an initial public offering, but is not certain of demand for the stock or how to establish the price of the IPO. Busi- ness decisions generally involve uncertainty, particularly when a company is seeking new opportunities.

Ambiguity Ambiguity means that the nature of the problem itself is unclear. Objectives are vague and decision alternatives are difficult to define. This is by far the most difficult decision situation, but perhaps the most common.

Managers face a variety of problems and decisions. Complete certainty and predictable future outcomes may make business research a waste of time. However, under conditions of uncertainty or ambiguity, business research becomes more attractive to the decision makers. Decisions also vary in terms of importance, meaning that some may have great impact on the welfare of the firm and others may have negligible impact. The more important, ambiguous, or uncertain a situation is, the more likely it is that additional time must be spent on business research.

Problems and Opportunities Exhibit 4.1 depicts decision situations char- acterized by the nature of the decision and the degree of ambiguity.2 Under problem- focused decision making and conditions of high ambiguity, symptoms may not clearly point to some problem. Indeed, they may be quite vague or subtle, indicating only small deviations from normal conditions. For instance, a fast-food restaurant may be experiencing small changes in the sales of its individual products, but no change in overall sales. Such a symptom may not easily point to a problem such as a change in con- sumer tastes. As ambiguity is lessened, the symptoms are clearer and are better indica- tors of a problem. A large and sudden drop in overall sales may suggest the problem that

Review the online sur- vey we are using for this course. Based on the data that the survey gathers, what business problems or opportunities do you feel can be addressed from the information? Specify at least three research questions that can be answered by the information gathered by this survey. Do you think this survey is most representative of an exploratory research, descriptive research, or causal research design? Justify your answer.

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Can you identify symptoms that may indicate problems for these businesses? What business problems might they signify?

SURVEY

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the restaurant’s menu does not fare well compared to competitors’ menus. Thus, a menu change may be in order. However, it is also possible the drop in sales is due to new competi- tion, or a competitor’s price drop or new promotional campaign. Thus, research is needed to clarify the situation.

Similarly, in opportunity-oriented research, ambiguity is characterized by environmental trends that do not suggest a clear direction. As the trends become larger and clearer, they are more diagnostic, meaning they point more clearly to a single opportunity.

Types of Business Research Business research is undertaken to reduce uncertainty and focus decision making. In more ambiguous circumstances, management may be totally unaware of a business problem. Alter- natively, someone may be scanning the environment for opportunities. For example, an entre- preneur may have a personal interest in softball and baseball. She is interested in converting her hobby into a profitable business venture and hits on the idea of establishing an indoor softball and baseball training facility and instructional center. However, the demand for such a business is unknown. Even if there is sufficient demand, she is not sure of the best location, actual services offered, desired hours of operation, and so forth. Some preliminary research is necessary to gain insights into the nature of such a situation. Without it, the situation may remain too ambigu- ous to make more than a seat-of-the-pants decision. In this situation, business research is almost certainly needed.

EXHIBIT 4.1

Describing Decision-Making Situations

Opportunity

Problem

Symptoms exist, but are subtle and few, making problem identification difficult

Trends seem evident but do not point clearly to a single best opportunity

Discovery Orientation

Many noticable symptoms pointing to a single question

Trends more obviously point to a single best opportunity

Confirmatory Orientation

Complete Ambiguity Uncertainty

Complete Certainty

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CHAPTER 4 The Business Research Process: An Overview • 51

Has the Pillsbury Doughboy ever changed? How old should the Brawny (paper towel) man be? What should the M&Ms characters be named? These questions all have many possible answers. In truth, a lot of research goes into answering these kinds of questions. It often begins with exploratory research. For instance, focus groups involving female consumers revealed a considerable amount of intimate discussion about the Brawny man. Thus, it seemed that a sexy Brawny man would yield a better response than a humorous or intelligent Brawny man.

Mr. Peanut, the icon for Planters Peanuts, has actually changed very little since his introduction in the 1920s. He looks good for his age! Again, exploratory research suggests generally positive comments about Mr. Peanut, so only minor changes in the color scheme have been introduced. A few years ago, exploratory research led to some further tests of a Mr. Peanut in Bermuda shorts, but the tests proved overwhelmingly negative, sending Planters back to a more original peanut.

Similarly, explor- atory research asked a few consumers for their reactions to the Mars M&Ms characters. Mars was interested in discovering names for the characters. They found that most con- sumers simply referred to them by their colors. This piece of information became useful in shaping future research and business strategy.

Sources: Voight, Joan, “Mascot Makeover: The Risky Business of Tampering with Brand Icons,” Adweek (July 7, 2003), 20–26; Elliot, Stuart, “Updating a Venerable Character, or Tarnishing a Sterling Reputation?” The New York Times (March 19, 2004), C5; “Advertising Mascots—People,” TV Acres, http://www.tvacres.com/admascots_brawny.htm, accessed August 3, 2011.

Cute, Funny, or Sexy? What Makes a Mascot Tick?

In other situations, researchers know exactly what their problems are and can design careful studies to test specific hypotheses. For example, an organization may face a problem regarding health care benefits for their employees. Awareness of this problem could be based on input from human resource managers, recruiters, and current employees. The problem could be contributing to difficulties in recruiting new employees. How should the organization’s executive team address this problem? They may devise a careful test exploring which of three different health plans are judged the most desirable. This type of research is problem-oriented and seems relatively unam- biguous. This process may culminate with researchers preparing a report suggesting the relative effect of each alternative plan on employee recruitment. The selection of a new health plan should follow relatively directly from the research.

Business research can be classified on the basis of either technique or purpose. Experiments, surveys, and observational studies are just a few common research techniques. Classifying research by its purpose, such as the situations described above, shows how the nature of a decision situation influ- ences the research methodology. The following section introduces the three types of business research:

1. Exploratory 2. Descriptive 3. Causal

Matching the particular decision situation with the right type of research is important in obtaining useful research results.

Exploratory Research Exploratory research is conducted to clarify ambiguous situations or discover potential business opportunities. As the name implies, exploratory research is not intended to provide conclusive evi- dence from which to determine a particular course of action. In this sense, exploratory research is not an end unto itself. Usually exploratory research is a first step, conducted with the expectation that additional research will be needed to provide more conclusive evidence. Exploratory research is often used to guide and refine these subsequent research efforts. The Research Snapshot “Cute, Funny, or Sexy? What Makes a Mascot Tick?” illustrates a use of exploratory research. For example, rushing into detailed surveys before it is clear exactly what decisions need to be made can waste

exploratory research

Conducted to clarify ambiguous situations or discover ideas that may

be potential business opportunities.

RESEARCH SNAPSHOT

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time, money, and effort by providing irrelevant information. This is a common mistake in business research programs.

Exploratory research is particularly useful in new product development.3 Sony and Honda have each been instrumental in developing robot technology.4 Making a functional robot that can move around, perform basic functions, carry out instructions, and even carry on a conversation isn’t really a problem. What Sony and Honda have to research is what business opportunities may exist based on robot technology. Exploratory research allowing consumers to interact with robots suggests that consumers are more engaged when the robot has human qualities, such as the ability to walk on two legs. Researchers noticed that people will actually talk to the robot (which can understand basic oral commands) more when it has human qualities. In addition, consumers do seem enter- tained by a walking, talking, dancing robot. These initial insights have allowed each company to form more specific research questions focusing on the relative value of a robot as an entertainment device or as a security guard, and identifying characteristics that may be important to consumers.

Descriptive Research As the name implies, the major purpose of descriptive research is to describe characteristics of objects, people, groups, organizations, or environments. In other words, descriptive research tries to “paint a picture” of a given situation by addressing who, what, when, where, and how questions. For example, every month the Bureau of Labor Statistics (BLS) conducts descriptive research in the form of the Current Population Survey. Official statistics on a variety of characteristics of the labor force are derived from this survey (the Current Population Survey can be found at http://www .bls.gov/CPS/). This research describes the who, what, when, where, and how regarding the current economic and employment situation.

Unlike exploratory research, descriptive studies are conducted after the researcher has gained a firm grasp of the situation being studied. This understanding, which may have been developed in part from exploratory research, directs the study toward specific issues. Later, we will discuss the role of research questions and hypotheses. These statements help greatly in designing and implementing a descriptive study. Without these, the researcher would have little or no idea of what questions to ask. The Research Snapshot “Taking a Swing at Business Success” illustrates an application of descriptive research.

Descriptive research often helps describe market segments. For example, researchers used descriptive surveys to describe consumers who are heavy consumers (buy a lot) of organic food products. The resulting report showed that these consumers tend to live in coastal cities with popu- lations over 500,000, with the majority residing on the West Coast. The most frequent buyers of organic foods are affluent men and women ages 45–54 (36 percent) and 18–34 (35 percent).5 Inter- estingly, consumers who buy organic foods are not very brand-oriented—81 percent of them cannot name a single organic brand. Research such as this helps high-quality supermarkets such as Whole Foods make location decisions. Over half of Whole Foods’ food products are organic.

Similarly, the university considering the addition of an online MBA program might benefit from descriptive research profiling current and potential customers. Online customers are not identical to the traditional MBA student. They tend to be older than the average 24-year-old tradi- tional student, averaging about 30 years of age. Also, they tend to live in rural com- munities, be more introverted, and expect a higher workload than traditional students. Another key statistic is that the dropout rate for online students is significantly higher than for traditional MBA students. Nearly

descriptive research

Describes characteristics of objects, people, groups, organizations, or environments; tries to “paint a picture” of a given situation.

Descriptive research about consumers who buy organic food has paid off for the Whole Foods chain of stores.

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CHAPTER 4 The Business Research Process: An Overview • 53

14 percent of online students drop before completing a course as compared to 7.2 percent for tra- ditional in-class students. For this and other reasons, online students are much more costly to serve.6

Accuracy is critically important in descriptive research. If a descriptive study incorrectly esti- mates a university’s demand for its MBA offering by even a few students, it can mean the difference between the program sustaining itself or being a drain on already scarce resources. For instance, if a cohort group of 25 students is predicted, but only 15 students actually sign up, the program will likely not generate enough revenue to sustain itself. Therefore, it is easy to see that descriptive research forecasting sales revenue and costs or describing consumer attitudes, satisfaction, and com- mitment must be accurate or decision making will suffer.

Survey research typifies a descriptive study. For example, state societies of certified public accoun- tants (CPAs) conduct annual practice management surveys that ask questions such as, “Do you charge clients for travel time at regular rates?” “Do you have a program of continuing education on a regular basis for professional employees?” “Do you pay incentive bonuses to professional staff?” Although the researcher may have a general understanding of the business practices of CPAs, conclusive evidence in the form of answers to questions of fact must be collected to determine the actual activities.

A diagnostic analysis seeks to diagnose reasons for business outcomes and focuses specifically on the beliefs and feelings respondents have about and toward specific issues. A research study try- ing to diagnose slumping French wine sales might ask consumers their beliefs about the taste of French, Australian, and American wines. The results might indicate a deficiency in taste, suggesting that consumers do not believe French wines taste as fruity as do the others. Descriptive research can sometimes provide an explanation by diagnosing differences among competitors, but descrip- tive research does not provide direct evidence of causality.

Causal Research If a decision maker knows what causes important outcomes like sales, stock price, and employee satisfaction, then he or she can shape firm decisions in a positive way. Causal inferences are very powerful because they lead to greater control. Causal research seeks to identify cause-and-effect

diagnostic analysis

Seeks to diagnose reasons for business outcomes and focuses

specifically on the beliefs and feelings consumers have

about and toward competing products.

causal research

Allows causal inferences to be made; seeks to identify cause-

and-effect relationships.

Greg Norman is best known for performance on the golf course. However, he is also one of the most successful busi- nesspeople to come out of sports. Among his many ventures, Norman is a well-respected vintner. Norman Estates gained fame in the wine trade with Australian wines that offered con- siderable quality at a fair price. More recently, Norman Estates is expanding its portfolio by purchasing vineyard properties and production capacity in California. As Norman Estates and other wineries consider diversifying production beyond their tradi- tional boundaries, descriptive research can be vital in making these key decisions.

Descriptive research details what wine consumers like to drink in terms of where the wine is from and where the con- sumers are located. Consumers around the world form geo- graphic segments with preferences for wines from certain areas. American consumers, for instance, have contributed to the growing slump in French wine sales by switching increasingly from French wines to Australian- and American-made wines. In particular, French wines at low and moderate prices have suffered, whereas higher price French wine sales remain steady.

In addition, wine sales in the United States and in the United Kingdom are relatively strong compared to wine sales in France and Germany.

All of these descriptive results may allow Greg Norman a better understanding of the international wine market and therefore allow him to make better decisions about where to grow and produce wine. Do you think the choice to expand to California rather than France seems like a good decision?

Sources: Orth, U. R., M. M. Wolf, and T. Dodd, “Dimensions of Wine Region Equity and Their Impact on Consumer Preferences,” Journal of Product and Brand Management 14, no. 2 (2005), 88–97; “Swings and Roundabouts: An Analysis of Consumption Trends,” AIM-Digest (2008), http://www .aimdigest.com/gateway/pages/s&p%20drinking%20patterns/articles/ trends%202005.htm.

Taking a Swing at Business Success

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RESEARCH SNAPSHOT

causal inference

A conclusion that when one thing happens, another specific thing will follow.

relationships. When something causes an effect, it means it brings it about or makes it happen. The effect is the outcome. Rain causes grass to get wet. Rain is the cause and wet grass is the effect.

The different types of research discussed here are often building blocks—exploratory research builds the foundation for descriptive research, which usually establishes the basis for causal research. Thus, before causal studies are undertaken, researchers typically have a good understanding of the phenomena being studied. Because of this, the researcher can make an educated prediction about the cause-and-effect relationships that will be tested. Although greater knowledge of the situa- tion is a good thing, it doesn’t come without a price. Causal research designs can take a long time to implement. Also, they often involve intricate designs that can be very expensive. Even though managers may often want the assurance that causal inferences can bring, they are not always willing to spend the time and money it takes to get them.

Causality Ideally, managers want to know how a change in one event will change another event of interest. As an example, how will implementing a new employee training program change job performance? Causal research attempts to establish that when we do one thing, another thing will follow. A causal inference is just such a conclusion. While we use the term “cause” frequently in our everyday lan- guage, scientifically establishing something as a cause is not so easy. A causal inference can only be supported when very specific evidence exists. Three critical pieces of causal evidence are:

1. Temporal Sequence 2. Concomitant Variance 3. Nonspurious Association

TEMPORAL SEQUENCE Temporal sequence deals with the time order of events. In other words, having an appropriate causal order of events, or temporal sequence, is one criterion for causality. Simply put, the cause must occur before the effect. It would be difficult for a restaurant manager to blame a decrease in sales on a new chef if the drop in sales occurred before the new chef arrived. If a change in the CEO causes a change in stock prices, the CEO change must occur before the change in stock values.

CONCOMITANT VARIATION Concomitant variation occurs when two events “covary” or “correlate,” meaning they vary sys- tematically. In causal terms, concomitant variation means that when a change in the cause occurs, a change in the outcome also is observed. A correlation coefficient, which we discuss in Chapter 23, is often used to represent concomitant variation. Causality cannot possibly exist when there is no systematic variation between the variables. For example, if a retail store never changes its employees’ vacation policy, then the vacation policy cannot possibly be responsible for a change in employee satisfaction. There is no correlation between the two events. On the other hand, if two events vary together, one event may be causing the other. If a university increases its number of online MBA course offerings and experiences a decrease in enrollment in its traditional in-class MBA offerings, the online course offerings may be causing the decrease. But the systematic variation alone doesn’t guarantee it.

NONSPURIOUS ASSOCIATION Nonspurious association means any covariation between a cause and an effect is true, rather than due to some other variable. A spurious association is one that is not true. Often, a causal inference cannot be made even though the other two conditions exist because both the cause and effect have some common cause; that is, both may be influenced by a third variable. For instance, there is a strong, positive correlation between ice cream purchases and murder rates—as ice cream purchases increase, so do murder rates.7 When ice cream sales decline, murder rates also drop. Do people become murderers after eating ice cream? Should we outlaw the sale of ice cream? This would be silly because the concomitant variation observed between ice cream consumption and murder

temporal sequence

One of three criteria for causality; deals with the time order of events—the cause must occur before the effect.

concomitant variation

One of three criteria for causality; occurs when two events “covary,” meaning they vary systematically.

nonspurious association

One of three criteria for causality; means any covariation between a cause and an effect is true and not simply due to some other variable.

CHAPTER 4 The Business Research Process: An Overview • 55

rates is spurious. A third variable is actually important here. People purchase more ice cream when the weather is hot. People are also more active and likely to commit a violent crime when it is hot. The weather, being associated with both may actually cause both. Exhibit 4.2 illustrates the concept of spurious association.

absolute causality

Means the cause is necessary and sufficient to bring about

the effect.

conditional causality

Means that a cause is necessary but not sufficient to bring

about an effect.

Establishing evidence of nonspuriousness can be difficult. If a researcher finds a third vari- able that is related to both the cause and effect, which causes a significant drop in the correlation between the cause and effect, then a causal inference becomes difficult to support. Although the researcher would like to rule out the possibility of any alternative causes, it is impossible to observe the effect of every variable on the correlation between the cause and effect. Therefore, the researcher must use logic, or a theory, to identify the most likely “third” variables that would relate significantly to both the cause and effect. The researcher must control for these variables in some way. In addition, the researcher should use theory to make sure the assumed cause-and-effect relationship truly makes sense.

In summary, causal research should do all of the following:

1. Establish the appropriate causal order or sequence of events 2. Measure the concomitant variation between the presumed cause and the presumed effect 3. Examine the possibility of spuriousness by considering the presence of alternative plausible

causal factors

Degrees of Causality In everyday language, we often use the word “cause” in an absolute sense. For example, a warning label used on cigarette packages claims “smoking causes cancer.” Is this true in an absolute sense? Absolute causality means the cause is necessary and sufficient to bring about the effect. Thus, if we find only one smoker who does not eventually get cancer, the claim is false. Although this is a very strong inference, it is impractical to think that we can establish absolute causality in the behavioral sciences.

Why do we continue to do causal research then? Well, although managers may like to be able to draw absolute conclusions, they can often make very good decisions based on less powerful inferences. Conditional causality means that a cause is necessary but not sufficient to bring about

Proposed Causal Inference Spurious Association

DO NOT CROSS

EXHIBIT 4.2

The Spurious Effect of Ice Cream

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56 • PART ONE Introduction

an effect. This is a weaker causal inference. One way to think about conditional causality is that the cause can bring about the effect, but it cannot do so alone. If other conditions are right, the cause can bring about the effect. We know there are other medical factors that contribute to cancer. For instance, genetics, lifestyle, and diet are also plausible causes of cancer. Thus, if one smokes and has a genetic disposition, diet, and lifestyle that promote cancer, smoking could be considered a con- ditional cause of cancer. However, if we can find someone who has contracted cancer and never smoked, the causal inference would be proven wrong.

Contributory causality is the weakest form of causality, but it is still a useful concept. A cause need be neither necessary nor sufficient to bring about an effect. However, causal evidence can be established using the three factors discussed. For any outcome, there may be multiple causes. So, an event can be a contributory cause of something so long as the introduction of the other possible causes does not eliminate the correlation between it and the effect. This will become clearer when we discuss ways to test relationships in Chapters 23 and 24. Smoking then can be a contributory cause of cancer so long as the introduction of other possible causes does not cause both smoking and cancer.

Experiments Business experiments hold the greatest potential for establishing cause-and-effect relationships. An experiment is a carefully controlled study in which the researcher manipulates a proposed cause and observes any corresponding change in the proposed effect. An experimental variable represents the proposed cause and is controlled by the researcher by manipulating it. Manipulation means that the researcher alters the level of the variable in specific increments.

For example, consider a manager who needs to make decisions about the price and distribu- tion of a new video game console called the Wee Box. She understands that both the price level and the type of retail outlet in which the product is placed are potential causes of sales. A study can be designed which manipulates both the price and distribution. The price can be manipu- lated by offering it for $100 among some consumers and $200 among others. Retail distribution may be manipulated by selling the Wee Box at discount stores in some consumer markets and at specialty electronics stores in others. The retailer can examine whether price and distribution cause sales by comparing the sales results in each of the four conditions created. Exhibit 4.3 illustrates this study.

contributory causality

Means that a cause need be neither necessary nor sufficient to bring about an effect.

experiment

A carefully controlled study in which the researcher manipulates a proposed cause and observes any corresponding change in the proposed effect.

experimental variable

Represents the proposed cause and is controlled by the researcher by manipulating it.

manipulation

Means that the researcher alters the level of the variable in specific increments.

test-market

An experiment that is conducted within actual market conditions.

An experiment like the one described above may take place in a test-market. Test- marketing is a frequently used form of business experimentation. A test-market is an experiment that is conducted within actual business conditions. McDonald’s restaurants have a long-standing tra- dition of test-marketing new product concepts by introducing them at selected stores and mon- itoring sales and customer feedback. Recently, McDonald’s extensively test-marketed McCafé

Wee Box Sales by Condition

High Price Low Price

Specialty Distribution Peoria, Illinois: Retail Price: $200 Retail Store: Best Buy

Des Moines, Iowa: Retail Price: $100 Retail Store: Best Buy

General Distribution St. Louis, Missouri: Retail Price: $200 Retail Store: Big Cheap-Mart

Kansas City, Missouri: Retail Price: $100 Retail Store: Big Cheap-Mart

Assuming that Wee Box consumers are the same in each of these cities, the extent to which price and distribution cause sales can be examined by comparing the sales results in each of these four conditions.

EXHIBIT 4.3

Testing for Causes with an Experiment

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CHAPTER 4 The Business Research Process: An Overview • 57

specialty coffees and beverages. These products were sold at a group of McDonald’s outlets and feedback was used to refine the offering including the size of the cups, prices, and what types of extras to add to the drink (including sprinkles of chocolate, whipped cream, steamed milk, and chocolate, vanilla, and caramel shots). McDonald’s could then monitor the effect on overall sales, as well as cannibalization of regular coffee sales, in a real-world setting. Earlier, McDon- ald’s had test-marketed Wi-Fi service in some outlets. Three different rival Wi-Fi service pro- viders (the manipulation) were used in different locations and the cost, service, and customer feedback were used to select the best provider for use in McDonald’s restaurants.

Most basic scientific studies in business (for example, the development of theories about employee motivation or consumer behavior) ultimately seek to identify cause-and-effect relation- ships. In fact, we often associate science with experiments. To predict a relationship between, say, price and perceived quality of a product, causal studies often create statistical experiments with controls that establish contrast groups.

Uncertainty Influences the Type of Research So, which form of research—exploratory, descriptive, or causal—is appropriate for the current situation? The most appropriate type and the amount of research needed are largely a function of how much uncertainty surrounds the situation motivating the research. Exhibit 4.4 contrasts the types of research and illustrates that exploratory research is conducted during the early stages of decision making. At this point, the decision situation is usually highly ambiguous and manage- ment is very uncertain about what actions should, or even could, be taken. When management is aware of the problem but lacks some knowledge, descriptive research is usually conducted. Causal research requires sharply defined problems.

Each type of research also produces a different type of result. In many ways, exploratory research is the most productive since it should yield large numbers of ideas. It is part of the “domain of discovery,” and as such, unstructured approaches can be very successful. Too much structure in this type of research may lead to more narrowly focused types of responses that could

EXHIBIT 4.4

Characteristics of Different Types of Business Research

Exploratory Research Descriptive Research Causal Research

Amount of Uncertainty Characterizing Decision Situation

Highly ambiguous Partially defined Clearly defined

Key Research Statement Research question Research question Research hypothesis

When Conducted? Early stage of decision making Later stages of decision making

Later stages of decision making

Usual Research Approach Unstructured Structured Highly structured

Examples “Our sales are declining for no apparent reason.” “What kinds of new products are fast-food customers interested in?”

“What kind of people patronize our stores compared to our primary competitor?” “What product features are most important to our customers?”

“Will consumers buy more products in a blue package?” “Which of two advertising campaigns will be more effective?”

Nature of Results Discovery oriented, productive, but still speculative. Often in need of further research.

Can be confirmatory although more research is sometimes still needed. Results can be managerially actionable.

Confirmatory oriented. Fairly conclusive with managerially actionable results often obtained.

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58 • PART ONE Introduction

stifle creativity. Thus, although it is productive, exploratory research results usually need further testing and evaluation before they can be made actionable. At times, however, managers do take action based only on exploratory research results. Sometimes, management may not be able or may not care to invest the time and resources needed to conduct further research. Decisions made based only on exploratory research can be more risky, since exploratory research does not test ideas among a scientific sample.8 For instance, a business school professor may ask a class of current MBA students for ideas about an online program. Although the students may provide many ideas that sound very good, even the best of them has not been tested on a sample of potential online MBA students.

Descriptive research is typically focused around one or more fairly specific research questions. It is usually much more structured and, for many common types of business research, can yield managerially actionable results. For example, descriptive research is often used to profile a customer segment both demographically and psychographically. Results like this can greatly assist firms in deciding when and where to offer their goods or services for sale.

Causal research is usually very focused around a small number of research hypotheses. Experi- mental methods require tight control of research procedures. Thus, causal research is highly struc- tured to produce specific results. Causal research results are often managerially actionable since they suggest that if management changes the value of a “cause,” some desirable effect will come about. So, by changing the training program, the cause, an increase in employee productivity, can result.

Stages in the Research Process Business research, like other forms of scientific inquiry, involves a sequence of highly interrelated activities. The stages of the research process overlap continuously, and it is clearly an oversimpli- fication to state that every research project has exactly the same ordered sequence of activities. Nevertheless, business research often follows a general pattern. We offer the following research business stages:

1. Defining the research objectives 2. Planning a research design 3. Planning a sample 4. Collecting the data 5. Analyzing the data 6. Formulating the conclusions and preparing the report

Exhibit 4.5 portrays these six stages as a cyclical or circular-flow process. The circular-flow concept is used because conclusions from research studies can generate new ideas and knowledge that can lead to further investigation. Thus, there is a dashed connection between conclusions and reporting and defining the research objectives. Notice also that management is in the center of the process. The research objectives cannot be properly defined without managerial input. After all, it is the manager who ultimately has to make the decision. It is also the manager who may ask for additional research once a report is given.

In practice, the stages overlap somewhat from a timing perspective. Later stages sometimes can be completed before earlier ones. The terms forward linkage and backward linkage reflect the interrelationships between stages. Forward linkage implies that the earlier stages influence the later stages. Thus, the research objectives outlined in the first stage affect the sample selection and the way data are collected. The sample selection question affects the wording of questionnaire items. For example, if the research concentrates on respondents with low educational levels, the question- naire wording will be simpler than if the respondents were college graduates.

Backward linkage implies that later steps influence earlier stages of the research process. If it is known that the data will be collected via e-mail, then the sampling must include those with e-mail access. A very important example of backward linkage is the knowledge that the executives who will read the research report are looking for specific information. The professional researcher anticipates executives’ needs for information throughout the planning process, particularly during the analysis and reporting.

forward linkage

Implies that the earlier stages of the research process influence the later stages.

backward linkage

Implies that later steps influence earlier stages of the research process.

CHAPTER 4 The Business Research Process: An Overview • 59

Alternatives in the Research Process The researcher must choose among a number of alternatives during each stage of the research process. The research process can be compared to a map. It is important to remember that there is no single “right” path for all journeys. The road one takes depends on where one wants to go and the resources (money, time, labor, and so on) available for the trip. The map analogy is useful for the business researcher because there are several paths that can be followed at each stage. When there are severe time constraints, the quickest path may be most appropriate. When money and human resources are plentiful, more options are available and the appropriate path may be quite different.

Chapter 1 introduced the research process. Here, we briefly describe the six stages of the research process. Later, each stage is discussed in greater depth. Exhibit 4.6 shows the decisions that researchers must make in each stage. This discussion of the research process begins with research objectives, because most research projects are initiated to remedy managers’ uncertainty about some aspect of the firm’s business program.

Defining the Research Objectives Exhibit 4.6 shows that the research process begins with research objectives. Research objectives are the goals to be achieved by conducting research. In consulting, the term deliverables is often used to describe the objectives to a research client. The genesis of the research objectives lies in the type of decision situation faced. The objectives may involve exploring the possibilities of entering a new market. Alternatively, they may involve testing the effect of some policy change on employee job satisfaction. Different types of objectives lead to different types of research designs.

research objectives

The goals to be achieved by conducting research.

deliverables

The term used often in consulting to describe research objectives to a research client.

Defining Research Objectives

Research Design

Sampling

Data Collection

Conclusions and Reporting

Data Analysis

Research is sometimes directly actionable. The results may also suggest ideas for new studies.

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EXHIBIT 4.5

Stages of the Research Process

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60 • PART ONE Introduction

In applied business research, the objectives cannot really be determined until there is a clear understanding of the managerial decision to be made. This understanding must be shared between the actual decision maker and the lead researcher. We often describe this understanding as a problem statement. In general usage, the word problem suggests that something has gone wrong. This isn’t always the case before research gets started. Actually, the research objective may be to simply clarify a situation, define an opportunity, or monitor and evaluate current business operations. The research objectives cannot be developed until managers and researchers have agreed on the actual business “problem” that will be addressed by the research. Thus, they set out to “discover” this problem through a series of interviews and through a document called a research proposal.

EXHIBIT 4.6

Flowchart of the Business Research Process

Define research objectives

Selection of exploratory research

technique

Problem definition (statement of

research objectives)

Secondary (historical)

data

Previous research

Case study

Selection of basic research

method

Experiment

Laboratory Field

Survey

Interview Questionnaire

Problem Discovery and Definition

Planning the Research Design

Selection of sample design

Data Gathering

Data Processing and Analysis

Drawing Conclusions and Preparing Report

Sampling

Experience survey

Observation Secondary data study

Collection of data

(fieldwork)

Editing and coding

data

Data processing

and analysis

Interpretation of

findings

Report

Probability sampling

Nonprobability sampling

Note: Diamond-shaped boxes indicate stages in the research process in which a choice of one or more techniques must be made. The dotted line indicates an alternative path that skips exploratory research.

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CHAPTER 4 The Business Research Process: An Overview • 61

It should be noted that this process is oriented more toward discovery than confirmation or justification. Managers and researchers alike may not have a clear-cut understanding of the situa- tion at the outset of the research process. Managers may only be able to list symptoms that could indicate a problem. For example, employee turnover is increasing, but management may not know the exact nature of the problem. Thus, the problem statement often is made only in general terms; what is to be investigated is not yet specifically identified.

Defining the Managerial Decision Situation In business research, the adage “a problem well defined is a problem half solved” is worth remember- ing. Similarly, Albert Einstein noted that “the formulation of a problem is often more essential than its solution.”9 These phrases emphasize that an orderly definition of the research problem provides direction to the investigation. Careful attention to problem definition allows the researcher to set the proper research objectives. When the purpose of the research is clear, the chances of collecting the necessary and relevant information, and not collecting surplus information, will be much greater.

Managers often are more concerned with finding the right answer rather than asking the right question. They also want one solution quickly, rather than having to spend time considering many possible solutions. However, properly defining a problem can be more difficult than actually solv- ing it. In business research, if data are collected before the nature of the problem is carefully thought out, they probably will not yield useful information.

Thus, defining the decision situation must precede the research objectives. Frequently the researcher will not be involved until the management team has discovered that some information about a particular aspect of the business is needed. Even at this point the exact nature of the situa- tion may be poorly defined. Once a problem area has been discovered, the researcher and manage- ment together can begin the process of precisely defining it.

Much too often research is conducted without a clear definition of the objectives. Researchers forget that the best place to begin a research project is at the end. In other words, knowing what is to be accomplished determines the research process. An error or omission in specifying objectives is likely to be a costly mistake that cannot be corrected in later stages of the research process.

Exploratory Research Exploratory research can be used to help identify and clarify the decisions that need to be made. These preliminary research activities can narrow the scope of the research topic and help transform ambiguous problems into well-defined ones that yield specific research objectives. By investigating any existing stud- ies on the subject, talking with knowledgeable individuals, and informally investigating the situation, the researcher can progres- sively sharpen the focus of the research. After such exploration, the researcher should know exactly which data to collect during the formal phases of the project and how to conduct the project. Exhibit 4.6 indicates that managers and researchers must decide whether to use one or more exploratory research techniques. As Exhibit 4.6 indicates, this stage is optional.

The business researcher can employ techniques from four basic categories to obtain insights and gain a clearer idea of the problem: previous research, pilot studies, case studies, and experi- ence surveys. These are discussed in detail in later chapters. This section will briefly discuss previous research and focus group interviews, the most popular type of pilot study.

PREVIOUS RESEARCH As a general rule, researchers should first investigate previous research to see whether or not others may have already addressed similar research problems. Initially, internal research reports ©

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The library contains a wealth of information. Studies forming a literature review can be found in the library.

62 • PART ONE Introduction

should be searched within the company’s archives. In addition, some firms, such as Hoover’s in the chapter vignette, specialize in providing various types of research reports, such as economic fore- casts. The Census of Population and the Survey of Current Business are each examples of previous research conducted by an outside source.

Previous research may also exist in the public domain. The first place researchers will likely look today is online. The Internet and modern electronic search engines available through most university libraries have made literature reviews simpler and faster to conduct. A literature review is a directed search of published works, including periodicals and books, that discusses theory and presents empirical results that are relevant to the topic at hand. While a literature survey is common in applied research studies, it is a fundamental requirement of a basic research report.

Suppose, for example, that a bank is interested in determining the best site for additional auto- mated teller machines. A logical first step would be to investigate the factors that bankers in other parts of the country consider important. By reading articles in banking journals, management might quickly discover that the best locations are inside supermarkets located in residential areas where people are young, highly educated, and earning higher-than-average incomes. These data might lead the bank to investigate census information to determine where in the city such people live. Reviewing and building on the work already compiled by others is an economical starting point for most research.

PILOT STUDIES Almost all consumers take a test drive before buying a car. A pilot study serves a similar purpose for the researcher. A pilot study is a small-scale research project that collects data from respondents similar to those that will be used in the full study. It can serve as a guide for a larger study or exam- ine specific aspects of the research to see if the selected procedures will actually work as intended. Pilot studies are critical in refining survey questions and reducing the risk that the full study will be fatally flawed. This is particularly true for experimental research, which depends critically on valid manipulations of experimental variables.10 Pilot studies also often are useful in fine-tuning research objectives. Pilot studies are sometimes referred to as pretests. A pretest is a very descriptive term indicating a small-scale study in which the results are preliminary and intended only to assist in design of a subsequent study.

Focus group interviews are sometimes used as a pilot study. A focus group interview brings together six to twelve people in a loosely structured format. The technique is based on the assump- tion that individuals are more willing to talk about things when they are able to do so within a group discussion format. Focus group respondents sometimes feed on each other’s comments to develop ideas that would be difficult to express in a different interview format.

For example, suppose a consultant is hired by Carrefour to research the way consumers react to sales promotions. Carrefour began in France over 50 years ago and pioneered the discount hyper- market format. Carrefour is now the second largest retailer in the world (behind Walmart), oper- ating nearly 11,000 stores in 29 countries. Specifically, the researcher is asked to help Carrefour executives decide whether or not the size of promotional discounts should vary with national culture. In other words, the basic research question is whether or not culture influences consumer perceptions of sales promotions.11 A pretest may be needed to examine whether or not differences in currency might interfere with these perceptions, or whether or not the different terms that refer to promotions and discounts can be translated into the languages of each culture. For example, is a discount expressed in Korean won interpreted the same way as a discount expressed in euros? Each euro equals about $1.43 U.S., whereas a single U.S. dollar is worth about 1,057 won.12

Exploratory research need not always follow a structured design. Because the purpose of exploratory research is to gain insights and discover new ideas, researchers may use considerable creativity and flexibility. Some companies perform exploratory research routinely as part of envi- ronmental scanning. If the conclusions made during this stage suggest business opportunities, the researcher is in a position to begin planning a formal, quantitative research project.

Stating Research Objectives After identifying and clarifying the problem, with or without exploratory research, the researcher must formally state the research objectives. This statement delineates the type of research that is needed and what intelligence may result that would allow the decision maker to make informed

pilot study

A small-scale research project that collects data from respondents similar to those to be used in the full study.

pretest

A small-scale study in which the results are only preliminary and intended only to assist in design of a subsequent study.

focus group

A small group discussion about some research topic led by a moderator who guides discussion among the participants.

literature review

A directed search of published works, including periodicals and books, that discusses theory and presents empirical results that are relevant to the topic at hand.

CHAPTER 4 The Business Research Process: An Overview • 63

choices. The statement of research objectives culminates the process of clarifying the managerial decision into something actionable.

A written decision statement expresses the business situation to the researcher and makes sure that managers and researchers are on the same page. The research objectives try to directly address the decision statement or statements, as the case may be. As such, the research objectives represent a contract of sorts that commits the researcher to producing the needed research. This is why they are expressed as deliverables in applied business research. These research objectives drive the rest of the research process. Indeed, before proceeding, the researcher and managers must agree that the objectives are appropriate and will produce relevant information.

Linking Decision Statements, Objectives, and Hypotheses In Chapter 3 we discussed the role of theory and research hypotheses. Our hypotheses should be logically derived from and linked to our research objectives. For example, consider a person who is interested in pursuing an MBA from their local university. Since this same prospective student also needs to work, the researcher may use theoretical reasoning to develop the following hypothesis:

H1: The more hours per week a prospective student works, the more favorable the attitude toward online MBA class offerings.

Exhibit 4.7 illustrates how decision statements are linked to research objectives, which are linked to research hypotheses. Although the first two objectives each have one hypothesis, notice that the third has two. In reality, most research projects will involve more than one research objec- tive, and each of these may often involve more than one hypothesis. Think about how you might go about trying to test the hypothesis listed in Exhibit 4.7.

Decision Statement Research Objectives Hypotheses

What should be the retail price for product X?

Forecast sales for product X at three different prices.

Sales will be higher at $5.00 than at $4.00 or at $6.99.

In what ways can we improve our service quality?

Identify the top factors that contribute to customers’ perceptions.

Cleanliness is related positively to customers’ service quality service perceptions. Crowding is related negatively to customers’ service quality perceptions.

Should we invest in a training program to reduce role conflict among our employees?

Determine how much role conflict influences employee job satisfaction.

Role conflict is related positively to job satisfaction.

EXHIBIT 4.7

Example Decision Statements, Research Objectives, and Research Hypotheses

research design

A master plan that specifies the methods and procedures

for collecting and analyzing the needed information.

Planning the Research Design After the researcher has formulated the research problem, he or she must develop the research design as part of the research design stage. A research design is a master plan that specifies the methods and procedures for collecting and analyzing the needed information. A research design provides a framework or plan of action for the research. Objectives of the study determined during the early stages of research are included in the design to ensure that the information collected is appropriate for solving the problem. The researcher also must determine the sources of informa- tion, the design technique (survey or experiment, for example), the sampling methodology, and the schedule and cost of the research.

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64 • PART ONE Introduction

Selection of the Basic Research Method Here again, the researcher must make a decision. Exhibit 4.6 shows four basic design techniques for descriptive and causal research: surveys, experiments, secondary data, and observation. The objec- tives of the study, the available data sources, the urgency of the decision, and the cost of obtaining the data will determine which method should be chosen. The managerial aspects of selecting the research design will be considered later.

In business research, the most common method of generating primary data is the survey. Most people have seen the results of political surveys by Gallup or Harris Online, and some have been respondents (members of a sample who supply answers) to research questionnaires. A survey is a research technique in which a sample is interviewed in some form or the behavior of respondents is observed and described in some way. The term surveyor is most often reserved for civil engineers who describe some piece of property using a transit. Similarly, business researchers describe some group of interest (such as executives, employees, customers, or competitors) using a questionnaire. The task of writing a list of questions and designing the format of the printed or written question- naire is an essential aspect of the development of a survey research design.

Research investigators may choose to contact respondents by telephone or mail, on the Internet, or in person. An advertiser spending $3 million for 30 seconds of commercial time during the Super Bowl may telephone people to quickly gather information concerning their responses to the advertising. The economic development director for a city trying to determine the most important factors in attracting new businesses might choose a mail questionnaire because the appropriate executives are hard to reach by telephone. A manufacturer of a birth control device for men might determine the need for a versatile survey method wherein an interviewer can ask a variety of personal questions in a flexible format. While personal interviews are expensive, they are valuable because investigators can use visual aids and supplement the interviews with observations. Each of these survey methods has advantages and disadvantages. A researcher’s task is to find the most appropriate way to collect the needed information in a particular situation.

The objective of many research projects is merely to record what can be observed—for exam- ple, the number of automobiles that pass by a proposed site for a gas station. This can be mechani- cally recorded or observed by humans. Research personnel known as mystery shoppers may act as customers to observe actions of sales personnel or do comparative shopping to learn prices at com- peting outlets. A mystery shopper is paid to pretend to be a customer and gather data about the way employees behave and the way they are treated in general. How often are store policies followed? Are they treated courteously? Mystery shoppers can be valuable sources for observational data.

The main advantage of the observation technique is that it records behavior without relying on reports from respondents. Observational data are often collected unobtrusively and passively with- out a respondent’s direct participation. For instance, Nielsen Media Research uses a “ people meter” attached to television sets to record the programs being watched by each household member. This eliminates the possible bias of respondents stating that they watched the president’s State of the Union address rather than Gossip Girl on another station.

Observation is more complex than mere “nose counting,” and the task is more difficult than the inexperienced researcher would imagine. While observation eliminates potential bias from interviewer interaction, several things of interest, such as attitudes, opinions, motivations, and other intangible states of mind, simply cannot be observed.

The “Best” Research Design It is argued that there is no single best research design. As such, the researcher often has several alternatives that can accomplish the stated research objectives. Consider the researcher who must forecast sales for the upcoming year. Some commonly used forecasting methods are surveying executive opinion, collecting sales force composite opinions, surveying user expectations, project- ing trends, and analyzing environmental factors. Any one of these may yield a reliable forecast.

The ability to select the most appropriate research design develops with experience. Inexpe- rienced researchers often jump to the conclusion that a survey methodology is usually the best design because they are most comfortable with this method. When Chicago’s Museum of Science

survey

A research technique in which a sample is interviewed in some form or the behavior of respondents is observed and described in some way.

‘‘You cannot put the same shoe on every foot.’’—PUBLIUS SYRUS

CHAPTER 4 The Business Research Process: An Overview • 65

and Industry wanted to determine the relative popularity of its exhibits, it could have conducted a survey. Instead, a creative researcher familiar with other research designs suggested a far less expen- sive alternative: an unobtrusive observation technique. The researcher suggested that the museum merely keep track of the frequency with which the floor tiles in front of the various exhibits had to be replaced, indicating where the heaviest traffic occurred. When this was done, the museum found that the chick-hatching exhibit was the most popular. This method provided the same results as a survey but at a much lower cost. Take a look at the research design used by Rolling Rock illustrated in the Research Snapshot “Rolling Rock.”

Sampling Although the sampling plan is outlined in the research design, the sampling stage is a distinct phase of the research process. For convenience, however, we will treat the sample planning and the actual sample generation processes together in this section.

If you take your first bite of shrimp po-boy and conclude that it needs Tabasco, you have just conducted a sample. Sampling involves any procedure that draws conclusions based on

sampling

Involves any procedure that draws conclusions based on

measurements of a portion of the population.

Making a mark in the U.S. beer market can be difficult. American consumers tend to favor milder beers at lower price points. Some argue that most beers taste very similar. Taste tests do reveal that similarly positioned beers do taste very much the same. However, the taste rankings do not correspond to market share. For instance, Stroh’s fared very well in the taste tests, but it is hardly a market leader. Rolling Rock rated 12th out of 12 beers tasted. Tasters said it tasted a bit like canned corn. Clearly, there is something more to a successful beer than taste.

For many years Rolling Rock beer was a regional brand in western Pennsylvania. Its signature package was a longneck green bottle with a white painted label featuring icons such as a horse head, a steeplechase, the number 33, and a legend about the beer being brought to you “from the glass-lined tanks of Old Latrobe.” The brand, now sold by Labatt USA, expanded nationally during the 1980s by focusing on core consumers who purchased specialty beers for on-premise consumption and who were willing to pay higher prices than for national brands such as Budweiser.

As years went by, packaging options expanded to include bottles with ordinary paper labels for take-home consumption, often packaged in 12-packs. In the mid-1990s, in response to a competitive explosion from microbrews, Rolling Rock offered a number of line extensions, such as Rock Bock and amber Rock Ice. They failed. Sales stagnated. In New York and other crucial markets, price reductions to the level of Budweiser and Miller became inhibiting aspects of its marketing program. Business executives held the view that the longneck painted bottle was the heart of the brand. However, earlier efforts to develop cheaper imitations of the painted-label look had not achieved success.

Rolling Rock executives decided to conduct a massive consumer study, recruiting consumers at shopping malls and other

venues to view “live” shelf sets of beer—not just specialty beer, but beer at every price range from subpremiums and up. Consum- ers given money to spend in the form of chips were exposed to “old-bundle” packages (the old graphics and the paper-label stub- bies) and “new-bundle” packages (two new graphics approaches, including the one ultimately selected, and painted-label longnecks) at a variety of price points and asked to allocate chips to their next ten purchases. Some were even invited to take the “new-bundle” packages home with them for follow-up research.

As the business executives had hoped, the results did not leave any room for interpretation: Not only did the new pack- ages meet with consumers’ strong approval, but consumers consistently indicated that they would be willing to pay more for the brand in those packages. In fact, not only were they will- ing to pay more; they expected to pay more, particularly among consumers already familiar with the Rock. In three regions, the Northeast, Southeast, and West, purchase intent among users increased dramatically both at prices 20 cents higher per 12-pack and at prices 40 cents higher per 12-pack. The increase in pur- chase intent was milder in the Midwest, but there Rock already commanded a solid premium over Bud and other premium beers. The sole exception to that trend was in the brand’s core markets in Pennsylvania and Ohio, where Rock has never entirely escaped its shot-and-a-beer origins, but even there, purchase intent declined by only 2 percent at each of the higher prices.

Sources: Gerry Khermouch, “Sticking Their Neck Out,” BrandWeek ( November 9, 1998) 25–34, © 2006 VNU Business Media, Inc. Used with permission from Brandweek. © 1998–1999 VNU Business Media Inc.; “Which Brew for You?” Consumer Reports (August 2001), 10–17.

Rolling Rock

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RESEARCH SNAPSHOT

measurements of a portion of the population. In other words, a sample is a subset from a larger population. If certain statistical procedures are followed, a researcher need not select every item in a population because the results of a good sample should have the same characteristics as the population as a whole. Of course, when errors are made, samples do not give reliable estimates of the population.

A famous example of error due to sampling is the 1936 Literary Digest fiasco. The magazine conducted a survey and predicted that Republican Alf Landon would win over Democrat Franklin D. Roosevelt by a landslide in that year’s presidential election. This prediction was wrong—and the error was due to sample selection. The post-mortems showed that Literary Digest had sampled its readers and names drawn from telephone books and auto registrations. In 1936, not everyone had a telephone or a car; thus the sample was biased toward people with means. In reality, Roosevelt received over 60 percent of the popular vote.

In 2004, early exit polls led many to believe that John Kerry would win the U.S. presidential election.13 The exit polls were performed early on election day and done mostly in highly urban areas in the Northeast, areas that are predominantly Democratic. The resulting sample of voters responding to the early exit polls did not represent the entire U.S. population, and Kerry lost to Bush by over 3 million votes, or about 3 percent of all votes cast. Thus, the accuracy of predictions from research depends on getting a sample that really matches the population.

The first sampling question to ask is, “Who is to be sampled?” The answer to this primary question requires the identification of a target population. Who do we want the sample to reflect? Defining this population and determining the sampling units may not be so easy. If, for example, a savings and loan association surveys people who already have accounts for answers to image ques- tions, the selected sampling units may represent current customers but will not represent potential customers. Specifying the target population is a crucial aspect of the sampling plan.

The next sampling issue concerns sample size. How big should the sample be? Although management may wish to examine every potential buyer of a product or service, doing so may be unnecessary as well as unrealistic. Other things equal, larger samples are more precise than smaller ones. However, proper probability sampling can allow a small proportion of the total population to give a reliable measure of the whole. A later discussion will explain how large a sample must be in order to be truly representative of the universe or population. Essentially, this is a question of how much variance exists in the population.

The final sampling decision is how to select the sampling units. Simple random sampling may be the best known type, in which every unit in the population has an equal and known chance of being selected. However, this is only one type of sampling. For example, if members of the population are found in close geographical clusters, a cluster sampling procedure (one that selects area clusters rather than individual units in the population) will reduce costs. Rather than select- ing 1,000 individuals throughout the United States, it may be more economical to first select 25 counties and then sample within those counties. This will substantially reduce travel, hiring, and training costs. In determining the appropriate sampling plan, the researcher will have to select the most appropriate sampling procedure for meeting the established study objectives.

Gathering Data The data gathering stage begins once the sampling plan has been formalized. Data gathering is the process of gathering or collecting information. Data may be gathered by human observers or inter- viewers, or they may be recorded by machines as in the case of scanner data and web-based surveys.

Obviously, the many research techniques involve many methods of gathering data. Surveys require direct participation by research respondents. This may involve filling out a questionnaire or interacting with an interviewer. In this sense, they are obtrusive. Unobtrusive methods of data gathering are those in which the subjects do not have to be disturbed for data to be collected. They may even be unaware that research is going on at all. For instance, a simple count of motorists driv- ing past a proposed franchising location is one kind of data gathering method. However the data are collected, it is important to minimize errors in the process. For example, the data gathering should be consistent in all geographical areas. If an interviewer phrases questions incorrectly or records a respondent’s statements inaccurately (not verbatim), major data collection errors will result.

unobtrusive methods

Methods in which research respondents do not have to be disturbed for data to be gathered.

CHAPTER 4 The Business Research Process: An Overview • 67

Processing and Analyzing Data Editing and Coding After the fieldwork has been completed, the data must be converted into a format that will answer the manager’s questions. This is part of the data processing and analysis stage. Here, the information content will be mined from the raw data. Data processing generally begins with editing and coding the data. Editing involves checking the data collection forms for omissions, legibility, and consistency in classification. The editing process corrects problems such as interviewer errors (an answer recorded on the wrong portion of a questionnaire, for example) before the data are transferred to the computer.

Before data can be tabulated, meaningful categories and character symbols must be established for groups of responses. The rules for interpreting, categorizing, recording, and transferring the data to the data storage media are called codes. This coding process facilitates computer or hand tabulation. If computer analysis is to be used, the data are entered into the computer and verified. Computer-assisted (online) interviewing is an example of the impact of technological change on the research process. Telephone interviewers, seated at computer terminals, read survey questions displayed on the monitor. The interviewer asks the questions and then types in the respondents’ answers. Thus, answers are collected and processed into the computer at the same time, eliminating intermediate steps that could introduce errors.

Data Analysis Data analysis is the application of reasoning to understand the data that have been gathered. In its simplest form, analysis may involve determining consistent patterns and summarizing the rel- evant details revealed in the investigation. The appropriate analytical technique for data analysis will be determined by management’s information requirements, the characteristics of the research design, and the nature of the data gathered. Statistical analysis may range from portraying a simple frequency distribution to more complex multivariate analyses approaches, such as multiple regres- sion. Part 6 of this text will discuss three general categories of statistical analysis: univariate analysis, bivariate analysis, and multivariate analysis.

Drawing Conclusions and Preparing a Report One of the most important jobs that a researcher performs is communicating the research results. This is the final stage of the research project, but it is far from the least important. The con- clusions and report preparation stage consists of interpreting the research results, describing the implications, and drawing the appropriate conclusions for managerial decisions. These conclusions should fulfill the deliverables promised in the research proposal. In addition, it’s important that the researcher consider the varying abilities of people to understand the research results. The report shouldn’t be written the same way to a group of PhDs as it would be to a group of line managers.

All too many applied business research reports are overly complicated statements of technical aspects and sophisticated research methods. Frequently, management is not interested in detailed reporting of the research design and statistical findings, but wishes only a summary of the findings. If the findings of the research remain unread on the manager’s desk, the study will have been useless. The importance of effective communication cannot be overemphasized. Research is only as good as its applications.

Now that we have outlined the research process, note that the order of topics in this book fol- lows the flowchart of the research process presented in Exhibit 4.4. Keep this flowchart in mind while reading later chapters.

The Research Program Strategy Our discussion of the business research process began with the assumption that the researcher wished to collect data to achieve a specific organizational objective. When the researcher has only one or a small number of research objectives that can be addressed in a single study that study is referred to as a research project. We have emphasized the researcher’s need to select specific

research project

A single study that addresses one or a small number of

research objectives.

data analysis

The application of reasoning to understand the data that have

been gathered.

68 • PART ONE Introduction

techniques for solving one-dimensional problems, such as identifying customer segments, selecting the most desirable employee insurance plan, or determining an IPO stock price.

However, if you think about a firm’s business activities in a given period of time (such as a year), you’ll realize that business research is not a one-shot activity—it is a continuous process. An exploratory research study may be followed by a survey, or a researcher may con- duct a specific research project for each business tactical decision. If a new product is being developed, the different types of research might include studies to identify the size and charac- teristics of the market; product usage testing to record consumers’ reactions to prototype prod- ucts; brand name and packaging research to determine the product’s symbolic connotations; and test-marketing the new product. Thus, when numerous related studies come together to address issues about a single company, we refer to this as a research program. Because research is a continuous process, management should view business research at a strategic planning level. The program strategy refers to a firm’s overall plan to use business research. It is a planning activity that places a series of research projects in the context of the company’s strategic plan.

The business research program strategy can be likened to a term insurance policy. Conduct- ing business research minimizes risk and increases certainty. Each research project can be seen as a series of term insurance policies that makes the manager’s job a bit safer.

research program

Numerous related studies that come together to address multiple, related research objectives.

SUMMARY

1. Define decision making and understand the role research plays in making deci- sions. Decision making occurs when managers choose among alternative ways of resolving problems or pursuing opportunities. Decision makers must recognize the nature of the problem or opportunity, identify how much information is available, and recognize what information they need. Every business decision can be classified on a continuum ranging from complete certainty to absolute ambiguity. Research is a way that managers can become informed about different alternatives and make an educated guess about which alternative, if any, is the best to pursue.

2. Classify business research as either exploratory research, descriptive research, or causal research. Exploratory, descriptive, and causal research are three major types of business research projects. The clarity with which the decision situation is defined determines whether exploratory, descriptive, or causal research is most appropriate. When the decision is very ambiguous, or the interest is on discovering new ideas, exploratory research is most appropriate. Descriptive research attempts to paint a picture of the given situation by describing characteristics of objects, people, or organizations. Causal research identifies cause-and-effect relationships. Or, in other words, what change in “Y” will occur when there is some change in “X”? Three conditions must be satisfied to establish evidence of causality: 1) temporal sequence—the cause must occur before the effect; 2) concomitant variation—a change in the cause is associated (correlated) with a change in the effect; and 3) nonspurious association—the cause is true and not eliminated by the introduction of another potential cause.

3. List the major phases of the business research process and the steps within each. The six major phases of the research process discussed here are 1) defining the research objectives, 2) planning the research design, 3) sampling, 4) data gathering, 5) data processing and analysis, and 6) drawing conclusions and report preparation. Each stage involves several activities or steps. For instance, in planning the research design, the researchers must decide which type of study will be done and, if needed, recruit participants and design and develop experimental stimuli. Quite often research projects are conducted together as parts of a research program. Such programs can involve successive projects that monitor different elements of a firm’s operations.

4. Explain the difference between a research project and a research program. A research project addresses one of a small number of research objectives that can be included in a single study. In contrast, a research program represents a series of studies addressing multiple research objectives. Many business activities require an ongoing research task of some type.

CHAPTER 4 The Business Research Process: An Overview • 69

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. List five ways that business research can contribute to effective

business decision making. 2. Define business opportunity, business problem, and symptoms.

Give an example of each as it applies to a university business school. 3. Consider the following list, and indicate and explain whether

each best fits the definition of a problem, opportunity, or symptom: a. A 12.5 percent decrease in store traffic for a children’s shoe

store in a medium-sized city mall. b. Walmart’s stock price has decreased 25 percent between

2007 and 2009. c. A furniture manufacturer and retailer in North Carolina

reads a research report indicating consumer trends toward Australian Jarrah and Karri wood. The export of these products is very limited and very expensive.

d. Marlboro reads a research report written by the U.S. FDA. It indicates that the number of cigarette smokers in sub- Saharan Africa is expected to increase dramatically over the next decade.

4. What are the three types of business research? Indicate which type each item in the list below illustrates. Explain your answers. a. Establishing the relationship between advertising and sales in

the beer industry b. Ranking the key factors new college graduates are seeking

in their first career position c. Estimating the five-year sales potential for CAT scan

machines in the Ark-La-Tex (Arkansas, Louisiana, and Texas) region of the United States

d. Testing the effect of “casual day” on employee job satisfaction

e. Discovering the ways that people who live in apartments actually use vacuum cleaners, and identifying cleaning tasks for which they do not use a vacuum

5. Describe the type of research evidence that allows one to infer causality.

6. What is an experimental manipulation? A business researcher is hired by a specialty retail firm. The retailer is trying to decide what level of lighting and what temperature it should maintain in its stores to maximize sales. How can the researcher manipu- late these experimental variables within a causal design?

7. A business researcher gives a presentation to a music indus- try executive. After considering the results of a test-market

examining whether or not lowering the price of in-store CDs will lower the number of illicit downloads of the same music, the executive claims: “The test-market was conducted in eight cities. In two of the cities, lowering the price did not decrease illicit downloading. Therefore, lowering the price does not decrease this behavior, and we should not decide to lower prices based on this research.” Comment on the executive’s conclusion. What type of inference is being made? Will the decision not to lower prices be a good one?

8. We introduced the scientific method in Chapter 3. Do the stages in the research process discussed here seem to follow the scientific method?

9. Why is the “defining the research objectives” phase of the research process probably the most important stage?

10. Suppose Auchan (http://www.auchan.fr), a hypermarket chain based out of France, was considering opening three hypermar- kets in the midwestern United States. What role would theory play in designing a research study to track how the shopping habits of consumers from the United States differ from those in France and from those in Japan? What kind of hypothesis might be examined in a study of this topic?

11. Define research project and research program. Referring to the question immediately above, do you think a research project or a research program is needed to provide useful input to the Auchan decision makers?

12. What type of research design would you recommend in the sit- uations below? For each applied business research project, what might be an example of a “deliverable”? Which do you think would involve actually testing a research hypothesis? a. The manufacturer of flight simulators and other pilot

training equipment wishes to forecast sales volume for the next five years.

b. A local chapter of the American Lung Association wishes to identify the demographic characteristics of individuals who donate more than $500 per year.

c. Caterpillar Inc. is concerned about increasing inventory costs and is considering going completely to a just-in-time inventory system.

d. A food company researcher wishes to know what types of food are carried in brown-bag lunches to learn if the com- pany can capitalize on this phenomenon.

e. A researcher wishes to identify who plays bingo.

absolute causality, 56 backward linkage, 59 business opportunity, 49 business problem, 49 causal inference, 55 causal research, 54 concomitant variation, 55 conditional causality, 56 contributory causality, 57 data analysis, 68 decision making, 49 deliverables, 60

descriptive research, 53 diagnostic analysis, 54 experiment, 57 experimental variable, 57 exploratory research, 52 focus group, 63 forward linkage, 59 literature review, 63 manipulation, 57 nonspurious association, 55 pilot study, 63 pretest, 63

research design, 64 research objectives, 60 research program, 69 research project, 68 sampling, 66 survey, 65 symptoms, 49 temporal sequence, 55 test-market, 57 unobtrusive methods, 67

KEY TERMS AND CONCEPTS

70 Part 1: Introduction

RESEARCH ACTIVITIES 1. ’NET Look up information about the online MBA programs at

the University of Phoenix (http://www.mba-online- program. com/university_of_phoenix_online_mba.html). Compare it to the traditional MBA program at your university. Suppose each was looking to expand the numbers of students in their programs. How might the research design differ for each?

2. ’NET Use a web browser to go to the Gallup Organization’s home page at (http://www.gallup.com). The Gallup home page changes regularly. However, it should provide an oppor- tunity to read the results of a recent poll. For example, a poll might break down Americans’ sympathies toward Israel or the

Palestinians based on numerous individual characteristics such as political affiliation or religious involvement. After reading the results of a Gallup poll of this type, learn how polls are conducted. You may need to click “About Gallup” and/or Frequently Asked Questions (FAQs) to find this information on how the polls are conducted. List the various stages of the research process and how they were (or were not) followed in Gallup’s project.

3. Any significant business decision requires input from a research project. Write a brief essay either defending this statement or refuting it.

Joe Brown is ready to start a new career. After spend- ing 30 years as a market researcher and inspired by the success of Starbucks, he is ready to enter the

coffee shop business. However, before opening his first shop, he realizes that a great deal of research is needed. He has some key questions in mind.

■ What markets in the United States hold the most promise for a new coffee shop?

■ What type of location is best for a coffee shop? ■ What is it that makes a coffee shop popular? ■ What coffee do Americans prefer?

A quick trip to the Internet reveals more previous research on coffee, markets, and related materials than he expected. Many studies address taste. For example, he finds several studies that in one way or another compare the taste of different coffee shop coffees. Most commonly, they compare the taste of coffee from Starbucks against coffee from McDonald’s, Dunkin’ Donuts, Burger King, and sometimes a local competitor. However, it becomes difficult to draw a conclusion as the results seem to be inconsistent.

■ One study had a headline that poked fun at Starbucks’ high- priced coffee. The author of this study personally purchased coffee to go at four places, took them to his office, tasted them, made notes and then drew conclusions. All the coffee was tasted black with no sugar. Just cups of joe. He reached the conclusion that McDonald’s Premium Coffee (at about $1.50 a cup), tasted nearly as good as Starbucks House Blend (at about $1.70 a cup), both of which were much better than either Dunkin’ Donuts (at about $1.20) or Burger King (less than $1). This study argued that McDonald’s was best, all things considered.

■ Another study was written up by a good critic who was sim- ply interested in identifying the best-tasting coffee. Again, he tasted them all black with nothing added. Each cup of coffee was consumed in an urban location near the inner city center in which he lived. He reached the conclusion that Starbucks’ coffee had the best flavor although it showed room for improvement.

McDonald’s Premium Coffee was not as good, but it was better than the other two. Dunkin’ Donuts coffee had reasonably unob- jectionable taste but was very weak and watery. The Burger King coffee was simply not very good.

■ Yet another study talked about Starbucks becoming a huge com- pany and how it had lost touch with the common coffee shop customer. The researchers stood outside a small organic specialty shop and interviewed 100 consumers as they exited the shop. They asked, “Which coffee do you prefer?” The results showed a preference for a local coffee, tea, and incense shop, and otherwise put Starbucks last behind McDonald’s, Burger King, and Dunkin’ Donuts.

■ Still another study compared the coffee-drinking experi- ence. A sample of 50 consumers in St. Louis, Missouri, were interviewed and asked to list the coffee shop they fre- quented most. Starbucks was listed by more consumers than any other place. A small percentage listed Dunkin’ Donuts but none listed McDonald’s, despite their efforts at creat- ing a premium coffee experience. The study did not ask consumers to compare the tastes of the coffee across the different places.

Joe also wants to find data showing coffee consumption patterns and the number of coffee shops around the United States, so he spends time looking for data on the Internet. His searches don’t reveal anything satisfying.

As Joe ponders how to go about starting “A Cup of Joe,” he wonders about the relevance of this previous research. Is it use- ful at all? He even questions whether he is capable of doing any primary research himself and considers hiring someone to do a feasibility study for him. Maybe doing research is easier than using research.

Sources: Shiver, J., “Taste Test: The Little Joes Take on Starbucks,” USA Today (March 26, 2008), http://www.usatoday.com/money/industries/food/2006-03-26- coffee_x.htm, accessed August 3, 2011; Associated Press, “McDonald’s Coffee Beats Starbucks, Says Consumer Reports,” The Seattle Times (February 2, 2007), http://seattletimes.nwsource.com/html/ businesstechnology/2003553322_ webcoffeetest02.html, accessed August 3, 2011; “Coffee Wars: Starbucks v McDonald’s,” The Economist 386 (January 10, 2008), 58.

A New “Joe” on the Block

CASE 4.1

(Continued )

Chapter 4: The Business Research Process: An Overview 71

Questions 1. What are the top three key decisions faced by Joe? 2. What are the key deliverables that an outside researcher should

produce to help Joe with the key decisions? 3. How relevant are the coffee taste studies cited above? Explain. 4. What flaws in the coffee taste studies should Joe consider in

trying to weigh the merits of their results? 5. Briefly relate this situation to each of the major stages of the

marketing research process.

6. Try to do a quick search to explore the question: “Are American consumer preferences the same all across the United States?”

7. Would it be better for Joe to do the research himself or have a consultant perform the work?

8. If a consultant comes in to do the job, what are three key deliv- erables that would likely be important to Joe in making a decision to launch the “A Cup of Joe” coffee shop.

Case 4.1 (Continued )

72 Part 1: Introduction

After studying this chapter, you should be able to

1. Know when research should be conducted externally and when it should be done internally

2. Be familiar with the types of jobs, job responsibilities, and career paths available within the business research industry

3. Understand the often conflicting relationship between management and researchers

4. Define ethics and understand how it applies to business research

5. Know and appreciate the rights and obligations of a) research respondents—particularly children, b) business researchers, and c) research clients or sponsors

6. Know how to avoid a conflict of interest in performing business research

C H A P T E R

J ohn Harris has been senior research analyst for Delavan Insurance Group (DIG), a large insurance company, for the past 10 years. The consumer insurance industry has changed drastically in the past few years with the

growing acceptance of Internet-based insurance providers and traditional insurance companies expanding their web presence. John’s company had long based their reputation and business model on developing close personal relationships between the company’s insurance agents and their customers. DIG’s manage- ment feared that offering insurance direct to consumers online would harm this relationship and undermine their agent system. However, when Scott Jeckel was hired as the new CEO, that all changed. Scott was determined to make Delavan Insurance Group “a 21st century insurance provider” and demanded that the company push forward with a new website and introduce online insurance quotes and sales. Scott hired his cousin, Doug Jeckel, to develop and launch the new corporate web system.

John’s latest project was to assess consumer perceptions of the company’s new website. The study focused on the informa- tion provided, ease of use, and compared DIG’s website to the

LEARNING OUTCOMES

The Human Side of Business Research Organizational and Ethical Issues

Chapter Vignette:

I Can’t Share This Report!

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Introduction The vignette described above involves a researcher who faces a challenge in what is learned from the research process. Many companies have their own employees perform research projects and research programs. Thus, research is sometimes performed in-house, meaning that employees of the company that will benefit from the research project actually perform the research. In other cases, the research is performed by an outside agency, meaning that the company that will benefit from the research results hires an independent, outside firm to perform a research project.

While it would seem that in-house research would usually be of higher quality because of the increased knowledge of the researchers conducting the studies, there are several reasons why employees of the firm may not always be the best people to do the job (see Exhibit 5.1). When the firm facing a decision encounters one of the following situations, they should consider having the research performed by an outside agency:

EXHIBIT 5.1

Should Research Be Done In-House or by an Outside Agency?

outside agency

An independent research firm contracted by the company

that actually will benefit from the research.

in-house research

Research performed by employees of the company that

will benefit from the research.

leading Internet insurance providers. His analyses and results were clear—the new website was not viewed as favorably as the competition on any dimension. In fact, of the six insurance companies included in the study, DIG was rated the very lowest. John carefully reviewed the research methodology and double- checked the analysis. There was no doubt that the new website was a failure.

John presented the results of the study to Scott Jeckel the week before the scheduled meeting of DIG’s board of direc- tors. Scott was furious! “I have my entire reputation riding on this website. We have invested millions of dollars on our

new system. There is no way I am presenting the results of this study to our board. You must have made some mistake.” John told Scott that he was very careful in carrying out the study and double-checked every step of the process. Scott pushed the report back across his desk to John and firmly stated, “I am certain you have made a mistake. I expect to see a new report with the correct results on my desk tomorrow; a report that we will all be excited to share with the board. Your career is riding on that.” John picked up the report from Scott’s desk and walked out of the office. What was he

supposed to do?

Advantages of In-House Research:

• Quick turn-around • Better collaboration w/employees • Cheaper costs • Secret process

Advantages of an Outside Agency:

• Fresh perspective • More objectivity • Special expertise • Local knowledge

■ An outside agency often can provide a fresh perspective. Creativity is often hindered by too much knowledge. When a firm is seeking new ideas, particularly in discovery-oriented research, an outsider is not constrained by the groupthink that often affects a company employee. In other words, employees who spend so much time together in their day-to-day work activities begin to act and think alike to a large degree. For example, history is filled with stories of products that remained unsuccessful commercially for years until someone from outside the company discovered a useful application. The technology for a microwave oven was invented in the 1940s by a company called Raytheon. Raytheon worked on radar

‘‘To manage a business is to manage its future; and to manage the future is to manage information.’’—MARION HARPER

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74 • PART ONE Introduction

systems for the Allied military in World War II. Not until someone from another company, Amana, had the creativity to test the concept of using microwaves in a kitchen appliance did it become a commercial success. For a glimpse at some of the research organizations perceived as creative, take a look at the websites of the “Top 10 Most Innovative Research Organiza- tions” shown in Exhibit 5.2.

Rank Company Website Number of Mentions

1 Brainjuicer http://www.brainjuicer.com/ 60

2 TNS Global http://www.tnsglobal.com/ 32

3 Vision Critical http://www.visioncritical.com/ 32

4 Synovate http://www.synovate.com/ 31

5 Ipsos http://www.ipsos-na.com/ 25

6 Nielsen http://www.nielsen.com/ 25

7 Anderson Analytics http://www.andersonanalytics.com/ 21

8 Itracks http://www.itracks.com/ 18

9 GFK http://www.gfk.com/ 17

10 Peanut Labs http://www1.peanutlabs.com/ 16

Source: “Top 10 Market Research Companies Perceived to be Innovative,” posted by Leonard Murphy, Tuesday, February 15, 2011, 15:53 pm at http://www.greenbookblog.org/2011/02/15/top-10-companies-perceived-to-be-innovative-grit-2010-sneak-peek/, accessed April 30, 2011.

EXHIBIT 5.2

“Top 10” Most Innovative Research Organizations

■ An outside agency often can be more objective. When a firm is facing a particularly sensitive situation that may even impact a large number of jobs within the company, it may be difficult for researchers to be objective. Alternatively, as depicted in our opening vignette, if a chief executive within the firm is in love with some new idea, researchers may feel a great deal of pressure to present results that are supportive of the concept. In these cases, outside research- ers may be a good choice. Since they don’t have to work for the company and interact with the players involved on a daily basis, they are less concerned about presenting results that may not be truly welcome.

■ An outside agency may have special skills. When a firm needs research requiring a particular expertise that some outside agency specializes in, it may be a good idea to use that firm to conduct the research. For example, if a company is searching for new ideas about how to use its website, an online focus group interview may be needed. While this is a skill that may not be prevalent within the company, there are several research firms that specialize in this particular type of research. Thus, the outside agency may have greater competency in this specific area.

■ An outside agency often has local expertise allowing it to specialize in research from its home area or other global regions. When a company needs research conducted in a particu- lar country or even from a specific area of a country, an outside agency may be advanta- geous because of its knowledge of the local customs and values. The company probably also knows acceptable ways to get information from citizens in that particular area. For example, a research agency based here in the United States would probably not strongly consider a door-to-door survey for consumer research. However, in other parts of the world,

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 75

particularly with a less developed communication infrastructure, door-to-door interviews may be a viable option. Thus, the outside agency may have greater competency in this specific area.

Likewise, there are conditions that make in-house research more attractive as well, as in the following situations:

■ If the research project needs to be completed very quickly, chances are that in-house research- ers can get started more quickly and get quicker or better access to internal resources that can help get the project done in short order.

■ If the research project will require the close collaboration of many other employees from diverse areas of the organization, then in-house research may be preferable. The in-house research team can usually gain cooperation and more quickly ascertain just who needs to be interviewed and where those people can be found.

■ A third reason for doing a project in-house has to do with economy. In-house research can almost always be done more cheaply than that done by an outside research firm.

■ If secrecy is a major concern, then the research is best done in-house. Even though the outside firm might be trusted, it may take slightly less care in disguising its research efforts. Thus, other companies may pick up on signals in the marketplace that suggest the area of research for a firm.

This chapter focuses on the human side of research. We first discuss the internal working of a research unit within a large company. We then turn to the different types of options that exist when dealing with an outside agency. Some of the most creative research companies are presented in Exhibit 5.2. All of this is wrapped up by a discussion of the many ways in which ethics and research come together.

Organizational Structure of Business Research The placement of business research within a firm’s organizational structure and the structure of the research department itself vary substantially, depending on the firm’s acceptance of the concept of internal research and its stage of research sophistication. A research department can easily become

SURVEY

THIS! By now, you are becoming famil- iar with the student questionnaire that accompanies this book. Examine the items in the ques- tionnaire and the questionnaire overall for the following issues.

1. Were you required to identify yourself by name in complet- ing the survey?

2. Can the results (you can access the results through your instructor) be linked to respondents by name?

3. Do any items need to be tied to a name to be useful to the researcher? 4. Consider the portion of the survey shown here. What if another instructor asked for the results from this particular

section of the survey but was only interested in them if the names of the students also can be provided? The instructor believes that he can use the results to encourage particular students to change their study habits. Take the role of the researchers who implemented this research. Should you provide the information this instructor is asking for? Why or why not?

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isolated with poor organizational placement. Researchers may lack a voice in executive committees when they have no continuous relation- ship with management. This can occur when the research depart- ment is positioned at an inappropri- ately low level. Given the critically important nature of the intelligence coming out of a research depart- ment, it should be placed relatively high in the organizational struc- ture to ensure that senior manage- ment is well informed. Research departments should also be linked with a broad spectrum of other units within the organization. Thus, they should be positioned to pro- vide credible information both upstream and downstream within the organization.

Research departments that perform a staff function must wait for management to request assistance. Similar to John’s situation in the opening vignett, often the term “client” or “internal consultant” is used by the research department to refer to management for whom services are being performed. The research department responds to clients’ requests and is responsible for the design and execution of all research. It should function like an internal consulting organization that develops action-oriented, data-based recommendations.

Business Research Jobs Research organizations themselves consist of layers of employees. Each employee has certain spe- cific functions to perform based on his or her area of expertise and experience. A look at these jobs not only describes the potential structure of a research organization, but it also provides insight into the types of careers available as a business research specialist.

Small Firms While it is difficult to precisely define the boundaries between small firms, midsized firms, and large firms, generally speaking, government statistics usually consider firms with fewer than 100 emp- loyees to be small. In small firms, the vice president of marketing may be in charge of all significant internal research projects. This officer may focus on organizational research projects that relate to staffing or stakeholder relations, or may be a sales manager who collects and analyzes sales histories, trade association statistics, and other internal data. Small companies usually have few resources and special competencies to conduct large-scale, sophisticated research projects. An advertising agency or a business consulting firm that specializes in research will be contracted if a large-scale survey is needed. At the other extreme, a large company like Procter & Gamble may staff its research depart- ments with more than 100 people.

Midsized Firms Midsized firms can be thought of as those with between 100 and 500 employees. In a mid- sized firm, the research department may reside in the organization under the director of mar- keting research, as shown in Exhibit 5.3. This person provides leadership in research efforts and

When research departments grow, they begin to specialize by product or business unit. This happened in the Marriott Corporation, which now has a specific director of research for its lodging facilities.

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CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 77

research assistants

Research employees who provide technical assistance

with questionnaire design, data analyses, and similar activities.

manager of decision support systems

Employee who supervises the collection and analysis of

sales, inventory, and other periodic customer relationship

management (CRM) data.

forecast analyst

Employee who provides technical assistance such as

running computer programs and manipulating data to generate a sales forecast.

research analyst

A person responsible for client contact, project design,

preparation of proposals, selection of research suppliers,

and supervision of data collection, analysis, and

reporting activities.

EXHIBIT 5.3

Organization of the Marketing Research Department in a Large Firm

Supervisor Marketing

Information from

Syndicated Services

Supervisor Marketing Statistics

Supervisor Fundamental

Research/ Environmental

Researchd

Supervisor Promotion Researchc

Supervisor Marketing Practice

Researchb

Supervisor Employee Researcha

Supervisor Customer

Satisfaction Research

Supervisor Product

Research

Supervisor Product

Research

Manager Market/

New Product

Research

Manager Customer

Satisfaction and Total

Quality

Manager Marketing Research

Manager Decision Support Systems

Director Marketing Research

(by product groupings)

aConducts research to improve total quality management in production. bConducts research that cuts across product lines or involves competitive marketing practices or characteristics of customer groups. cConducts research that cuts across product lines to measure the effectiveness of promotional activities. dConducts research aimed at gaining a basic understanding of various elements of the marketing process.

integrates all staff-level research activities. (This position will be discussed in greater detail in the next section.)

A research analyst is responsible for client contact, project design, preparation of proposals, selection of research suppliers, and supervision of data collection, analysis, and reporting activities. Normally, the research analyst is responsible for several projects simultaneously covering a wide spectrum of the firm’s organizational activities. He or she works with product or division manage- ment and makes recommendations based on analysis of collected data.

Research assistants (or associates) provide technical assistance with questionnaire design, data analyses, and so forth. Another common name for this position is junior analyst. The manager of decision support systems supervises the collection and analysis of sales, inven- tory, and other periodic customer relationship management (CRM) data. Sales forecasts for product lines usually are developed using analytical and quantitative techniques. Sales infor- mation is provided to satisfy the planning, analysis, and control needs of decision makers. The manager of decision support systems may be assisted by a forecast analyst who provides technical assistance, such as running computer programs and manipulating data to forecast sales for the firm.

Personnel within a planning department may perform the research function in a midsized firm. At times, they may outsource some research functions, depending on the size of the proj- ect and the degree of sophistication. The planner may design research studies and then contract with outside firms that supply research services such as interviewing or data processing. They can combine the input from these outside agencies with their own work to write research reports.

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78 • PART ONE Introduction

Large Research Firms As research departments grow, they tend to specialize by product or strategic business unit. Major firms can be thought of as those with over 500 employees. Marriott Corporation has a director of research for lodging (for example, Marriott Hotels and Resorts, Courtyard by Marriott, and Fairfield Inn) and a director of research for contract services and restaurants (for example, Roy Rogers, Big Boy, and Senior Living Services). Each business unit’s research director reports to the vice president of corporate marketing services. Many large organizations have managers of cus- tomer quality research who specialize in conducting surveys to measure consumers’ satisfaction with product quality.

In many instances, business research units are located within a firm’s marketing function. Exhibit 5.3 illustrates the organization of a typical major firm’s marketing research department. Within this organization, the centralized research department conducts research for all the divi- sion’s product groups. This is typical of a large research department that conducts much of its own research, including fieldwork.

Other positions within a major firm’s research department may include director of data col- lection (field supervisor), manager of quantitative research, focus group moderator, and manager of data processing. These are not shown in Exhibit 5.3. Even large firms sometimes outsource some research functions or even an entire project from time to time. For now, we turn our attention to the job of director of research and the interface between the research department and other departments.

The Director of Research as a Manager A director of research plans, executes, and controls the firm’s research function. This person typically serves on company executive committees that identify competitive opportunities and formulate strategies that involve customers or other organizational stakeholders. The various directors from each functional area generally make up this committee (such as finance, sales, production, and so forth). The director of research provides the research perspective during meetings. For instance, the researcher can provide input as to what types of business intelligence can be feasibly obtained given the decision being discussed. Research directors typically face problems like these:

■ Skilled research professionals may like conducting research better than managing people. They pride themselves on being hands-on researchers. However, a director is a manager and spends more time in meetings and managing than actually conducting research.

■ The research management role often is not formally recognized. ■ Outstanding research professionals often have trouble delegating responsibility. The pride that

comes with being a knowledgeable researcher makes it difficult to give up control. They may genuinely feel “I can do it better myself.” As a result, they delegate only elementary or tedious tasks to subordinates. The subordinates can sometimes become disenchanted and thus become unhappy with their work.

■ Finally, research is often seen as a hodgepodge of techniques available to answer individual, unrelated questions. According to this view, a research operation encompasses an array of more or less equal projects, each handled by a project director. Hence, many firms view a full-time director as unnecessary.1

Sources of Conflict between Senior Management and Research In principle, the functions of research should merge harmoniously with the objectives of manage- ment for the benefit of both parties. In practice, the relationship between a research department and the users of research frequently is characterized by misunderstanding and conflict.

‘‘The longer the title, the less important the job.’’—GEORGE McGOVERN

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 79

Research That Implies Criticism As we saw in the chapter vignette, a manager who requests information on customer reactions to a new website may not be happy when the results are unfavorable. Similarly, a sales manager who informally projects a 5 percent increase in sales will not like hearing from the research depart- ment that the market potential indicates sales volume should be up by 20 percent. In each of these situations, the research presents information that implies criticism of a line executive’s decision. In personal life, a sure way to lose a friend is to be openly critical of him or her. Things are no dif- ferent in business.

Money Research budgets are a source of conflict between management and researchers. Financial manag- ers often see research as a cost rather than as an investment or a way of lowering risk. Successful decisions that are supported by research are seldom attributed to the researcher. Thus, as is often true in many areas of business, managers often want to spend as little as possible on research. In contrast, researchers often vigorously resist cutting corners in conducting research. For instance, they may feel that a large random sample is necessary to adequately address a research question using descriptive research. This approach can be very expensive and sometimes time consuming. Inevitably, management’s desire to save money and the researcher’s desire to conduct rigorous research conflict. Successful research projects often are those that are based on compromise. This may involve working within a budget that will produce meaningful results and sacrifice precision and rigor minimally.

Time Researchers say, “Good research takes time!” Managers say, “Time is money!” Like oil and water, these two views do not go together easily. A look back at the research process in the last chapter makes it clear that it can take some time to complete a research project. Simply planning one can involve days, if not weeks, of study and preparation. For instance, conducting a literature review or a review of previous studies can take weeks. Without them, the researcher may not be able to develop specific research hypotheses that would direct the project very specifically toward the cur- rent issue. Other times, the researcher may wish to interview more people than time can allow or take the time to use a more sophisticated data analysis approach.

The customer’s voice is powerful in many ways. How can a company harness that power? Many companies turn to J. D. Power to find powerful research results. J. D. Power rates competing company’s products and services in many indus- tries. For instance, do you want to know what tires make consumers the happiest? This might be an important ques- tion for retail dealers considering product lines and for auto manufacturers looking to enhance the value of their new cars by using quality OEM component parts. A sample of 30,000 consumers ranks Michelin highest based on good wear and the smallest number of problems. However, Pirelli tire owners thought their tires looked the best! Value comes in many forms. J. D. Power also breaks results down by region. In the southern

U.S., consumers rate Bright House Networks as the highest performing cable company based on customer service, price, and product offerings. However, in the Upper Midwest, WideOpenWest (WOW) is the leading provider. Perhaps the cable companies or even competitors like DirecTV will find information like this full of power. Sources: Robuck, Mike “Cable operators fare well in J. D. Power study,” CedMagazine.com - September 15, 2010; “Michelin, Pirelli Brands Top J. D. Power OE Tire Consumer Satisfaction Study,” Tire Business 28 (May 10, 2010), 22–23.

The True Power of Research

RESEARCH SNAPSHOT

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Oftentimes, the more quickly the research project is done, the less likely it is to be successful. This doesn’t mean it can’t provide valuable information. It simply is not as certain that a quickly put together study will provide answers as valuable as a more deliberately planned project. When studies are rushed, the following sources of error become more prominent than they would be otherwise:

■ Conducting a study that is needed. Taking more time to perform a literature search, including through company and industry reports, may have provided the needed intelligence without a new study.

■ Addressing the wrong issue. Taking more time to make sure the decision statement is well defined and that the research questions that follow will truly address relevant issues can lessen the chance that the research goes in the wrong direction.

■ Sampling difficulties. Correctly defining, identifying, and contacting a truly representative sample is a difficult and time consuming task. However, in some types of research, the quality of results depends directly on the quality of the sample.

■ Inadequate data analysis. The researcher may analyze the data quickly and without the rigor that would otherwise be taken. Therefore, certain assumptions may not be considered, and important information within the data is simply not discovered.

Sometimes a researcher will have to submit to the time pressure and do a quick-and-dirty study. A sudden event can make it necessary to acquire data quickly—but rush jobs can some- times be avoided with proper planning of the research program. If it is necessary to conduct a study under severe time limitations, the researcher is obligated to point this out to management. The research report and presentation should include all the study limitations, including those that resulted from a shortage of time or money.

Intuitive Decision Making The fact of the matter is that managers are decision makers. They are action-oriented, and they often rely on gut reaction and intuition. Many times their intuition serves them well, so it isn’t surprising that they sometimes do not believe a research project will help improve their decision making. At other times, they resist research because it just may provide information that is counter to their intuition or their desires. They particularly abhor being held back while waiting for some research report.

‘‘Someone’s sitting in the shade today because someone planted a tree a long time ago.’’ —WARREN BUFFETT

Business researchers provide analyses and reports, but do deci- sion makers always listen and use that information? Recent research provides evidence that regardless of the “facts,” senior executives can make bad judgments, even when they are seek- ing to improve their company.

An Wang, CEO of Wang Laboratories, headed a company that dominated the computer word processing market. Despite clear and convincing evidence, he felt compelled to build a computer using a proprietary operating system, despite the IBM PC’s dominance at the time, and the fact that Microsoft had developed the primary operating system and not IBM. What drove this decision? Wang had a long distrust of IBM, which dated back to his own personal dislike for the company years before. This had perhaps clouded his judgment, which ulti- mately led to the demise of the company.

Scientists recognize that any decision maker is a victim of their own mental biases and stereotypes. Some of the biases

include making decisions based upon an overattachment to a particular plan, or even to a particular person. Other biases can include stereotypes about the importance of speed in making decisions, and an overreliance on emotion in mak- ing a decision. When making judgments, your brain can “trip you up,” by causing you to see patterns in the results that are not there, or when you use your past experiences to see the results you wish to see. Recognizing your own cognitive shortcomings can be an important step towards avoiding a bad decision.

Source: Campbell, Andrew, Jo Whitehead, and Syndey Finkelstein, “Why Good Leaders Make Bad Decisions,” Harvard Business Review (February 2009), 60–66.

When Your Brain “Trips Up”

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RESEARCH SNAPSHOT

If managers do use research, they often request simple projects that will provide concrete results with certainty. Researchers tend to see problems as complex questions that can be answered only within probability ranges. One aspect of this conflict is the fact that a research report provides find- ings, but cannot make decisions. Decision-oriented executives may unrealistically expect research to make decisions for them or provide some type of guarantee that the action they take will be correct. While research provides information for decision making, it does not always remove all the uncertainties involved in complex decisions. Certain alternatives may be eliminated, but the research may reveal new aspects of a problem. Although research is a valuable decision-making tool, it does not relieve the executive of the decision-making task.

Presentation of the right facts can be extremely useful. However, decision makers often believe that researchers collect the wrong facts. Many researchers view themselves as technicians who generate numbers using sophisticated mathematical and statistical techniques; they may spend more time on technical details than on satisfying managerial needs. Each person who has a nar- row perspective of another’s job is a partial cause of the problem of generating limited or useless information.

Consider this situation: An Internet retailer (Send.com) used a television ad to try to stimulate more gift purchasing among its customers. The spot centers on several men on the golf course drinking champagne. The “punch line” comes when one of the guys is hit in the groin. The voice over exclaims, “He just got hit in the little giver!”

A male executive may like punch lines like this. However, the audience for these ads is not all male. Had research been used to test these ideas prior to spending the money to produce the ads and buy the spots, it would have revealed that men didn’t respond as favorably as expected to these ads and women found them boorish.2 Thus, intuition has its limits as a replacement for informed research intelligence.3

Future Decisions Based on Past Experience Managers wish to predict the future, but researchers measure only current or past events. In 1957, Ford introduced the Edsel, one of the classic business failures of all time. One reason for the Edsel’s failure was that the research conducted several years before the car’s introduction indicated a strong demand for a medium-priced car for the “man on his way up.” By the time the car was introduced, however, consumer preference had shifted to two cars, one being a small import for the suburban wife. Not all research information is so dated, but all research describes what people have done in the past. In this sense, researchers use the past to predict the future. As seen in the Research Snapshot “When Your Brain ‘Trips Up’ ”, experiences can affect how decision makers see results.

Reducing the Conflict between Management and Researchers Given the conflicting goals of management and research, it is probably impossible to completely eliminate the conflict. However, when researchers and decision makers work more closely together, there will be less conflict. The more closely they work together, the better the communication between decision makers and researchers. In this way, business decision makers will better understand the information needs and work requirements of researchers. It will allow for better planning of research projects and a greater appreciation for the role that research plays in minimizing the riskiness of business decision making. Exhibit 5.4 lists some common areas of conflict between research and management. Many of these can be avoided through improved understanding of the other’s position.

With closer cooperation, managers are more involved with projects from the beginning. Early involvement increases the likelihood that managers will accept and act on the results. Researchers’ responsibility should be made explicit by a formal job description. Better planning and an annual statement of the research program for the upcoming year will help minimize emergency assign- ments, which usually waste resources and demoralize personnel.

Business researchers likewise will come to understand management’s perspective bet- ter. Researchers enhance company profits by encouraging better decisions. The closer together

82 • PART ONE Introduction

managers and researchers work, the more researchers realize that managers sometimes need infor- mation urgently. Thus, they should try to develop cost-saving research alternatives and realize that sometimes a quick-and-dirty study is necessary, even though it may not be as scientifically rigorous as might be desired. Sometimes, quick-and-dirty studies still provide usable and timely informa- tion. In other words, they should focus on results.

Perhaps most important is more effective communication of the research findings and research designs. The researchers must understand the interests and needs of the users of the research. If the researchers are sensitive to the decision-making orientation of management and can translate research performance into management language, organizational conflict will diminish.

A research generalist can effectively serve as a link between management and the research specialist. The research generalist acts as a problem definer, an educator, a liaison, a communicator, and a friendly ear. This intermediary could work with specialists who understand management’s needs and demands. The student with research skills who has a business degree seems most suited for this coordinating function.

Several strategies for reducing the conflict between management and research are possible. Managers generally should plan the role of research better, and researchers should become more decision-oriented and improve their communication skills (see Exhibit 5.5).4

EXHIBIT 5.4

Areas of Conflict between Top Management and Marketing Researchers

Area of Potential Conflict Top Management’s Position Business Researcher’s Position

Research responsibility Researchers lack a sense of accountability. The sole function of the researcher is to provide information.

The responsibility for research should be explicitly defined, and this responsibility should be consistently followed. The researcher should be involved with top management in decision making.

Research personnel Researchers are generally poor communicators who lack enthusiasm, skills, and imagination.

Top managers are anti-intellectual. Researchers should be hired, judged, and compensated on the basis of their research capabilities.

Budget Research costs too much. Since the research department’s contribution is difficult to measure, budget cuts in the department are defensible.

“You get what you pay for.” Research must have a continuing, long-term commitment from top management.

Assignments Projects tend to be overengineered and not executed with a sense of urgency. Researchers have a ritualized, staid approach.

Top managers make too many nonresearchable or emergency requests and do not allocate sufficient time or money.

Problem definition The researcher is best equipped to define the problem; it is sufficient for the top manager to give general direction. Top managers cannot help it if circumstances change. The researcher must appreciate this and be willing to respond to changes.

Researchers are often not given all the relevant facts about situations, which often change after research is under way. Top managers are generally unsympathetic to this widespread problem.

Research reporting Most reports are dull, use too much jargon and too many qualifiers, and are not decision oriented. Reports too often are presented after a decision has been made.

Top managers treat research reports superficially. Good research demands thorough reporting and documentation. Top managers give insufficient time to prepare good reports.

Use of research Top managers should be free to use research as they see fit. Changes in the need for and timing of research are sometimes unavoidable.

Top managers’ use of research to support a predetermined position or to confirm or excuse past decisions represents misuse. Also, it is wasteful to request research and then not use it after it has been conducted.

Source: Based on John G. Keane, “Some Observations on Marketing Research in Top Management Decision Making,” Journal of Marketing, October 1969, p. 13. Reprinted with permission from the Journal of Marketing, published by the American Marketing Association.

research generalist

An employee who serves as a link between management and research specialists. The research generalist acts as a problem definer, an educator, a liaison, a communicator, and a friendly ear.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 83

Cross-Functional Teams The ability to develop a successful decision making approach is often a function of the input of many different stakeholders. With improved communication, a more focused solution is possible. One way to encourage this is through cross-functional teams.

Cross-functional teams are composed of individuals from various functional areas such as engineering, production, finance, and marketing who share a common purpose. Cross-functional teams help organizations focus on a core business process, such as customer service or new- product development. Working in teams reduces the tendency for employees to focus single- mindedly on an isolated functional activity. Cross-functional teams help employees increase customer value since communication about their specific desires and opinions are better com- municated across the firm.

cross-functional teams

Employee teams composed of individuals from various

functional areas such as engineering, production,

finance, and marketing who share a common purpose.

EXHIBIT 5.5

Improving Two-Way Communication to Reduce Conflict

Business Research Position

Top Management

Define Research Responsibilities

Budget Realistically

Be Objective

Systematically Plan Research Programs

Avoid Quick-and-Dirty Studies

Emphasize High-Yield Projects

Understand Research Limitations

Minimize Management Filters

Effectively Communicate Research Results

Recognize Time Constraints

Provide Methodology of Consistent Quality

Communicate Research Point of View

Ensure That Information Needs Are Jointly Determined

Be Decision-Oriented, Not Technique-Oriented

Reflect Management Viewpoint

Treat Management as a Client

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84 • PART ONE Introduction

At trendsetting organizations, many research directors are members of cross-functional teams. New product development, for example, may be done by a cross-functional team of engineers, finance executives, production personnel, marketing managers, and staff researchers who take an integrated approach to solve a problem or exploit opportunities. In the old days, research may not have been involved in developing new products until long after many key decisions about product specifications and manufacturing had been made. Now researchers’ input is part of an integrated team effort. Researchers act both as business consultants and as providers of technical services. Researchers working in teams are more likely to understand the broad purpose of their research and less likely to focus exclusively on research methodology.

The effective cross-functional team is a good illustration of the business research concept in action. It reflects an effort to satisfy customers by using all the organization’s resources. Cross- functional teams are having a dramatic impact on views of the role of business research within the organization.

Research Suppliers and Contractors As mentioned in the beginning of the chapter, there are times when it makes good sense to obtain business research from an outside organization. In these cases, managers must interact with research suppliers, who are commercial providers of business and marketing research services. Business research is carried out by firms that may be variously classified as marketing and business research consulting companies, such as Burke, Market Facts, Inc., or The Freedonia Group; suppliers of syn- dicated research services, such as Experian Simmons as well as interviewing agencies, universities, and government agencies.

Syndicated Service No matter how large a firm’s research department is, some projects are too expensive to perform in-house. A syndicated service is a research supplier that provides standardized information for many clients in return for a fee. They serve as a sort of supermarket for standardized research results. For example, J. D. Power and Associates sells research about customers’ ratings of automobile qual- ity and their reasons for satisfaction. Most automobile manufacturers and their advertising agencies subscribe to this syndicated service because the company provides important industry-wide infor- mation it gathers from a national sample of thousands of car buyers. By specializing in this type of customer satisfaction research, J. D. Power gains certain economies of scale.

Syndicated services can provide expensive information economically to numerous clients because the information is not specific to one client but interests many. Such suppliers offer stan- dardized information to measure media audiences, wholesale and retail distribution data, and other forms of data.

Standardized Research Services Standardized research service companies develop a unique methodology for investigating a busi- ness specialty area. Several research firms, such as Verdict Research (http://www.verdict.co.uk/), pro- vide location services for retail firms. The Research Snapshot “Finding Häagen-Dazs in China” illustrates an interesting application for which an outside location service company may be par- ticularly useful. Research suppliers conduct studies for multiple, individual clients using the same methods.

Nielsen (http://www.nielsen.com) collects information throughout the new product develop- ment process, from initial concept screening through test- marketing. The BASES system can evalu- ate initiatives relative to other products in the competitive environment. For example, a client can compare its day-after recall scores with average scores for a product category.

research suppliers

Commercial providers of research services.

syndicated service

A research supplier that provides standardized information for many clients in return for a fee.

standardized research service

Companies that develop a unique methodology for investigating a business specialty area.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 85

custom research

Research projects that are tailored specifically to a client’s

unique needs.

Even when a firm could perform the research task in-house, research suppliers may be able to conduct the project at a lower cost, faster, and relatively more objectively. A company that wishes to quickly evaluate a new advertising strategy may find an ad agency’s research department is able to provide technical expertise on copy development research that is not available within the company itself. Researchers may be well advised to seek outside help when conducting research in a foreign country in which the necessary human resources and knowledge to effectively collect data are lacking. The Research Snapshot “Finding Häagen-Dazs in China” illustrates this situation.

Limited Research Service Companies and Custom Research Limited-service research suppliers specialize in particular research activities, such as syndicated service, field interviewing, data warehousing, or data pro- cessing. Full-service research suppliers sometimes contract these companies for ad hoc research projects. The client usually controls these agencies or management consulting firms, but the research supplier handles most of the operating details of custom research projects. These are projects that are tai- lored specifically to a client’s unique needs. A custom research supplier may employ individuals with titles that imply relationships with clients, such as account executive or account group manager, as well as functional special- ists with titles such as statistician, librarian, director of field services, director of tabulation and data processing, and interviewer.

Largest Research Organizations Exhibit 5.6 lists the top 15 suppliers of global research and their statistics from 2010. Just a few decades ago, the list would comprise solely firms based in the United States. Large research firms now base their operations in places around the globe including London, Tokyo, and Neuremberg. The explo- sion in global data availability will only add to the growth in the industry.

Ice cream lovers needn’t worry if they are sent on a business trip to China. Häagen-Dazs ice cream shops first appeared in Shang- hai, China, in 1996 and now there are dozens of Häagen-Dazs ice cream shops in coastal China, with plans for hundreds more. Clearly, many firms would like to follow Häagen-Dazs into China. China is expected to be the world’s largest consumer market by 2020. However, where should an ice cream shop be located in China? While location decisions can be difficult enough within the borders of one’s own country, imagine trying to decide where to put a shop in a huge, unfamiliar country.

Fortunately, standardized research companies have resources deployed all around the world that can synthesize geographic information system (GIS) information with survey research and other information to assist firms with location decisions in China and in other developing countries. Since U.S.-based retail firms may lack the necessary connections and knowledge (expertise) to efficiently conduct research in faraway places, the use of an

outside research provider not only saves time and money, but also yields higher quality results than an in-house study. Imagine how difficult language barriers could be when dealing with the Chinese consumer market.

And, as difficult as identifying good retail locations seems in China, other top emerging retail nations include India, Russia, and the Ukraine. As in China, American and European firms may find that using a research supplier to help with retail loca- tion issues in these countries is wiser than doing the research themselves.

Sources: “Häagen-Dazs in China,” China Business Review 31 (Jul/Aug 2004), 22; Hall, Cecily, “Spanning the Retail Globe,” WWD: Women’s Wear Daily 190 (July 21, 2005), 11.

Finding Häagen-Dazs in China

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Research in a foreign country is often better done by an outside agency with resources in those places.

RESEARCH SNAPSHOT

Most of these organizations provide various services ranging from designing activities to field- work. The services they can provide are not covered in detail here because they are discussed throughout the book, especially in the sections on fieldwork. However, here we briefly consider some managerial and human aspects of dealing with research suppliers. Clearly, the exhibit reveals that research is big business. Its growth will continue as data availability increases and as busi- nesses desire more precision in their decision making. Therefore, attractive career opportunities are numerous for those with the right skills and desires.

Ethical Issues in Business Research As in all human interactions, ethical issues exist in research. Our earlier discussion of potential organizational politics and the implication of different goals or perspectives introduced a situation where ethics can come into play. This book considers various ethical issues concerning fair busi- ness dealings, proper research techniques, and appropriate use of research results in other chapters. The remainder of this chapter addresses society’s and managers’ concerns about the ethical implica- tions of business research.

EXHIBIT 5.6

Top 15 Global Research Firms

Rank Organization Headquarters Home Country Website

Employees (Full-Time)

Number of Countries

Approximate Revenue ($ millions)

1 The Nielsen Co. New York USA nielson.com 33,100 100 4,628

2 Kantar London UK kantar.com 19,400 80 2,823

3 IMS Health Inc. Norwalk, CT USA imshealth.com 7,300 75 2,190

4 GfK Neuremberg Germany gfk.com 10,100 59 1,622

5 Ipsos New York USA ipsos.com 8,800 64 1,315

6 Westat Inc. Rockville, MD USA wesstat.com 2,100 1 1,000

7 Synovate London UK synovate.com 6,000 62 817

8 Symphony IRI Chicago, IL USA symphonyiri.com 700 8 706

9 Arbitron Inc. Columbia, MD USA arbitron.com 1,029 2 385

10 JD Power Westlake Village, CA

USA jdpower.com 800 8 370

11 INTAGE, Inc. Tokyo Japan intage.co.jp 2,000 3 369

12 NPD Group Inc. Port Washington, NY

USA npd.com 1,000 12 226

13 dunnhumbyUSA Cincinnati, OH USA dunnhumby.com 1,100 18 203

14 Video Research Ltd. Tokyo Japan videor.co.jp 400 3 201

15 Harris Interactive New York USA harrisinteractive.com 800 8 167

Source: Data taken from Honomichl, J., “Global Top 25: 2010 Honomichl Report,” Marketing News, (August 30, 2010), 16.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 87

Ethical Questions Are Philosophical Questions Ethical questions are philosophical questions. There are several philosophical theories that address how one develops a moral philosophy and how behavior is affected by morals. These include theo- ries about cognitive moral development, the bases for ethical behavioral intentions, and opposing moral values.5 While ethics remain a somewhat elusive topic, what is clear is that not everyone involved in business, or in fact involved in any human behavior, comes to the table with the same ethical standards or orientations.6

Business ethics is the application of morals to behavior related to the business environment or context. Generally, good ethics conforms to the notion of “right,” and a lack of ethics conforms to the notion of “wrong.” Highly ethical behavior can be characterized as being fair, just, and acceptable.7 Ethical values can be highly influenced by one’s moral standards. Moral standards are principles that reflect beliefs about what is ethical and what is unethical. More simply, they can be thought of as rules distinguishing right from wrong. The Golden Rule, “Do unto others as you would have them do unto you,” is one such ethical principle.

An ethical dilemma simply refers to a situation in which one chooses from alternative courses of actions, each with different ethical implications. Each individual develops a philosophy or way of thinking that is applied to resolve the dilemmas they face. Many people use moral standards to guide their actions when confronted with an ethical dilemma. Others adapt an ethical orientation that rejects absolute principles. Their ethics are based more on the social or cultural acceptabil- ity of behavior. If it conforms to social or cultural norms, then it is ethical. Relativism is a term that reflects the degree to which one rejects moral standards in favor of the acceptability of some action. This way of thinking rejects absolute principles in favor of situation-based evaluations. Thus, an action that is judged ethical in one situation can be deemed unethical in another. In contrast, idealism is a term that reflects the degree to which one bases one’s morality on moral standards. Someone who is an ethical idealist will try to apply ethical principles like the Golden Rule in all ethical dilemmas.

For example, a student may face an ethical dilemma when taking a test. Another student may arrange to exchange multiple choice responses to a test via electronic text messages. This represents an ethical dilemma because there are alternative courses of action each with differ- ing moral implications. An ethical idealist may apply a rule that cheating is always wrong and therefore would not be likely to participate in the behavior. An ethical relativist may instead argue that the behavior is acceptable because a lot of the other students will be doing the same. In other words, the consensus is that this sort of cheating is acceptable, so this student would be likely to go ahead and participate in the behavior. Researchers and business stake- holders face ethical dilemmas practically every day. The following sections describe how this can occur.

General Rights and Obligations of Concerned Parties Everyone involved in business research can face an ethical dilemma. For this discussion, we can divide those involved in research into three parties:

1. The people actually performing the research, who can also be thought of as the “doers” 2. The research client, sponsor, or the management team requesting the research, who can be

thought of as the “users” of research 3. The research participants, meaning the actual research respondents or subjects

Each party has certain rights and obligations toward the other parties. Exhibit 5.7 diagrams these relationships.

Like the rest of business, research works best when all parties act ethically. Each party depends on the other to do so. A client depends on the researcher to be honest in presenting research results. The researcher depends on the client to be honest in presenting the reasons for doing the research and in describing the business situation. Each is also dependent on the research participant’s honesty

business ethics

The application of morals to behavior related to the

exchange environment.

moral standards

Principles that reflect beliefs about what is ethical and what

is unethical.

ethical dilemma

Refers to a situation in which one chooses from alternative courses of actions, each with different ethical implications.

relativism

A term that reflects the degree to which one rejects moral

standards in favor of the acceptability of some action. This way of thinking rejects

absolute principles in favor of situation-based evaluations.

idealism

A term that reflects the degree to which one bases one’s

morality on moral standards.

88 • PART ONE Introduction

in answering questions during a research study. Thus, each is morally obligated toward the other. Likewise, each also has certain rights. The following section elaborates on the obligations and rights of each party.

Rights and Obligations of the Research Participant Most business research is conducted with the research participant’s consent. In other words, the participation is active. Traditional survey research requires that a respondent voluntarily answer questions in one way or another. This may involve answering questions on the phone, respond- ing to an e-mail request, or even sending a completed questionnaire by regular mail. In these cases, informed consent means that the individual understands what the researcher wants him or her to do and consents to the research study. In other cases, research participants may not be aware that they are being monitored in some way. For instance, a research firm may moni- tor superstore purchases via an electronic scanner. The information may assist in understanding how customers respond to promotions. However, no consent is provided since the participant is participating passively. The ethical responsibilities vary depending on whether participation is active or passive.

The Obligation to Be Truthful When someone willingly consents to participate actively, it is generally expected that he or she will provide truthful answers. Honest cooperation is the main obligation of the research participant. In return for being truthful, the subject has the right to expect confidentiality. Confidentiality means that information involved in the research will not be shared with others. When the respondent truly believes that confidentiality will be maintained, then it becomes much easier to respond truthfully, even about potentially sensitive topics. Likewise, the researcher and the research sponsor also may expect the respondent to maintain confidentiality. For instance, if the research involves a new food product from Nabisco, then they may not want the respondent to discuss the idea for fear that the idea may fall into the competition’s hands. Thus, confidentiality is a tool to help ensure truthful responses.

Participant’s Right to Privacy

ACTIVE RESEARCH Most people relish their privacy. Hence, the right to privacy is an important issue in business research. This issue involves the participant’s freedom to choose whether to comply with the inves- tigator’s request. Traditionally, researchers have assumed that individuals make an informed choice.

informed consent

When an individual understands what the researcher wants him or her to do and consents to the research study.

confidentiality

The information involved in a research study will not be shared with others.

Subject Researcher Client

Researcher’s Rights

Researcher’s Obligations

Researcher’s Rights

Researcher’s Obligations

Client’s RightsResearch Participant’s Rights

Client’s ObligationsSubject’s Obligations

Subject’s Rights

Client’s Obligations

EXHIBIT 5.7

Interaction of Rights and Obligations between Parties

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CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 89

However, critics have argued that the old, the poor, the poorly educated, and other underprivileged individuals may be unaware of their right to choose. They have further argued that an interviewer may begin with some vague explanation of a survey’s purpose, initially ask questions that are rela- tively innocuous, and then move to questions of a highly personal nature. It has been suggested that subjects be informed of their right to be left alone or to break off the interview at any time. Researchers should not follow the tendency to “hold on” to busy respondents. However, this view definitely is not universally accepted in the research community.

The privacy issue is illustrated by these questions:

■ Is a telephone call that interrupts family dinner an invasion of privacy? ■ Is an e-mail requesting response to a 30-minute survey an invasion of privacy?

Generally, interviewing firms practice common courtesy by trying not to interview late in the eve- ning or at other inconvenient times. However, the computerized random phone number interview has stimulated increased debate over the privacy issue. As a practical matter, respondents may feel more relaxed about privacy issues if they know who is conducting the survey. Thus, it is generally recommended that field interviewers indicate that they are legitimate researchers and name the company they work for as soon as someone answers the phone. For in-person surveys, interview- ers should wear official name tags and provide identification giving their name and the names of their companies.

Research companies should adhere to the principles of the “do-not-call” policy and should respect consumers’ “Internet privacy.” Do-not-call legislation restricts any telemarketing effort from calling consumers who either register with a no-call list in their state or who request not to be called. Legislators aimed these laws at sales-related calls. However, legislation in several states, including California, Louisiana, and Rhode Island, has extended this legislation to apply to “those that seek marketing information.” Thus, the legislation effectively protects consumers’ privacy from researchers as well as salespeople.8

Consumers often are confused about the difference between telemarketing efforts and true marketing or business research. Part of this is because telemarketers sometimes disguise their sales efforts by opening the conversation by saying they are doing research. The resulting confusion contributes to both increased refusal rates and lower trust. In 1980, a public opinion poll found that 19 percent of Americans reported having refused to participate in a marketing survey within the past year. Today, that number approaches 50 percent. In 2001, only 40 percent of Americans either agreed or strongly agreed that marketers will protect their privacy. That number is down from 50 percent in 1995.9

Companies using the Internet to conduct research also face legislative changes. Much of this legislation is aimed at making sure consumers are properly notified about the collection of data and to whom it will be distributed. Researchers should make sure that consumers are given a clear and easy way to either consent to participation in active research or to easily opt out. Furthermore, companies should ensure that the information consumers send via the Internet is secure.10

PASSIVE RESEARCH Passive research involves different types of privacy issues. Generally, it is believed that unobtrusive observation of public behavior in places such as stores, airports, and museums is not a serious inva- sion of privacy. This belief is based on the fact that the consumers are indeed anonymous in that they are never identified by name nor is any attempt made to identify them. They are “faces in the crowd.” As long as the behavior observed is typical of behavior commonly conducted in public, then there is no invasion of privacy. In contrast, recording behavior that is not typically conducted in public would be a violation of privacy. For example, hidden cameras recording people (without consent) taking showers at a health club, even if ultimately intended to gather information to help improve the shower experience, would be considered inappropriate.

Technology has also created new ways of collecting data passively that have privacy implica- tions. Researchers are very interested in consumers’ online behavior. For instance, the paths that consumers take while browsing the Internet can be extremely useful in understanding what kinds of information are most valued by consumers. Much of this information can be harvested and

do-not-call legislation

Restricts any telemarketing effort from calling consumers

who either register with a no-call list or who request not

to be called.

90 • PART ONE Introduction

entered into a data warehouse. Researchers sometimes have legitimate reasons to use this data, which can improve consumers’ ability to make wise decisions. In these cases, the researcher should gain the consumers’ consent in some form before harvesting information from their web usage patterns. Furthermore, if the information will be shared with other companies, a specific consent agreement is needed. This can come in the form of a question to which consumers respond yes or no, as in the following example:

From time to time, the opportunity to share your information with other companies arises and this could be very helpful in offering you desirable product choices. We respect your privacy, however, and if you do not wish us to share this information, we will not. Would you like us to share your information with other companies?

• Yes, you can share the information • No, please keep my information private

Not all of these attempts are legitimate. Most readers have probably encountered spyware on their home computer. Spyware is software that is placed on your computer without consent or knowledge while using the Internet. This software then tracks your usage and sends the infor- mation back through the Internet to the source. Then, based on these usage patterns, the user will receive push technology advertising, usually in the form of pop-up ads. Sometimes, the user will receive so many pop-up ads that the computer becomes unusable. The use of spyware is illegitimate because it is done without consent and therefore violates the right to privacy and confidentiality.

Legislators are increasingly turning their attention to privacy issues in data collection. When children are involved, researchers have a special obligation to insure their safety. COPPA, the Children’s Online Privacy Protection Act, was enacted into U.S. federal law on April 12, 2000. It defines a child as anyone under the age of 13. Anyone engaging in contact with a child through the Internet is obligated to obtain parental consent and notification before any personal information or identification can be provided by a child. Therefore, a researcher collecting a child’s name, phone number, or e-mail address without parental consent is violating the law. While the law and ethics do not always correspond, in this case, it is probably pretty clear that a child’s personal information shouldn’t be collected. The Research Snapshot “Crazy Good! Have Fun, Play Games (and Buy Pop-Tarts)!” further explains how conducting research with children is ethically complex.

Deception in Research Designs and the Right to Be Informed

EXPERIMENTAL DESIGNS Experimental manipulations often involve some degree of deception. In fact, without some decep- tion, a researcher would never know if a research subject was responding to the actual manipula- tion or to their perception of the experimental variable. This is why researchers sometimes use a placebo.

A placebo is a false experimental effect used to create the perception of a true effect. Imagine two consumers, each participating in a study of the effect of a new herbal supplement on hyperten- sion. One consumer receives a packet containing the citrus-flavored supplement, which is meant to be mixed in water and consumed with breakfast. The other also receives a packet, but in this case the packet contains a mixture that will simply color the water and provide a citrus flavor. The second consumer also believes he or she is drinking the actual supplement. In this way, the psy- chological effect is the same on both consumers, and any actual difference in hypertension must be due to the actual herbs contained in the supplement. Interestingly, experimental subjects often display some placebo effect in which the mere belief that some treatment has been applied causes some effect.

This type of deception can be considered ethical. Primarily, researchers conducting an experi- ment must generally (1) gain the willful cooperation of the research subject and (2) fully explain the actual experimental variables applied following the experiment’s completion. Every experiment should include a debriefing session in which research subjects are fully informed and provided a chance to ask any questions that they may have about the experiment.

placebo

A false experimental effect used to create the perception that some effect has been administered.

spyware

Software placed on a computer without consent or knowledge of the user.

debriefing

Research subjects are fully informed and provided with a chance to ask any questions they may have about the experiment.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 91

DESCRIPTIVE RESEARCH Researchers sometimes will even withhold the actual research questions from respondents in sim- ple descriptive research. A distinction can thus be made between deception and discreet silence. For instance, sometimes providing the actual research question to respondents is simply providing them more information than they need to give a valid response. A researcher may ask questions about the perceived price of a product when his or her real interest is in how consumers form quality impressions.

Protection from Harm Researchers should do everything they can to make sure that research participants are not harmed by participating in research. Most types of research do not expose participants to any harm. However, the researcher should consider every possibility. For example, if the research involves tasting food or drink, the possibility exists that a research participant could have a severe allergic reaction. Similarly, researchers studying retail and workplace atmospherics often manipulate odors by injecting certain scents into the air.11 The researcher is sometimes in a difficult situation. He or she has to somehow find out what things the subject is allergic to, without revealing the actual experimental conditions. One way this may be done is by asking the subjects to provide a list of potential allergies ostensibly as part of a separate research project.

Other times, research may involve some potential psychological harm. This may come in the form of stress or in the form of some experimental treatment that questions some strongly held conviction. For instance, a researcher studying helping behavior may lead a subject to believe that another person is being harmed in some way. In this way, the researcher can see how much a subject can withstand before doing something to help another person. In reality, the other person is usually a research confederate simply pretending to be in pain. Three key questions that can determine whether a research participant is being treated unethically as a result of experimental procedures are:

1. Has the research subject provided consent to participate in an experiment? 2. Is the research subject exposed to substantial physical or psychological trauma? 3. Can the research subject be easily returned to his or her initial state?

The online marketing of products and services to children has expanded exponentially. In the past, television was the primary advertising medium used to attract kids’ interest. The idea behind marketing to children on television was simple. Children would see advertised food as fun, which would encourage them to get their parents to buy the product. Several studies examining the ethical aspects of television advertising to chil- dren have challenged whether this was an appropriate way to sell food.

With the advent of the Internet and electronic gaming, advertising food products to children through online web- sites has reached new levels of sophistication. For example, at PopTarts.com kids can enter PopTartsWorld.com and can play online games, enter the “store,” and interactively create images and put their photo on a PopTarts box. Is this simply an online entertainment site, or something more?

Recent research indicates that these online sites contain “entertainment” that is also designed to communicate a careful

message—and that children may not rec- ognize that they are being exposed to a sophisticated market- ing tool that seeks to influence them (and ultimately their parents) into buying food products. Some ethical challenges in particular are the direct inducement to buy product, and the challenges of privacy protection for children. Long term, the benefits and downsides of online entertainment and the marketing of products and services to children are only now being understood.

Source: Moore, E. S. and V. J. Rideout, “The Online Marketing of Food to Children: Is It Just Fun and Games?” Journal of Public Policy & Marketing 26, no. 2 (2007), 202–220. Reprinted by permission.

Crazy Good! Have Fun, Play Games (and Buy Pop-Tarts)!

RESEARCH SNAPSHOT

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The issue of consent is tricky in experiments because the researcher cannot reveal exactly what the research is about ahead of time or the validity of the experiment will be threatened. In addition, experimental research subjects are usually provided some incentive to participate. We will have more on this in Chapter 12, but ethically speaking, the incentives should always be noncoercive. In other words, a faculty member seeking volunteers should not withhold a student’s grade if he or she does not participate in an experiment. Thus, the volunteer should provide consent without fear of harm for saying no and with some idea about any potential risk involved.

If the answer to the second question is yes, then the research should not be conducted. If the answer to the second question is no and consent is obtained, then the manipulation does not pres- ent an ethical problem, and the researcher can proceed.

The third question is helpful in understanding how far one can go in applying manipulations to a research subject. If the answer to the third question is no, then the research should not be conducted. For example, researchers who seek to use hypnosis as a means of understanding prefer- ences may be going too far in an effort to arrive at an answer. If the hypnotic state would cause the participant severe trauma, or if he or she cannot be easily returned to the prehypnotic state, then the research procedure should not be used. If, for instance, the consumer makes a large number of purchases under hypnosis, going deeply into debt, returning him or her to the original state may be difficult. If so, the application of hypnosis is probably inappropriate. If the answer to this question is yes, then the manipulation is ethical.

Many research companies and practically all universities now maintain a human subjects review committee. This is a committee that carefully reviews a proposed research design to try to make sure that no harm can come to any research participant. A side benefit of this committee is that it can also review the procedures to make sure no legal problems are created by implementing the particu- lar design. This committee may go by some other name such as internal review board, but despite the name difference, the function remains to protect the company from doing harmful research.

Rights and Obligations of the Researcher Research staff and research support firms should practice good business ethics. Researchers are often the focus of discussions of business ethics because of the necessity that they interact with the public. Several professional organizations have written and adopted codes of ethics for their researchers, including the American Marketing Association (AMA), the European Society for Opinion and Market Research (ESOMAR), the Marketing Research Society (MRS), and the Council of American Survey Research Organizations (CASRO). In reaction to the growing use of online surveying techniques, CASRO has provided guidelines for those conducting and participat- ing in online research, presented in Exhibit 5.8.

In addition, the researchers have rights. In particular, once a research consulting firm is hired to conduct some research, they have the right to cooperation from the sponsoring client. In addition, the researchers have the right to be paid for the work they do as long as it is done professionally. Sometimes, the client may not like the results. But not liking the results is no basis for not paying. In addition, the client should pay the researcher in full and in a timely manner.

Research That Isn’t Research

MIXING SALES OR FUND-RAISING WITH RESEARCH Consumers sometimes agree to participate in an interview that is purported to be pure research, but it eventually becomes obvious that the interview is really a sales pitch in disguise. The research industry refers to this practice as sugging, meaning “selling under the guise of research.” Closely related is the concept of frugging, or “fund-raising under the guise of research.” These are unpro- fessional at best and fraudulent at worst. The Federal Trade Commission (FTC) has indicated that it is illegal to use any plan, scheme, or ruse that misrepresents the true status of a person seeking admission to a prospect’s home, office, or other establishment. No research firm should engage in any sales or fund-raising attempts. Applied market researchers working for the sponsoring company should also avoid overtly mixing research and sales. However, the line is becoming less clear with increasing technology.

human subjects review committee

Carefully reviews proposed research designs to try to make sure that no harm can come to any research participant.

sugging

Selling under the guise of research.

frugging

Fund-raising under the guise of research.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 93

PSEUDO-RESEARCH Consider the vignette that opened this chapter. Despite his best efforts, John is clearly feeling pressure to justify certain results obtained from his study. In fact, it is easy to see what is actually going on. The CEO really wants research that will justify and support a decision and investment that already has been made. If the customer perceptions contradict the decision, a manager will often disregard the research. This isn’t really research so much as it is pseudo-research because it is conducted not to gather information for decisions but to bolster a point of view and satisfy other needs.

The most common type of pseudo-research is performed to justify a decision that has already been made or that management is already strongly com- mitted to. A media company may wish to sell adver- tising space on Internet search sites. Even though they strongly believe that the ads will be worth the rates they will charge advertisers, they may not have the hard evidence to support this view. For example, an advertiser’s sales force may provide feedback indi- cating customer resistance to moving their advertis- ing from local radio to the Internet. The advertising company may then commission a study for which the only result they care to find is that the Internet ads will be effective. In this situation, a researcher should walk away from the project if it appears that management strongly desires the research to support a predetermined opinion only. While it is a fairly

EXHIBIT 5.8

CASRO Online Survey Standards

Guidelines for Participating in Online Surveys

Use of the internet to conduct surveys has surged in recent years, and riding this trend are some dishonest or fraudulent research “pretenders.” These “companies” make empty promises of “earning high income as a survey respondent,” or worse, they may attempt to steal your money or your personal identity.

It is important to remember that the vast majority of online surveys are managed by legitimate companies and that your participation in these surveys is important—and they can also be a fun way to win prizes. The Council of American Survey Research Organizations (CASRO) offers the following guidelines to the public in an effort to help you recognize a legitimate and professional online survey and thereby avoid the potential risk to your money and your pri- vacy from dishonest practitioners.

Legitimate Survey Research Companies Will:

1. Have contact information on their website, and a repre- sentative available to answer questions/concerns

2. Limit the number of surveys you can take 3. Ask participants to answer screening questions to see if

they meet demographic requirements needed for their research sample

4. Value the time you take to complete a survey by offering modest incentives and “thank yous” such as sweep- stakes entries, coupons or a nominal monetary reward. Remember, if the incentive seems too good to be true - it probably is.

5. Generally affiliate themselves with a trade group. (Most associations require members to meet certain ethical standards)

6. Have accessible terms and conditions and a privacy policy 7. Honor your request to discontinue sending e-mail invi-

tations for surveys or to remove you from their online research panel

Legitimate Companies Won’t:

1. Request credit card or bank account information or your Social Security number

2. Try to sell or promote a product 3. Request payment for anything 4. Try to recruit you with a one-question survey (e.g.,

“What’s your favorite color?”) in a pop-up ad or unsolic- ited e-mail

5. Contact or seek personal information from any child under the age of 13 without a parent’s prior, verifiable consent

Source: http://www.casro.org/survandyou-guide.cfm, accessed April 30, 2011.

pseudo-research

Conducted not to gather information for marketing

decisions but to bolster a point of view and satisfy other needs.

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94 • PART ONE Introduction

easy matter for an outside researcher to walk away from such a job, it is another matter for an in-house researcher to refuse such a job. Thus, avoiding pseudo-research is a right of the researcher but an obligation for the manager.

Occasionally, research is requested simply to pass blame for failure to another area. A human resource manager may request a research study with no intention of paying attention to the find- ings and recommendations. The manager may know that the new health care plan is not desirable, but plays the standard game to cover up for his or her mismanagement. If the project fails, market- ing research will become the scapegoat. The ruse may involve a statement something like: “Well, research should have identified the problem earlier!”

Also, technology is making the line between research and sales less clear. It is very likely that research data collected by companies we transact with online could be used to push products toward us that we may truly like. This is the point of push technology. What makes this ethical or not ethical? With consent, it is clearly ethical. What other ethical challenges may be faced as the technology to collect consumer information continues to develop?

PUSH POLLS Politicians concocted and specialize in a particular type of pseudo-research. A push poll is telemarketing under the guise of research intended to “sell” a particular political position or point of view. The purpose of the poll is to push consumers into a predetermined response. For instance, a polling organization calls thousands of potential voters inviting each to participate in a survey.

Sometimes, the application of research procedures to research participants can present significant ethical issues that cannot be easily dismissed by a single researcher alone. This is where a peer review process takes place. A human subjects research committee consists of a panel of researchers (and sometimes a legal authority) who carefully review the proposed procedures to identify any obvious or nonobvious ethical or legal issues. In fact, any research supported by U.S. federal funds must be sub- ject to a peer review of this type. The peer review process for grants is described at this website: http://grants.nih.gov/grants/ peer/peer.htm.

Most business research is innocuous and affords little oppor- tunity for substantial physical or psychological trauma. However, companies involved in food marketing, dietary supplements or programs, and exercise physiology and pharmaceuticals, among others, do conduct consumer research with such possibilities. Academic researchers also sometimes conduct research with significant risks for participants. Consider research examining how some dietary supplement might make exercise more enjoy- able, thus creating a better overall health and psychological effect. Clearly, a peer review by knowledgeable researchers is needed before proceeding with such research.

As it isn’t possible to completely eliminate risk from research, a human subjects review is a good safety net. Deaths have been attributed to lack of or the breakdown of the human subjects review. Some of these have brought negative publicity to well-known universities including the University of Pennsylvania and Johns Hopkins University. At other times,

the risk to research participants is not obvious. For example, recently several researchers were interested in surveying through personal interviews victims of Hurricane Katrina. The results of the research may help public entities better serve victims, allow companies to respond with more appropriate goods and services, and help build psychological theory about how consumers make decisions under conditions of high personal trauma and stress. However, is it ethical to survey participants standing in the rubble of their home? Is it ethical to survey participants who are in the process of searching for or burying relatives that did not survive the disaster? Clearly, a thorough review of the procedures involved in such situations is called for.

Corporate human subjects committees are also becoming common. These reviews also consider the possibility of legal problems with experimental or survey procedures. In addition, as technology blurs the line between research and sales, they also should review the ethics of “research” that may somehow blend with sales. In addition, research conducted on animals also needs a critical review.

Sources: Glenn, David, “Lost (and Found) in the Flood,” Chronicle of Higher Education 52 (October 7, 2005), A14–A19; Putney, S. B. and S. Gruskin, “Time, Place and Consciousness: Three Dimensions of Meaning for U.S. Institutional Review Boards,” American Journal of Public Health 92 (July 2002), 1067–1071.

Is It Right, or Is It Wrong?

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RESEARCH SNAPSHOT

push poll

Telemarketing under the guise of research.

The interviewer then may ask loaded questions that put a certain spin on a candidate. “Do you think that candidate X, who is involved with people known to be linked to scandal and crime, can be trusted with the responsibility of office?” This is a push poll. An honest question may simply ask how much candidate X can be trusted.

Push polling doesn’t always involve political candidates. Residents do not always welcome new Walmart locations based on factors including the impact on existing businesses and the increase in traffic and congestion that the store can bring to an area. In 2009, residents of Chicago received a polling call that went something like this:

Mayor Daley says that the proposed Walmart would bring over 400 jobs to this area and allow neighbor- hood residents access to fresh food. Do you support the new Walmart store’s construction?

The results indicated that over 70 percent of residents supported the new store.12 However, the framing of the question by referencing the mayor and the mention of jobs, without mentioning potential problems, almost certainly swayed the results.

SERVICE MONITORING Occasionally, the line between research and customer service isn’t completely clear. For instance, Toyota may survey all of its new car owners after the first year of ownership. While the survey appears to be research, it may also provide information that could be used to correct some issue with the customer. For example, if the research shows that a customer is dissatisfied with the way the car handles, Toyota could follow up with the specific customer. The follow-up could result in changing the tires of the car, resulting in a smoother and quieter ride, as well as a more satisfied customer. Should a pattern develop showing other customers with the same opinion, Toyota may need to switch the original equipment tires used on this particular car.

In this case, both research and customer service is involved. Since the car is under warranty, there would be no selling attempt. Researchers are often asked to design satisfaction surveys. These may identify the customer so they may be contacted by the company. Such practice is acceptable as long as the researcher allows the consumer the option of either being contacted or not being contacted. In other words, the customer should be asked whether it is okay for someone to follow up in an effort to improve their satisfaction.

Selling under the guise of research, fund-raising under the guise of research, pseudo-research, and push polls are all misrepresentations of the true purpose of research and should be avoided. It is important that researchers understand the only accepted purpose of research is research.

Objectivity The need for objective scientific investigation to ensure accuracy is stressed throughout this book. Researchers should maintain high standards to be certain that their data are accurate. Furthermore, they must not intentionally try to prove a particular point for political purposes.

Misrepresentation of Research It should go without saying, but research results should not be misrepresented. This means, for instance, that the statistical accuracy of a test should be stated precisely and the meaning of findings should not be understated or overstated. Both the researcher and the client share this obligation. There are many ways that research results can be reported in a less than full and honest way. For example, a researcher may present results showing a relationship between advertising spending and sales. However, the researcher may also discover that this relationship disappears when the primary competitors’ prices are taken into account. In other words, the relationship between advertising spending and sales is made spurious by the competitors’ prices. Thus, it would be questionable to say the least to report a finding suggesting that sales could be increased by increasing ad spending without also mentioning the spurious nature of this finding.

HONESTY IN PRESENTING RESULTS Misrepresentation can also occur in the way results are presented. For instance, charts can be cre- ated that make a very small difference appear very big. Likewise, they can be altered to make a

96 • PART ONE Introduction

meaningful difference seem small. Exhibit 5.9 illustrates this effect. Each chart presents exactly the same data. The data represent consumer responses to service quality ratings and satisfaction ratings. Both quality and satisfaction are collected on a five-point strongly-disagree-to-strongly-agree scale. In frame A, the chart appears to show meaningful differences between men and women, particu- larly for the service-quality rating. However, notice that the scale range is shown as 4 to 5. In frame B, the researcher presents the same data but shows the full scale range (1 to 5). Now, the differences are reported as trivial.

All charts and figures should reflect fully the relevant range of values reported by respon- dents. If the scale range is from 1 to 5, then the chart should reflect a 1 to 5 range unless there is some value that is simply not used by respondents. If no or only a very few respondents had reported a 1 for their service quality or satisfaction rating, then it may be appropriate to show the range as 2 to 5. However, if there is any doubt, the researcher should show the full scale range.

4.2

4.4

4.6

4.8

Quality* Satisfaction*

Men Women

A) Researcher points to a “large” difference in quality ratings. Men report much higher quality service than do women. In contrast,

women report only “slightly” less satisfaction than do men.

1.5

2.5

3.5

4.5

Quality* Satisfaction*

Men Women

B) The researcher points to a “trivial” difference in quality percep- tions between men and women. There is no statistical difference

in the satisfaction ratings of men and women.

EXHIBIT 5.9

How Results Can Be Misrepresented in a Report or Presentation

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CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 97

HONESTY IN REPORTING ERRORS Likewise, any major error that has occurred during the course of the study should not be kept secret from management or the sponsor. Hiding errors or variations from the proper procedures tends to distort or shade the results. Similarly, every research design presents some limitations. For instance, the sample size may be smaller than ideal. The researcher should point out the key limita- tions in the research report and presentation. In this way, any factors that qualify the findings can be understood. The decision maker needs this information before deciding on any risky course of action.

Confidentiality Confidentiality comes into play in several ways. The researcher often is obligated to protect the confidentiality of both the research sponsor and the research participant. In fact, business clients value researchers’ confidentiality more than any other attribute of a research firm.13 Imagine a researcher conducting a test-market for a new high-tech Apple iPod device that allows interactive video. Just after conducting the research, the same researcher is contacted by Samsung. Samsung, which has yet to develop video capability, wants research that addresses whether or not there is a market for iPod video of any type. The researcher is now in a difficult position. Certainly, an ethical dilemma exists presenting multiple choices to the researcher, including:

■ Agreeing to do the research for Samsung and using some results from the Apple study to pre- pare a report and recommendation for Samsung

■ Agreeing to sell the new concept to Samsung without doing any additional research. In other words, provide Apple’s company secrets to Samsung

■ Conducting an entirely new project for Samsung without revealing any of the results or ideas from the Apple study

■ Turning down the chance to do the study without revealing any information about Apple to Samsung

Which is the best choice? Obviously, both of the first two options violate the principle of main- taining client confidentiality. Thus, both are unethical. The third choice, conducting an entirely new study, may be an option. However, it may prove nearly impossible to do the entire project as if the Apple study had never been done. Even with the best of intentions, the researcher may inadvertently violate confidentiality with Apple. The last choice is the best option from a moral standpoint. It avoids any potential conflict of interest. In other words, actions that would best serve one client, Samsung, would be detrimental to another client, Apple. Generally, it is best to avoid working for two direct competitors.

Likewise, the researcher must also predict any confidentiality agreement with research partici- pants. For instance, a researcher conducting a descriptive research survey may have identified each participant’s e-mail address in the course of conducting the research. After seeing the results, the client may ask for the e-mail addresses as a logical prospect list. However, as long as the researcher assured each participant’s confidentiality, the e-mail addresses cannot ethically be provided to the firm. Indeed, a commitment of confidentiality also helps build trust among survey respondents.14

Dissemination of Faulty Conclusions The American Marketing Association’s marketing research Code of Ethics states that “a user of research shall not knowingly disseminate conclusions from a given research project or service that are inconsistent with or not warranted by the data.” A dramatic example of a violation of this principle occurred in an advertisement of a cigarette smoker study. The advertisement compared two brands and stated that “of those expressing a preference, over 65 percent preferred” the advertised brand to a competing brand. The misleading portion of this reported result was that most of the respondents did not express a preference; they indicated that both brands tasted about the same. Thus, only a very small percentage of those studied actually revealed a preference, and the results were somewhat misleading. Such shading of results violates the obligation to report accurate findings.

conflict of interest

Occurs when one researcher works for two competing

companies.

‘‘He uses statistics as a drunken man uses a lamppost—for support rather than illumination.’’ —ANDREW LANG

98 • PART ONE Introduction

Rights and Obligations of the Client Sponsor (User) Ethical Behavior between Buyer and Seller The general business ethics expected between a purchasing agent and a sales representative should hold in a marketing research situation. For example, if a purchasing agent has already decided to purchase a product from a friend, it would be unethical for that person to solicit competitive bids from others because they have no chance of being accepted. Similarly, a client seeking research should only seek bids from firms that have a legitimate chance of actually doing the work. In addition, any section on the ethical obligation of a research client would be remiss not to mention that the user is obligated to pay the provider the agreed upon wage and pay within the agreed upon time.

An Open Relationship with Research Suppliers The client sponsor has the obligation to encourage the research supplier to objectively seek out the truth. To encourage this objectivity, a full and open statement of the decision situation, a full dis- closure of constraints in time and money, and any other insights that assist the researcher should be provided. This means that the researcher will be provided adequate access to key decision makers. These decision makers should agree to openly and honestly discuss matters related to the situation. Finally, this means that the client is open to actually using the research results. Time is simply too valuable to ask a researcher to perform a project when the results will not be used.

An Open Relationship with Interested Parties Conclusions should be based on data—not conjecture. Users should not knowingly disseminate conclusions from a research project in a manner that twists them into a position that cannot be supported by the data. Twisting the results in a self-serving manner or to support some political position poses serious ethical questions. A user may also be tempted to misrepresent results while trying to close a sale. Obviously, this is also morally inappropriate.

Advocacy research—research undertaken to support a specific claim in a legal action or to represent some advocacy group—puts a client in a unique situation. Researchers often conduct advocacy research in their role as an expert witness. For instance, a researcher may be deposed to present evidence showing that a “knock-off ” brand diminishes the value of a better known name brand. In conventional research, attributes such as sample size, profile of people actually inter- viewed, and number of questions asked are weighed against cost in traditional research. However, a court’s opinion on whether research results are reliable may be based exclusively on any one specific research aspect. Thus, the slightest variation from technically correct procedures may be magnified by an attorney until a standard research result or project no longer appears adequate in a judge’s eyes. How open should the client be in the courtroom?

The ethics of advocacy research present a number of serious issues that can lead to an ethical dilemma:

■ Attorneys’ first responsibility is to represent their clients. Therefore, they might not be inter- ested as much in the truth as they are in evidence that supports their client’s position. Present- ing accurate research results may harm the client.

■ A researcher should be objective. However, he or she runs the risk of conducting research that does not support the desired position. In this case, the lawyer may ask the researcher if the results can somehow be interpreted in another manner.

■ Should the attorney (in this case a user of research) ask the researcher to take the stand and present an inaccurate picture of the results?

Ethically, the attorney should certainly not put the researcher on the stand and encourage an act of perjury. The attorney may hope to ask specific questions that are so limited that taken alone, they may appear to support the client. However, this is risky because the opposing attorney likely also

advocacy research

Research undertaken to support a specific claim in a legal action or represent some advocacy group.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 99

has an expert witness that can suggest questions for cross-examination. Returning to our branding example, if the research does not support an infringement of the known brand’s name, then the brand name’s attorney should probably not have the researcher take the stand.

Advocacy researchers do not necessarily bias results intentionally. However, attorneys rarely submit advocacy research evidence that does not support their clients’ positions.

The question of advocacy research is one of objectivity: Can the researcher seek out the truth when the sponsoring client wishes to support its position at a trial? The ethical question stems from a conflict between legal ethics and research ethics. Although the courts have set judicial stan- dards for research methodology, perhaps only the client and individual researcher can resolve this question.

Privacy People believe the collection and distribution of personal information without their knowledge is a serious violation of their privacy. The privacy rights of research participants create a privacy obligation on the part of the research client. Suppose a database marketing company is offering a mailing list compiled by screening millions of households to obtain brand usage information. The information would be extremely valuable to your firm, but you suspect those individuals who filled out the information forms were misled into thinking they were participating in a survey. Would it be ethical to purchase the mailing list? If respondents have been deceived about the purpose of a survey and their names subsequently are sold as part of a user mailing list, this practice is certainly unethical. The client and the research supplier have the obligation to maintain respondents’ privacy.

Consider another example. Sales managers know that a research survey of their business- to-business customers’ buying intentions includes a means to attach a customer’s name to each questionnaire. This confidential information could be of benefit to a sales representative calling on a specific customer. A client wishing to be ethical must resist the temptation to identify those accounts (that is, those respondents) that are the hottest prospects.

Privacy on the Internet Privacy on the Internet is a controversial issue. A number of groups question whether website questionnaires, registration forms, and other means of collecting personal information will be kept confidential. Many business researchers argue that their organizations don’t need to know who the user is because the individual’s name is not important for their purposes. However, they do want to know certain information (such as demographic characteristics or product usage) associated with an anonymous profile. For instance, a web advertiser could reach a targeted audience without having access to identifying information. Of course, unethical companies may violate anonymity guidelines. Research shows that consumers are sensitive to confidentiality notices before provid- ing information via a website. Over 80 percent of consumers report looking for specific privacy notices before they will exchange information electronically. In addition, over half believe that companies do not do enough to ensure the privacy of personal information.15 Thus, research users should not disclose private information without permission from the consumers who provided that information.

A Final Note on Ethics Certainly, there are researchers who would twist results for a client or who would fabricate results for personal gain. However, these are not professionals. When one is professional, one realizes that one’s actions not only have implications for oneself but also for one’s field. Indeed, just a few unscrupulous researchers can give the field a bad name. Thus, researchers should maintain the highest integrity in their work to protect our industry. Research participants should also play

100 • PART ONE Introduction

their role, or else the data they provide will not lead to better products for all consumers. Finally, the research users must also follow good professional ethics in their treatment of researchers and research results.

SUMMARY

1. Know when research should be conducted externally and when it should be done internally. The company that needs the research is not always the best company to actually perform the research. Sometimes it is better to use an outside supplier of some form. An outside agency is better when a fresh perspective is needed, when it would be difficult for inside research- ers to be objective, and when the outside firm has some special expertise. In contrast, it is better to do the research in-house when it needs to be done very quickly, when the project requires close collaboration of many employees within the company, when the budget for the project is limi- ted, and when secrecy is a major concern. The decision to go outside or stay inside for research depends on these particular issues.

2. Be familiar with the types of jobs, job responsibilities, and career paths available within the business research industry. The business research function may be organized in any number of ways depending on a firm’s size, business, and stage of research sophistication. Business research managers must remember they are managers, not just researchers.

Research offers many career opportunities. Entry-level jobs may involve simple tasks such as data entry or performing survey research. A research analyst may be the next step on the career path. This position may involve project design, preparation of proposals, data analysis, and inter- pretation. Whereas there are several intermediate positions that differ depending on whether one works for a small or large firm, the director of research is the chief information officer in charge of marketing information systems and other research projects. The director plans, executes, and controls the research function for the firm.

3. Understand the often conflicting relationship between management and researchers. Researchers and managers have different and often conflicting goals. Some of the key sources of conflict include money, time, intuition, and experience. Managers want to spend the least amount of money on research possible, have it done in the shortest period of time con- ceivable, and believe that intuition and experience are good substitutes for research. Researchers will exchange greater expense for more precision in the research, would like to take more time to be more certain of results, and are hesitant to rely on intuition and experience. Better com- munication is a key to reducing this conflict. One tool that can be useful is the implementation of cross-functional teams.

4. Define ethics and understand how it applies to business research. Business ethics is the application of morals to behavior related to the exchange environment. Generally, good ethics conforms to the notion of “right” and a lack of ethics conforms to the notion of “wrong.” Those involved in research face numerous ethical dilemmas. Researchers serve clients or, put another way, the doers of research serve the users. It is often easy for a doer to compromise professional standards in an effort to please the user. After all, the user pays the bills. Given the large number of ethical dilemmas involved in research, ethics is highly applicable to business research.

5. Know and appreciate the rights and obligations of a) research respondents—particularly children, b) business researchers, and c) research clients or sponsors. Each party involved in research has certain rights and obligations. These are generally interdependent in the sense that one party’s right often leads to an obligation for another party. While the rights and obligations of all three parties are important, the obligation of the researcher to protect research participants is particularly important. Experimental manipulations can sometimes expose subjects to some form of harm or involve them in a ruse. The researcher must be willing to fully inform the subjects of the true purpose of the research during a debriefing. The researcher must also avoid subjecting participants to undue physical or psychological trauma. In addition, it should be reasonably easy to return an experimental subject to his or her original, pre-experiment condition.

6. Know how to avoid a conflict of interest in performing research. A conflict of interest occurs when a researcher is faced with doing something to benefit one client at the expense of another client. One good way to avoid a conflict of interest is to avoid getting involved with multiple projects involving competing firms.

CHAPTER 5 The Human Side of Business Research: Organizational and Ethical Issues • 101

QUESTIONS FOR REVIEW AND CRITICAL THINKING

1. What are the conditions that make in-house research preferable? What are the conditions that make outside research preferable?

2. Read a recent news article from the Wall Street Journal or other key source that deals with a new product introduction. Would you think it would be better for that firm to do research in-house or to use an outside agency? Explain.

3. What might the organizational structure of the research department be like for the following organizations? a. A large advertising agency b. A founder-owned company that operates a 20-unit

restaurant chain c. Your university d. An industrial marketer with four product divisions e. A large consumer products company

4. What problems do research directors face in their roles as managers? 5. What are some of the basic causes of conflict between manage-

ment and research? 6. Comment on the following situation: A product manager asks

the research department to forecast costs for some basic ingre- dients (raw materials) for a new product. The researcher asserts that this is not a research job; it is a production forecast.

7. What is the difference between research and pseudo-research? Cite several examples of each.

8. ETHICS What are business ethics? How are ethics relevant to research?

9. ETHICS What is the difference between ethical relativism and eth- ical idealism? How might a person with an idealist ethical philo- sophy and a person with a relativist ethical philosophy differ with respect to including a sales pitch at the end of a research survey?

10. ETHICS What obligations does a researcher have with respect to confidentiality?

11. How should a researcher help top management better under- stand the functions and limitations of research?

12. ETHICS List at least one research obligation for research partici- pants (respondents), researchers, and research clients (sponsors)?

13. ETHICS What is a conflict of interest in a research context? How can such conflicts of interest be avoided?

14. ETHICS What key questions help resolve the question of whether or not research participants serving as subjects in an experiment are treated ethically?

15. Identify a research supplier in your area and determine what syndicated services and other functions are available to clients.

16. ‘NET Use the Internet to find at least five research firms that per- form survey research. List and describe each firm briefly.

17. What actions might the business research industry take to con- vince the public that research is a legitimate activity and that firms that misrepresent their intentions and distort findings to achieve their aims are not true research companies?

18. ETHICS Comment on the ethics of the following situations: a. A food warehouse club advertises “savings up to

30 percent” after a survey showed a range of savings from 2 to 30 percent below average prices for selected items.

b. A radio station broadcasts the following message during a syndicated rating service’s rating period: “Please fill out your diary” (which lists what media the consumer has been watching or listening to).

c. A furniture retailer advertises a market test and indicates that prices will be 50 percent off for three days only.

d. A researcher tells a potential respondent that an interview will last 10 minutes rather than the 30 minutes he or she actually anticipates.

e. A respondent tells an interviewer that she wishes to cooper- ate with the survey, but her time is valuable and, therefore, she expects to be paid for the interview.

f. When you visit your favorite sports team’s home page on the web, you are asked to fill out a registration question- naire before you enter the site. The team then sells your information (team allegiance, age, address, and so on) to a company that markets sports memorabilia via catalogs and direct mail.

KEY TERMS AND CONCEPTS advocacy research, 99 business ethics, 88 confidentiality, 89 conflict of interest, 98 cross-functional teams, 84 custom research, 86 debriefing, 91 do-not-call legislation, 90 ethical dilemma, 88 forecast analyst, 78

frugging, 93 human subjects review committee, 93 idealism, 88 in-house research, 74 informed consent, 89 manager of decision support systems, 78 moral standards, 88 outside agency, 74 placebo, 91 pseudo-research, 94

push poll, 95 relativism, 88 research analyst, 78 research assistants, 78 research generalist, 83 research suppliers, 85 spyware, 91 standardized research service, 85 sugging, 93 syndicated service, 85

102 Part 1: Introduction

Global Eating Barton Boomer, director of marketing research for a large research firm, has a bachelor’s degree in market- ing from Michigan State University. He joined the

firm nine years ago after a one-year stint as a research trainee at the corporate headquarters of a western packing corporation. Barton has a wife and two children. He earns $60,000 a year and owns a home in the suburbs. He is a typical research analyst. He is asked to interview an executive with a local restaurant chain, Eats-R-Wee. Eats-R-Wee is expanding internationally. The logical two choices for expansion are either to expand first to other nations that have values similar to those in the market area of Eats-R-Wee or to expand to the nearest geographical neighbor. During the initial interviews, Mr. Big, Vice President of Operations for Eats-R-Wee, makes several points to Barton.

• “Barton, we are all set to move across the border to Ontario and begin our international expansion with our neighbor to the north, Canada. Can you provide some research that will support this position?”

• “Barton, we are in a hurry. We can’t sit on our hands for weeks waiting to make this decision. We need a comprehensive research project completed by the end of the month.”

• “We are interested in how our competitors will react. Have you ever done research for them?”

• “Don’t worry about the fee; we’ll pay you top money for a ‘good’ report.”

Marla Madam, Barton’s Director of Research, encourages Barton to get back in touch with Mr. Big and tell him that the project will get underway right away.

Question 1. Critique this situation with respect to Barton’s job. What

recommen dations would you have for him? Should the company get involved with the research? Explain your answers.

CASE 5.1

RESEARCH ACTIVITIES 1. Find the mission statement of Burke, Inc. (http://www.burke

.com). What career opportunities exist at Burke? Would you consider it a small, midsized, or large firm?

2. ‘NET—ETHICS One purpose of the United Kingdom’s Market Research Society is to set and enforce the ethical standards to be

observed by research practitioners. Go to its website at www.mrs.org.uk. Click on its code of conduct and evaluate it in light of the AMA’s code.

19. ETHICS Comment on the following interview:

Interviewer: Good afternoon, sir. My name is Mrs. Johnson, and I am with Counseling Services. We are conducting a survey concerning perceptions of Memorial Park Cemetery. Please answer the following questions with “yes” or “no.”

Respondent: (pauses) Interviewer: Are you familiar with Memorial Park? Respondent: Yes. Interviewer: Do you consider Memorial Park to be one of the best

maintained cemeteries in this area?

Respondent: Yes. Interviewer: You do own a funeral plot? Respondent: No. Interviewer: Would you mind if I sent you a letter concerning

Memorial Park and the availability of funeral plots? Would you please give me your address?

20. ETHICS Try to participate in a survey at a survey website such as http://www.mysurvey.com or http://www.themsrgroup .com. Write a short essay response about your experience with particular attention paid to how the sites have protections in place to prevent children from providing personal information.

Chapter 5: The Human Side of Business Research: Organizational and Ethical Issues 103

Big Brother Is Watching? Technology is making our behavior more and more difficult to keep secret. Right at this very moment, there is probably some way that your location can be

tracked in a way that researchers could use the information. Do you have your mobile phone with you? Is there an RFID tag in your shirt, your backpack, or some other personal item? Are you in your car, and does it have a GPS (Global Positioning Satellite) device? All of these are ways that your location and movements might be tracked.

For instance, rental cars can be tracked using GPS. Suppose a research firm contracts with an insurance firm to study the way people drive when using a rental car. A customer’s every movement is then tracked. So, if the customer stops at a fast-food restaurant, the researcher knows. If the customer goes to the movie when he or she should be on a sales call, the researcher knows. If the customer is speeding, the researcher knows.

Clearly, modern technology is making confidentiality more and more difficult to maintain. While legitimate uses of this type of

technology may assist in easing traffic patterns and providing better locations for service stations, shopping developments, and other retailers, at what point does the collection of such information become a concern? When would you become concerned about hav- ing your whereabouts constantly tracked?

Question 1. Suppose a GIS research firm is approached by the state legis-

lature and asked to provide data about vehicle movement within the state for all cars with a satellite tracking mechanism. Based on the movement of the cars over a certain time, the police can decide when a car was speeding. They intend to use this data to send speeding tickets to those who moved too far, too fast. If you are the research firm, would you supply the data? Discuss the ethical implications of the decision.

CASE 5.2

104 Part 1: Introduction

P a r t t W O

Beginning Stages of the Research Process

CHAPTER 6 Problem Definition: The Foundation of Business Research

CHAPTER 7 Qualitative Research Tools

CHAPTER 8 Secondary Data Research in a Digital Age

After studying this chapter, you should be able to

1. Explain why proper “problem definition” is essential to useful business research

2. Know how to recognize problems

3. Translate managerial decision statements into relevant research objectives

4. Translate research objectives into research questions and/or research hypotheses

5. Outline the components of a research proposal

6. Construct tables as part of a research proposal

C H A P T E R

D avid Deland, who has owned his trucking business for 20 years, struggles with the spreadsheet in front of him. His recruitment specialist sits glumly across from his desk, pondering what kind of

response to give to the inevitable question, “Why are our recruit- ment costs so high?” Next to the specialist sits James Garrett, a business research consultant who has been hired by the Deland Trucking Company to get a handle on the recruitment expenses the company has seen skyrocket over the last six months.

“I just don’t get it,” David sighs in frustration. “We have seen a 45 percent increase in our trucker recruitment advertising costs, and our driver intake and orientation expenses are killing us! James, I just don’t understand what is happening here.”

James and the specialist have had some initial discussions, but there is no easy way to reduce those costs without reducing the number of truckers that Deland hires. “Perhaps we can find a more efficient way of advertising our openings,” suggests the recruiting specialist. “Maybe we can reduce the number of orientation sessions or travel expenses associated with the hir- ing process.” David counters, “Well, I don’t see how we are any different from our competitors. We use the same recruitment and orientation approach that they use. I have no handle on

their expenses, but the fact that our expenses are skyrocketing must mean something is going on.”

James stares at his copy of the spreadsheet. “There is no easy way to do this, without hurting your ability to keep drivers in your trucks,” he says. “Is it that the costs for driver selection and recruitment have gone up?” “No, the costs have been the same,”

LEARNING OUTCOMES

Problem Definition The Foundation of Business Research

Chapter Vignette:

Deland Trucking Has a “Recruitment” Problem

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Introduction Importance of Starting with a Good Problem Definition The first stage of the research process introduced in the early chapters and highlighted in Chapter 4 involves translating the business decision situation into specific research objectives. While it is tempting to skip this step and go directly to designing a research project, the chances that a research project will prove useful are directly related to how well the research objectives correspond to the true business “problem.” Clearly, the easiest thing for James to do in the opening vignette is to start designing a study of Deland Trucking’s recruitment effectiveness. This seems to be what David and his recruitment specialist want. But is it what they really need?

This chapter looks at this important step in the research process more closely. Some useful tools are described that can help translate the business situation into relevant, actionable research objectives. Research too often takes the blame for business failures when the real failure was really management’s view of its own company’s situation. The Research Snapshot “Good Answers, Bad Questions?” describes some classic illustrations involving companies as big and successful as Coca- Cola, R.J. Reynolds, and Ford. While the researcher certainly has some say in what is actually stud- ied, we must remember that the client (either the firm’s management team or an outside sponsor) is the research customer and the researcher is serving the client’s needs. In other words, when the client fails to understand their situation or insists on studying an irrelevant problem, the research is very likely to fail, even if it is technically performed perfectly.

Translating a business situation into something that can be researched is somewhat like translat- ing one language into another. It begins by coming to a consensus on a decision statement or ques- tion. A decision statement is a written expression of the key question(s) that a research user wishes to answer. It is the reason that research is being considered. It must be well stated and relevant. As discussed in Chapter 4, the researcher translates this into research terms by rephrasing the decision statement into one or more research objectives. These are expressed as deliverables in the research proposal. The researcher then further expresses these in precise and scientific research terminology by creating research hypotheses from the research objectives.

In this chapter, we use the term problem definition. Realize that sometimes this is really opportunity seeking rather than truly problem solving. Nonetheless, for simplicity, the term problem definition is adapted here to refer to the process of defining and developing a decision statement and the steps involved in translating it into more precise research terminology, includ- ing a set of research objectives. If this process breaks down at any point, the research will almost certainly be useless or even harmful. It will be useless if it presents results that simply are deemed irrelevant and do not assist in decision making. It can be harmful both because of the wasted resources and because it may misdirect the company in a poor direction.

decision statement

A written expression of the key question(s) that the research user wishes to answer.

problem definition

The process of defining and developing a decision state- ment and the steps involved in translating it into more precise research terminology, including a set of research objectives.

responds the recruiter. “It’s just that we have had to do so many orientation and hiring sessions since the first of the year.”

“David, it might be best if I get a look at some of your hiring statistics, as well as your driver census over the last year,” com- ments James. Turning to the recruiter, James asks, “Can you give me some of your driver data to look through?”

“Sure,” says the recruiter. “We have lots of info about our drivers, and the driver census is updated monthly. We even have some exit data we have gathered from a few drivers who have left us. I don’t know exactly what the trend is with those drivers who leave, since we haven’t had a chance to really analyze the data. I will send it to you through e-mail this afternoon.”

James drives back to his office, reflecting on his meeting. As he passes by trucks on the way, he peeks at the drivers who are going

in the same direction as he is. What do they think about their company? Would they see Deland as a great place to work? What would make Deland Trucking’s recruitment costs go so high?

At his office, the e-mail with the trucker census and the hiring data has already arrived. Opening the numerous spreadsheets, James continues to wonder. Does Deland Truck- ing have a recruitment problem? Is it a training problem? Is retention too low? Is the problem the company itself? What is going on?

As he examines the hiring worksheet, he compares it to the driver census figures for the last six months. “There is the prob- lem!” he exclaims. “I think I need to put together a proposal for David on this. I’m sure he will be surprised about what his company’s problem really is.”

CHAPTER 6 Problem Definition: The Foundation of Business Research • 107

108

Ultimately, it is difficult to say that any one step in the research process is most important. However, formally defining the problem to be attacked by developing decision statements and translating them into actionable research objectives must be done well or the rest of the research process is misdirected. Even a good road map is useless unless you know where you are going! All of the roads can be correctly drawn, but they still don’t get you where you want to be. Similarly, even the best research procedures will not overcome poor problem definition.

Problem Complexity Simply put, the quality of business research is limited by the quality of the problem definition stage. While certainly critically important to the success of the project, this is far from the easiest stage of the research process. In fact, it can often be the most complex. Exhibit 6.1 helps to illustrate factors that influence how complex the process can be.

SURVEY

THIS! Review the survey you are using for this course. Consider the questions shown here and the other sections of the survey.

■ Based on the survey you have completed, identify at least three decision statements that might have driven the construction of this questionnaire.

■ Translate each of the decision state- ments from above into a research question and the related research hypothesis or hypotheses.

■ What would a dummy table (see Exhibit 6.10) look like that would contain the data from the survey that addresses each of these hypotheses?

■ What types of companies might be interested in this information? ■ Would any nonprofit institutions be interested in this data?

EXHIBIT 6.1

Defining Problems Can Be Difficult

Situation Research Objectives

Easier: 1. Situation is recurring/routine 2. A dramatic change occurs 3. Symptoms are isolated 4. Symptoms are consistent

Harder: 1. Situation appears new 2. Change(s) in situation is subtle 3. Symptoms are scattered 4. Symptoms are ambiguous

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RESEARCH SNaPSHOt

It’s amazing, but sometimes even the most successful compa- nies make huge research blunders. These mistakes often are based on a misunderstanding of exactly what the brand and/or product means to consumers. Some of the famous, or infamous, examples of such blunders include RJR’s introduction of Premier “Smokeless” Cigarettes, Ford’s introduction of the Edsel in the 1950s, and most famous (or infamous) of all, Coca-Cola’s intro- duction of New Coke as a replacement for regular “old” Coke.

Volumes have been written about each of these episodes. One does have to wonder, how did these great companies do such apparently dumb things? The blame is often placed at the foot of the decision makers: “Research should have revealed that product was a loser.” However, researchers address the questions they are asked to address by management. Certainly, the researchers play a role in framing any decision situation into something that can be addressed by a pointed research question. The decision makers almost always start the process by asking for input from their staff, or from research consultants they have hired. Hopefully, the dialogue that results will lead to a productive research question that will provide useful results. However it isn’t always the case that such research questions are self-evident.

The “New Coke” saga is well worth repeating. In hindsight, it seems almost unthinkable that Coke could have made its deci- sion to replace a product with a century-long success record without considering the emotional meaning that goes along with drinking a “Coke.” However, management considered Coke to be a beverage, not a brand. Thus, the focus was on the taste of Coke. Thus, researchers set about trying to decide if New Coke, which was more similar to Pepsi, tasted better than the original Coke. A great deal of very careful research suggested clearly that it did taste better. If the key question was taste, New Coke was preferred over old Coke by more consumers. In fact, there was considerable evidence that already showed a taste preference for Pepsi over old Coke. Interestingly, Coke appeared to view itself as its primary competitor. At least two very impor- tant questions were never asked or were addressed insufficiently:

1. Do consumers prefer New Coke over Pepsi? 2. When people know what they are drinking, do they still

prefer New Coke to old Coke?

For a taste test to be valid, it is should be done “blindly,” meaning that the taster doesn’t know what he or she is drinking.

Only then can one assess taste without being psy- chologically influenced by knowing the brand. So, Coke conducted a blind taste test. This is certainly a good research practice—if the question is taste. The Coke research correctly answered the taste question. The big problem is that since management didn’t realize that most of the mean- ing of Coke is psychological, and since they were so convinced that their old product was “inferior,” the dialogue between man- agement and researchers never produced more useful questions.

In the case of Ford’s Edsel, a postmortem analysis suggests that research actually indicated many of the problems that ulti- mately led to its demise. The name, Edsel, was never tested by research, even though hundreds of other possibilities were.

Similarly, the idea of a smokeless cigarette seemed appeal- ing. Research addressed the question, “What is the attitude of smokers and nonsmokers toward a smokeless cigarette?” Nonsmokers loved the idea. Smokers, particularly those who lived with a nonsmoker, also indicated a favorable attitude. However, as we know, the product failed miserably. If you take the “smoke” out of “smoking,” is it still the same thing? This question was never asked. Would someone who would try a smokeless cigarette replace their old brand with this new brand? Again, this wasn’t asked.

It is possible that some famous company could be mak- ing a very similar mistake today? Consider Macy’s. Macy’s has acquired many regional and local department stores around the country over the past few years. Clearly, Macy’s is a very rec- ognizable name brand that brings with it considerable “brand equity.” How important is it for Macy’s to ask, “What is the best name for this department store?” If the acquisition involves tak- ing over a local retail “icon” such as Chicago’s Marshall Field’s, is a name change a good thing? These seem to be relevant ques- tions to which research could probably provide good answers!

Sources: “New Coke: A ‘Classic’ Marketing Research Blunder?”, http:// imcetys.files.wordpress.com/2006/12/caso-newcoke.pdf, accessed May 8, 2011; “Is Macy’s the New Coke?” Advertising Age 76 (September 26, 2005), 24, http://www.fieldsfanschicago.org/, accessed May 8, 2011.

Good Answers, Bad Questions?

Does a name change always make sense? We must ask the correct research questions to be sure.

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Situation Frequency Many business situations are cyclical, leading to recurring business problems. These problems can even become routine. In these cases, it is typically easy to define problems and identify the types of research that are needed. In some cases, problems are so routine that they can be solved without any additional research, such as when recurring problems can be automated through a company’s decision support system (DSS).

For example, pricing problems often occur routinely. Just think about how the price of gas fluctuates when several stations are located within sight of each other. One station’s prices definitely affect the sales of the other stations as well as of the station itself. Similarly, automo- bile companies, airline companies, and computer companies, to name just a few, face recurring pricing issues. Because these situations recur so frequently, addressing them becomes routine. Decision makers know how to communicate them to researchers and researchers know what data are needed.

Most pricing decisions in the airline industry are automated based on sophisticated demand models. The models take into account fluctuations in travel patterns based on the time of the year, time of the day, degree of competition for that particular route, and many other factors. At one time, these decisions were based on periodic research reports. Now, the information is simply fed into a decision support system that generates a pricing schedule. You can see how quickly informa- tion is analyzed and used for pricing decisions by simply checking prices for a specific flight online over the period of a few days. It is interesting that one factor that is not very important in many of these pricing decisions is the cost involved in flying someone from point A to point B. Indeed, some passengers pay a fare much higher than the actual costs and others pay a fare much lower than the actual costs involved in getting them to their desired destination.

Dramatic Changes When a sudden change in the business situation takes place, it can be easier to define the problem. For example, if Deland Trucking’s recruiting expenses had increased sharply at the beginning of the year, the key factors to study could be isolated by identifying other factors that changed in that same time period. It could be that a very large trucking contract had been obtained, a current customer dramatically increased their distribution needs, or a new competitor entered the market.

In contrast, when changes are very subtle and take effect over a long period of time, it can be more difficult to define the actual decision and research problems. Detecting trends that would permanently affect the recruitment challenges that Deland faces can be difficult. It may be difficult to detect the beginning of such a trend and even more difficult to know whether such a trend is relatively permanent or simply a temporary occurrence.

How Widespread Are the Symptoms? The more scattered any symptoms are, the more difficult it is to put them together into some coherent problem statement. In contrast, firms may sometimes face situations in which multiple symptoms exist, but they are all pointing to some specific business area. For instance, an automobile manufacturing company may exhibit symptoms such as increased complaints about a car’s fit and finish, increased warranty costs, higher labor costs, and lower performance ratings by consumer advocates such as Consumer Reports. All of these symptoms point to production as a likely prob- lem area. This may lead to research questions that deal with supplier-manufacturer relationships, job performance, job satisfaction, supervisory support, and performance. Although having a lot of problems in one area may not sound very positive, it can be very helpful in pointing out the direc- tion that is most in need of attention and improvement.

In contrast, when the problems are more widespread, it can be very difficult to develop useful research questions. If consumer complaints dealt with the handling and the appearance of the car, and these were accompanied by symptoms including consumer beliefs that gas mileage could be better and that dealerships did not have a pleasant environment, it may be more difficult to put these scattered symptoms together into one or a few related research questions. Later in the chapter, we’ll discuss some tools for trying to analyze symptoms in an effort to find some potential com- mon cause.

Symptom Ambiguity Ambiguity is almost always unpleasant. People simply are uncomfortable with the uncertainty that comes with ambiguity. Similarly, an environmental scan of a business situation may lead to many symptoms, none of which seem to point in a clear and logical direction. In this case, the problem area remains vague and the alternative directions are difficult to ascertain.

110 • PART TWO Beginning Stages of the Research Process

A retail store may face a situation in which sales and traffic are up, but margins are down. They may have decreased employee turnover, but lower job satisfaction. In addition, there may be several issues that arise with their suppliers, none of which is clearly positive or negative. In this case, it may be very difficult to sort through the evidence and reach a definitive decision statement or list of research objectives.

The Problem-Definition Process Problems Mean Gaps A problem occurs when there is a difference between the current conditions and a more preferable set of conditions. In other words, a gap exists between the way things are now and a way that things could be better. The gap can come about in a number of ways:1

1. Business performance is worse than expected business performance. For instance, sales, profits, and margins could be below targets set by management. Or, employee turnover is higher than expected. This is a very typical type of problem analysis. Think of all the new products that fail to meet their targeted goals. Trend analysis would also be included in this type of problem. Management is constantly monitoring key performance variables. Previous performance usu- ally provides a benchmark forming expectations. Sales, for example, are generally expected to increase a certain percentage each year. When sales fall below this expectation, or particularly when they fall below the previous year’s sales, management usually recognizes that they have a potential problem on their hands. The Research Snapshot “Poor Questions Result in Poor Research in Japan!” illustrates this point.

2. Actual business performance is less than possible business performance. Realization of this gap first requires that management have some idea of what is possible. This may form a research problem in and of itself. Opportunity-seeking often falls into this type of problem-definition process. Many American and European Union companies have redefined what possible sales levels are based upon the expansion of free markets around the world. China’s Civil Aviation Administration has relaxed requirements opening the Chinese air travel market to private airlines.2 Suddenly, the possible market size for air travel has increased significantly, creating opportunities for growth.

3. Expected business performance is greater than possible business performance. Sometimes, management has unrealistic views of possible performance levels. One key problem with new product introductions involves identifying realistic possibilities for sales. While you may have heard the old adage that 90 percent of all new products fail, how many of the failures had a realistic sales ceiling? In other words, did the company know the possible size of the market? In this case, the problem is not with the product but with the plan. Some product “failures” may actually have been successful if management had a more accurate idea of the total market potential. Management can close this gap through decision making.

The Problem-Definition Process Steps The problem-definition process involves several interrelated steps, as shown in Exhibit 6.2. Some- times, the boundaries between each step aren’t exactly clear. But generally, completing one step leads to the other and by the time the problem is defined, each of these steps has been addressed in some way. The steps are:

1. Understand the business situation—identify key symptoms 2. Identify key problem(s) from symptoms 3. Write managerial decision statement and corresponding research objectives 4. Determine the unit of analysis 5. Determine the relevant variables 6. Write research questions and/or research hypotheses

A separate section deals with each stage below.

problem

Occurs when there is a differ- ence between the current con- ditions and a more preferable set of conditions.

CHAPTER 6 Problem Definition: The Foundation of Business Research • 111

112

Understand the Business Decision A situation analysis involves the gathering of background information to familiarize researchers and managers with the decision-making environment. The situation analysis can be written up as a way of documenting the problem-definition process. Gaining an awareness of the relevant conditions and an appreciation of the situation often requires exploratory research. Researchers

situation analysis

The gathering of background information to familiarize

researchers and managers with the decision-making

environment.

EXHIBIT 6.2

The Problem-Definition Process 6. Write research questions and/or

research hypotheses

1. Understand the situation—identify the key symptoms

2. Identify the problems from the symptoms

3. Write managerial decision statement and corresponding research objectives

4. Determine the unit of analysis

5. Determine relevant variables

French conglomerate BSN (Groupe Danone) is among the larg- est food manufacturers in the world. When the firm noted that the Japanese were becoming more Westernized, BSN decided to target Japan as a priority market for its yogurts. BSN knew better than to enter a new market without conducting mar- keting research, so they launched a market survey in Japan. The study reported that Japanese consumers were indeed becoming more Westernized in their food choices and eating habits and appeared to be offer a strong potential market for yogurts.

BSN rolled out their products in Japan, complete with exten- sive distribution and a well-financed promotional campaign. Sales, however, were disappointing, failing to meet company expectations.

After the fact, BSN conducted further research to under- stand what went wrong. The follow-up research indicated that the “Yes/No” questions asked were too simplistic to

actually gather accu- rate responses. The Japanese consumers were simply too polite to reply “No” to a question. Furthermore, the respondents did not want to offend the researchers by criticiz- ing advertisements featuring a spoon as an eating utensil. As a result the data was very misleading and presented an overly optimistic picture of the size, value, and potential of the Japanese market for yogurts.

Source: The Higher Education Academy: Teaching Resource Exchange, Hospitality, Leisure, Sport and Tourism Network, “Examples of Pitfalls and Mistakes,” http://www.heacademy.ac.uk/assets/hlst/documents/heinfe_ exchange/pitfalls.doc, accessed May 8, 2011.

Poor Questions Result in Poor Research in Japan!

RESEARCH SNaPSHOt

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sometimes apply qualitative research as discussed in the next chapter with the objective of better problem definition. The situation analysis usually begins with an interview between the researcher and management.

Interview Process The researcher must enter a dialogue with the key decision makers in an effort to fully under- stand the situation that has motivated a research effort. This process is critical and the researcher should be granted access to all individuals who have specific knowledge of or insight into this situation. Researchers working with managers who want the information “yesterday” often get little assistance when they ask, “What are your objectives for this study?” Nevertheless, even decision makers who have only a gut feeling that the research might be a good idea benefit greatly if they work with the researcher to articulate precise research objectives.3 Even when there is strong cooperation, seldom can key decision makers express the situation in research terms:

Despite a popular misconception to the contrary, objectives are seldom clearly articulated and given to the researcher. The decision maker seldom formulates his objectives accurately. He is likely to state his objec- tives in the form of platitudes which have no operational significance. Consequently, objectives usually have to be extracted by the researcher. In so doing, the researcher may well be performing his most useful service to the decision maker.4

Researchers may often be tempted to accept the first plausible problem statement offered by management. For instance, in the opening vignette, it is clear that David believes there is a recruit- ment problem. However, it is very important that the researcher not blindly accept a convenient problem definition for expediency’s sake. In fact, research demonstrates that people who are better problem solvers generally reject problem definitions as given to them. Rather, they take informa- tion provided by others and reassociate it with other information in a creative way. This allows them to develop more innovative and more effective decision statements.5

There are many ways to discover problems and spot opportunities. Often there is as much art as science involved in translating scattered pieces of evidence about some business situation into relevant problem statements and then relevant research objectives. While there are other sources that address creative thinking in detail, some helpful hints that can be useful in the interview pro- cess include:

1. Develop several alternative problem statements. These can emerge from the interview mate- rial or from simply rephrasing decision statements and problem statements.

2. Think about potential solutions to the problem.6 Ultimately, for the research to be actionable, some plausible solution must exist. After pairing decision statements with research objectives, think about the solutions that might result. This can help make sure any research that results is useful.

3. Make lists. Use free-association techniques to generate lists of ideas. At this stage of the research process, it is fair to say “the more ideas, the better.” Use interrogative techniques to generate lists of potential questions that can be used in the interview process. Interrogative techniques simply involve asking multiple what, where, who, when, why, and how ques- tions. They can also be used to provoke introspection, which can assist with problem definition.

4. Be open-minded. It is very important to consider all ideas as plausible in the beginning stages of problem solving. One sure way to stifle progress is to think only like those intimately involved in the business situation or only like those in other industries. Analogies can be useful in thinking more creatively.

Identifying Symptoms Interviews with key decision makers also can be one of the best ways to identify key problem symptoms. Recall that all problems have symptoms just as human disease is diagnosed through symptoms. Once symptoms are identified, then the researcher must probe to identify possible

interrogative techniques

Asking multiple what, where, who, when, why, and how questions.

CHAPTER 6 Problem Definition: The Foundation of Business Research • 113

causes of these changes. Probing is an interview technique that tries to draw progressively deeper and more elaborate explanations from the discussion. This discussion may involve potential prob- lem causes. This probing process will likely be very helpful in identifying key variables that are prime candidates for study.

One of the most important questions the researcher can ask during these interviews is, “What has changed?” Then, the researcher should probe to identify potential causes of the change. At the risk of seeming repetitive, it is important that the researcher repeat this process to make sure that any important changes have not been left out.

In addition, the researcher should look for changes in company documents, including financial statements and operating reports. Changes may also be identified by tracking down news about competitors and customers. Exhibit 6.3 provides a summary of this approach.

probing

An interview technique that tries to draw deeper and more

elaborate explanations from the discussion.

Think back to the opening vignette. Often, multiple interviews are necessary to identify all the key symptoms and gain a better understanding of the actual business situation. On a follow-up interview, the dialogue between James and David may proceed as follows:

James: David, it is clear that your recruitment costs have been increasing since the start of the year. What other changes have occurred inside of your business within the past year?

David: Just a few things. We have had pressures on our bottom line, so we held back on rais- ing the cents per mile that we give our drivers. Also, we have had to extend our long- haul trucking needs, so our drivers are on the road for a much longer period of time for each trip.

James (probing): Tell me, what led to this decision to extend the driver’s time on the road? David: It just worked out that way. Our contract just changed to allow us to do this, and our

operations manager felt we could make more money per load this way. James: Have you noticed changes in your customers? David: We do see that they are a little irritated due to some of the problems of getting their freight

delivered successfully. James: Has there been a change in personnel? David: Yes, we’ve had more than the usual share of turnover. I’ve turned over most personnel

decisions to our new human resources manager. We’ve had trouble maintaining a person in that role.

EXHIBIT 6.3

What Has Changed? Question: What changes have

occurred recently?

Probe: Tell me about this change.

Probe: What has brought this about?

Problem: How might this be related to your problem?

Question: What other changes have occurred recently (i.e.,

competitors, customers, environment, pricing, promotion,

suppliers, employees, etc.)?

Continue Probing

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114 • PART TWO Beginning Stages of the Research Process

115

In the change interview, the researcher is trying to identify possible changes in the customers, the competitors, the internal conditions of the company, and the external environment. The inter- play between things that have changed and things that have stayed the same can often lead to key research factors. Before preparing the proposal, James and David agree that the real decision faced is not as narrow as a recruiting problem. In this case, James is beginning to suspect that one key factor is that the increase in recruitment costs is a reflection of increased driver turnover. If driver retention could be increased, the need for larger recruitment expenses would stabilize, or even go down.

Almost any situation can be framed from a number of different perspectives. A pricing problem may be rephrased as a brand image problem. People expect high quality products to have higher prices. A quality problem may be rephrased as a packaging problem. For example, a potato chip company thought that a quality differential between their potatoes and their competitor’s was the cause for the symptom showing sliding market share. However, one of the research questions that eventually resulted dealt with consumer preferences for packaging. In the end, research suggested that consumers prefer a foil package because it helps the chips stay fresher longer. Thus, the key gap turned out to be a package gap!7

Researchers should make sure that they have uncovered all possible relevant symptoms and considered their potential causes. Perhaps more interview time with key decision makers asking why people choose Coke would have helped identify some of the less tangible aspects of the Coke- Pepsi-New Coke battle. Similarly, as seen in the Research Snapshot “Opportunity Is a ‘Fleeting’ Thing,” the makers of automobiles in the United States should examine more carefully the possible ways that consumers make choices about the vehicles they buy. It can help avoid mistakes later.

RESEARCH SNaPSHOt

Have non-European automotive companies missed out on European opportunities? Europe represents a nearly 14 million unit annual market for new automobiles. Traditionally, the thinking is that European’s prefer smaller or “light-cars.” Thus, European car companies like BMW and Audi were slow to enter the SUV market. Mercedes entered the SUV market rather early on, but the emphasis was on the American market. American and Japanese companies offered little more than a token effort at selling SUVs in Europe. Thus, the SUV wars were fought in America where total volume reached 4 million shortly after 2000. Europeans were left with fewer choices if an SUV struck their fancy.

As a result, pre-2000 SUV sales in Europe were almost non -existent. However, SUV sales in Europe have increased dra- matically since then. By 2004, about 1 in 20 of all new autos sold in Europe. Today, Nissan, Toyota, Land Rover, and Suzuki are major players in the European SUV market. However, sales expectations for new entries from Opel, Renault, Volkswagen, Mercedes, and Audi are sluggish through 2008 with so many SUVs to choose from coupled with high fuel prices. In hind- sight, could it be that several prominent automobile companies missed opportunities in Europe because they failed to know how big the market truly was?

Looking at this from the opposite direction, the tiny (by U.S. standards) two-seater SMART (http://www.smartusa.com) car has been introduced in the United States. SMART got off to a fast start in the United States, selling 24,622 cars in 2008. However sales fell to 5,927 units in 2010. SMART may now be poised to take advantage of an opportunity created by the current $4.00 a gallon gas prices. U.S. producers are also turning production away from large SUVs like HUMMER toward new entries like the Chevro- let Volt. The relative success of these new entries against European minis like the SMART may also depend on the exchange rate, which presently makes European entries expensive in the United States. Word is there may even be a SMART SUV—a miniature ver- sion of an American icon. What is the SMART future?

Sources: “The Business Week,” Business Week (June 16, 2008), 6–10; Crain, K. C., “Analyst Sees Sales Decline for Light Vehicles in 2005,” Automotive News 79 (January 24, 2005), 111; Meiners, Jena, “SUV Sales in Europe Will Peak in 2008,” Automotive News Europe 9 (June 28, 2004); Marquand, R., “Euorpe’s Little Smart Car to Hit U.S. Streets,” Christian Science Monitor (2008), http://www .csmonitor.com/2008/0109/p01s01-woeu.html, accessed August 3, 2011; “ Mercedes-Benz to Control Smart Car Distribution in the US - Penske Relieved of Duties,” February 15, 2011, http://altenergyautos.blogspot .com/2011/02/mercedes-benz-to-control-smart-car.html, accessed May 8, 2011.

Opportunity Is a “Fleeting” Thing

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Identifying the Relevant Issues from the Symptoms Anticipating the many influences and dimensions of a problem is impossible for any researcher or executive. The preceding interview is extremely useful in translating the decision situation into a working problem definition by focusing on symptoms. The probing process discussed on pages 113–115 begins this process. However, the researcher needs to be doubly certain that the research attacks real problems and not superficial symptoms.

For instance, when a firm has a problem with advertising effectiveness, the possible causes of this problem may be low brand awareness, the wrong brand image, use of the wrong media, or perhaps too small a budget. Certain occurrences that appear to be the problem may be only symp- toms of a deeper problem. Exhibit 6.4 illustrates how symptoms can be translated into a problem and then a decision statement.

‘‘The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.’’—MARCEL PROUST

Writing Managerial Decision Statements and Corresponding Research Objectives The situation analysis ends once researchers have a clear idea of the managerial objectives from the research effort. Decision statements capture these objectives in a way that invites multiple solutions. Multiple solutions are encouraged by using plural nouns to describe solutions. In other words, a decision statement that says in what “ways” a problem can be solved is better than one that says in what “way” a problem can be solved. Ultimately, research may provide evidence showing results of several ways a problem can be attacked.

EXHIBIT 6.4

Symptoms Can Be Confusing

Firm’s Situation Symptoms Likely Problem (s) Decision Statement

Research Action

Conduct situation analysis including interviews with key decision makers

Consider results of probing and apply creative processes

Express in actionable terms and make sure decision makers are in agreement

Situation 1 22-year-old neighborhood swimming association seeks research help

• Declining member ship for six years

• Increased attendance at new water park

• Less frequent usage among members

Swim facility is outdated and does not appeal to younger families. Younger families and children have a negative image of pool. Their “old market” is aging.

What things can be done to energize new markets and create a more favorable attitude toward the association?

Situation 2 Manufacturer of palm-sized computer with wireless Internet access believes B2B sales are too low

• Distributors complain prices are too high

• Business users still use larger computers for displaying information to customers or smartphones for other purposes

Business users do not see the advantages of smaller units. The advantages are not outweighed by costs. The transition costs may be a drawback for B2B customers more than for B2C customers

What things can be done to improve competitive positioning of the new product in B2B markets?

Situation 3 A new microbrewery is trying to establish itself

• Consumers seem to prefer national brands over the local microbrew products

• Many customers order national brands within the microbrew itself

• Some customers hesitant to try new microbrew flavors

Is there a negative flavor gap? Do consumers appreciate the microbrew approach and the full beer tasting (as opposed to drinking) experience?

How can we encourage more consumers to come to the microbrew and try our products?

Should we redesign the brewery to be more inviting?

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116 • PART TWO Beginning Stages of the Research Process

Decision statements must be translated into research objectives. At this point, the researcher is starting to visualize what will need to be measured and what type of study will be needed. Exhibit 6.5 extends the examples from Exhibit 6.4, showing research objectives that correspond to each decision statement. Note that each research objective states a corresponding potential result(s) of the research project. Thus, in some ways, it is stating the information that is needed to help make the decision. Once the decision statement is written, the research essentially answers the question, “What information is needed to address this situation?”

Referring back to the opening vignette, the analysis of the symptoms has led to the conclusion that there is an employee retention problem. Perhaps drivers are dissatisfied with being away from their families for so long and this is leading to higher levels of driver turnover. Or, perhaps it is the cents per mile that is leading to driver frustration and a desire to go to a higher-paying competitor. David and James eventually agree on the following decision statement:

In what ways can Deland Trucking build driver loyalty so that retention increases and subsequent recruit- ment costs decrease?

What information or data will be needed to help answer this question? Obviously, we’ll need to study the driver census and the number of hires needed to fill open positions. James needs to find out what might cause employee dissatisfaction and cause turnover to increase. Thinking back to the interview, James knows that there have been several changes in the company itself, many related to saving costs. Saving costs sounds like a good idea; however, if it harms driver

EXHIBIT 6.5

Translating Decision Statements

Decision Statement Research Objectives Research Questions Research Hypotheses

Research Action

Express in actionable terms and make sure decision makers are in agreement

Expresses potential research results that should aid decision-making

Ask a question that corresponds to each research objective

Specific statement explaining relationships, usually involving two variables, and including the direction of the relationship

Situation 1 What things can be done to energize new markets and create a more favorable attitude toward the association?

Determine reasons why families may choose to join or not join a “swim club.”

How do the type of facilities and pricing relate to family attitudes toward a swim facility?

Child-friendly pool designs are positively related to attitudes toward the facility.

Flexible pricing policies are positively related to attitudes toward the facility.

Situation 2 What product features can be improved and emphasized to improve competitive positioning of the new product in B2B markets?

List actions that may overcome the objections (switching costs) of B2B customers toward adoption of the new product.

What are the factors that most lead to perceptions of high switching costs?

Perceived difficulty in learning how to use the new device is related to switching costs.

Price is positively related to switching costs.

Knowledge of new product is positively related to switching costs.

Situation 3 How can we encourage more consumers to come to the microbrew and try our products?

Should we redesign the brewery to be more inviting?

Describe how situational factors influence beer consumption and consumer attitudes toward beer products.

List factors that will improve attitudes toward the microbrewery.

Do situational factors (such as time of day, food pairings, or environmental factors) relate to taste perceptions of beer?

Microbrew beer is preferred when consumed with food.

An exciting atmosphere will improve consumer attitudes toward the microbrew.

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CHAPTER 6 Problem Definition: The Foundation of Business Research • 117

loyalty even slightly, it probably isn’t worthwhile. Thus, the corresponding research objectives are stated as follows:

■ Determine what key variables relate to driver loyalty within the company, meaning (1) how does the lower level of pay impact driver retention and (2) what does the increase in long-haul trucking do to Deland Trucking’s ability to increase retention?

■ Assess the impact of different intervention strategies on driver satisfaction.

These research objectives are the deliverables of the research project. A research study will be conducted that (1) shows how much each of several key variables relates to loyalty and retention and (2) provides a description of likelihood of different intervention strategies on driver satisfaction.

The researcher should reach a consensus agreement with the decision maker regarding the overall decision statement(s) and research objectives. If the decision maker agrees that the statement captures the situation well and understands how the research objectives, if accomplished, will help address the situation, then the researcher can proceed. The researcher should make every effort to ensure that the decision maker understands what a research project can deliver. If there is no agree- ment on the decision statement or research objectives, more dialogue between decision makers and researchers is needed.

Determine the Unit of Analysis The unit of analysis for a study indicates what or who should provide the data and at what level of aggregation. Researchers specify whether an investigation will collect data about individuals (such as customers, employees, and owners), households (families, extended families, and so forth), organizations (businesses and business units), departments (sales, finance, and so forth), geographi- cal areas, or objects (products, advertisements, and so forth). For example, for clothing purchases, individuals are likely to be the appropriate level of analysis. In studies of home buying, however, the husband/wife dyad typically is the best unit of analysis since this purchase decision would be made jointly by husband and wife.

Researchers who think carefully and creatively about situations often discover that a problem can be investigated at more than one level of analysis. For example, a lack of worker productivity could be due to problems that face individual employees or it could reflect problems that are pres- ent in entire business units. Determining the unit of analysis should not be overlooked during the problem-definition stage of the research.

Determine Relevant Variables What Is A Variable? What things should be studied to address a decision statement? Researchers answer this question by identifying key variables. A variable is anything that varies or changes from one instance to another. Variables can exhibit differences in value, usually in magnitude or strength, or in direction. In research, a variable is either observed or manipulated, in which case it is an experimental variable.

The converse of a variable is a constant. A constant is something that does not change. Con- stants are not useful in addressing research questions. Since constants don’t change, management isn’t very interested in hearing the key to the problem is something that won’t or can’t be changed. In causal research, it can be important to make sure that some potential variable is actually held constant while studying the cause and effect between two other variables. In this way, a spurious relationship can be ruled out. This issue is discussed in detail in Chapter 12. At this point, the notion of a constant is more important in helping to understand how it differs from a variable.

Types of Variables There are several key terms that help describe types of variables. The variance in variables is cap- tured either with numerical differences or by an identified category membership. In addition, dif- ferent terms describe whether a variable is a potential cause or an effect.

unit of analysis

A study indicates what or who should provide the data and at

what level of aggregation.

variable

Anything that varies or changes from one instance to another;

can exhibit differences in value, usually in magnitude or

strength, or in direction.

constant

Something that does not change; is not useful in

addressing research questions.

118 • PART TWO Beginning Stages of the Research Process

A continuous variable is one that can take on a range of values that correspond to some quantitative amount. Consumer attitude toward different airlines is a variable that would gener- ally be captured by numbers, with higher numbers indicating a more positive attitude than lower numbers. Each attribute of airlines’ services, such as safety, seat comfort, and baggage handling can be numerically scored in this way. Sales volume, profits, and margin are common business metrics that represent continuous variables.

A categorical variable is one that indicates membership in some group. The term classificatory variable is sometimes also used and is generally interchangeable with categorical variable. Categorical variables sometimes represent quantities that take on only a small number of values (one, two, or three). However, categorical variables more often simply identify membership.

For example, people can be categorized as either male or female. A variable representing biological sex describes this important difference. The variable values can be an “M” for member- ship in the male category and an “F” for membership in the female category. Alternatively, the researcher could assign a 0 for men and a 1 for women. In either case, the same information is represented.

A common categorical variable in consumer research is adoption, meaning the consumer either did or did not purchase a new product. Thus, the two groups, purchase or not purchase, comprise the variable. Similarly, turnover, or whether an employee has quit or not, is a common organizational variable.

In descriptive and causal research, the terms dependent variable and independent variable describe different variable types. This distinction becomes very important in understanding how business processes can be modeled by a researcher. The distinction must be clear before one can correctly apply certain statistical procedures like multiple regression analysis. In some cases, however, such as when only one variable is involved in a hypothesis, the researcher need not make this distinction.

A dependent variable is a process outcome or a variable that is predicted and/or explained by other variables. An independent variable is a variable that is expected to influence the dependent variable in some way. Such variables are independent in the sense that they are determined outside of the process being studied. That is another way of saying that dependent variables do not change independent variables.

For example, average customer loyalty may be a dependent variable that is influenced or pre- dicted by an independent variable such as perceptions of restaurant food quality, service quality, and customer satisfaction. Thus, a process is described by which several variables together help create and explain how much customer loyalty exists. In other words, if we know how a customer rates the food quality, service quality, and satisfaction with a restaurant, then we can predict that customer’s loyalty toward that restaurant. Note that this does not mean that we can predict food quality or service quality with customer loyalty.

Dependent variables are conventionally represented by the letter Y. Independent vari- ables are conventionally represented by the letter X. If research involves two dependent variables and two or more independent vari- ables, subscripts may also be used to indicate Y

1 , Y

2 and X

1 , X

2 , and so on.

Ultimately, theory is critical in building processes that include both independent and dependent variables (see Chapter 4). Manag- ers and researchers must be careful to identify relevant and actionable variables. Relevant means that a change in the variable matters and actionable means that a variable can be controlled by managerial action. Superfluous variables are those that are neither relevant nor actionable and should not be included in a study. Theory should help distinguish rel- evant from superfluous variables.

continuous variable

A variable that can take on a range of values that correspond to some quantitative amount.

categorical variable

A variable that indicates membership in some group.

classificatory variable

Another term for a categorical variable because it classifies units into categories.

dependent variable

A process outcome or a variable that is predicted and/or explained by other variables.

independent variable

A variable that is expected to influence the dependent variable in some way.

Several variables describe child consumers. Their biological sex is a categorical variable; their age or how often they go out to the mall are continuous variables.

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CHAPTER 6 Problem Definition: The Foundation of Business Research • 119

The process of identifying the relevant variables overlaps with the process of determining the research objectives. Typically, each research objective will mention a variable or variables to be measured or analyzed. As the translation process proceeds through research objectives, research questions, and research hypotheses, it is usually possible to emphasize the variables that should be included in a study (as in Exhibit 6.5 and Exhibit 6.6).

research questions

Express the research objectives in terms of questions that can

be addressed by research.

Exhibit 6.6 includes some common business research hypotheses and a description of the key variables involved in each. In the first case, a regional grocery chain is considering offering a delivery service that would allow consumers to purchase groceries via the store website. They have conducted a trial of this in one market and have conducted a survey in that area. In the second case, a Korean automobile company is considering offering one of its models for sale in Europe. The company has also conducted a survey in two key European auto markets.

Write Research Objectives and Questions Both managers and researchers expect problem-definition efforts to result in statements of research questions and research objectives. At the end of the problem-definition stage, the researcher should prepare a written statement that clarifies any ambiguity about what the research hopes to accom- plish. This completes the translation process.

Research questions express the research objectives in terms of questions that can be addressed by research. For example, one of the key research questions involved in the opening vignette is “Are wages and long-haul distance related to driver loyalty and retention?” Hypotheses are more specific than research questions. One key distinction between research questions and hypotheses is that hypotheses can generally specify the direction of a relationship. In other words, when an

EXHIBIT 6.6

Example Business Decision Situations, Corresponding Research Hypotheses, and Variable Descriptions Example Business Decision Situations, Corresponding Research

Managerial Decision Research Question(s) Research Hypotheses Categorical Variable(s) Continuous Variable(s)

Retail grocer considering web-based delivery service

Is there sufficient demand?

How much should delivery personnel be paid?

Will delivery service (new retail form) cannibalize current business?

Projected sales volume will exceed $5 million annually.

Delivery personnel can be paid less than cashiers and achieve the same job satisfaction.

Web customers express lower intentions to visit store than other customers.

Type of employee (delivery, cashier, etc.)

Retail form (independent variable): classifies respondents based on whether they shopped (1) in store or (2) via the web (delivery)

Sales volume: dollar amount based on a test trial in one geographic market (i.e., Phoenix/ Scottsdale)

Hourly wages and satisfaction with pay

Intentions to visit store (dependent variable): the percentage likelihood that a survey respondent would visit the store within the next seven days.

What market segments should be served?

Does nationality matter? Will French and German consumers express interest in our product?

Does the attitude toward Korean companies influence purchase intentions?

French consumers have more interest in purchasing our product than German consumers.

Attitude toward Korean companies is related positively to product purchase interest.

Nationality (independent variable): represents which country a survey respondent lives in: (1) France (2) Germany

Attitude toward Korean companies (independent variable): ratings scale that describes how favorably survey respondents view Korean companies (quality, reputation, value—higher scores mean better attitude)

Product purchase interest: ratings scale that shows how interested a consumer is in buying the Korean product (higher scores = more interest)

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120 • PART TWO Beginning Stages of the Research Process

121

independent variable goes up, we have sufficient knowledge to predict that the dependent variable should also go up (or down as the case may be). One key research hypothesis for Deland Trucking is:

Higher cents per mile are related positively to driver loyalty.

At times, a researcher may suspect that two variables are related but have insufficient theoretical rationale to support the relationship as positive or negative. In this case, hypotheses cannot be offered. At times in research, particularly in exploratory research, a proposal can only offer research questions. Research hypotheses are much more specific and therefore require considerably more theoretical support. In addition, research questions are interrogative, whereas research hypotheses are declarative.

Clarity in Research Questions and Hypotheses Research questions make it easier to understand what is perplexing managers and to indicate what issues have to be resolved. A research question is the researcher’s translation of the marketing prob- lem into a specific inquiry.

A research question can be too vague and general, such as “Is advertising copy 1 better than advertising copy 2?” Advertising effectiveness can be variously measured by sales, recall of sales message, brand awareness, intention to buy, recognition, or knowledge, to name a few possibilities. Asking a more specific research question (such as, “Which advertisement has a higher day-after recall score?”) helps the researcher design a study that will produce useful results, as seen in the Research Snapshot “Pricing Turbulence.” Research question answers should provide input that can be used as a standard for selecting from among alternative solutions. Problem definition seeks to state research questions clearly and to develop well-formulated, specific hypotheses.

RESEARCH SNaPSHOt

A heavy equipment distributor sought out research because it believed there was an opportunity to increase revenues by raising prices. After several weeks of discussion, interviews, and proposal reviews, they settled on a decision statement that asked, “In what ways could revenues be increased by altering pricing policies across customers?” A research project was conducted that offered the following deliverables: (1) demonstrate how much customer characteristics and environ- mental characteristics influence price elasticity and (2) identify market segments based on price elasticity. This led to several hypotheses including the following:

H1: The desired delivery time for equipment is negatively related to price sensitivity. H2: The degree of market turbulence is negatively related to price sensitivity.

In addition, a research question specifically addressing mar- ket segments was asked:

RQ1: Are there market segments that can be identified based on customers’ desired benefits or environmental characteristics?

In other words, the more critical a piece of heavy equipment is to a company, the less concerned they are with the price. Similarly, customers are less concerned with price in markets

that are more turbulent, meaning there are ever-changing environmental, competitive, and political pressures.

A study of heavy equipment purchasers around the world supported both hypotheses. For business seg- ments where delivery time is of critical importance, higher prices can be charged without the fear of losing business. Similarly, in turbulent international markets, customers have other important concerns that make them less sensitive to equipment price and more sensitive to reliability and ser- vice. In the end, the heavy equipment company was able to build customer characteristics data into a DSS system that automated prices.

Interestingly, management did not express any concerns about either market segments or market turbulence in the initial interviews. Thus, this research succeeded because good research objectives, questions, and hypotheses were developed before any study was implemented.

Sources: Smith, M. F., I. Sinha, R. Lancianai, and H. Forman, “Role of Market Turbulence in Shaping Pricing,” Industrial Marketing Management 28 (November 1999), 637–649; Peters, G., “Combating Too Much Informa- tion,” Industrial Distribution 94 (December 2005), 22.

Pricing Turbulence

‘‘I don’t know the key to success, but the key to failure is trying to please everybody.’’—BILL COSBY

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A sales manager may hypothesize that salespeople who show the highest job satisfaction will be the most productive. An advertising manager may believe that if consumers’ attitudes toward a product are changed in a positive direction, consumption of the product also will increase. Hypoth- eses are statements that can be empirically tested.

A formal hypothesis has considerable practical value in planning and designing research. It forces researchers to be clear about what they expect to find through the study, and it raises crucial questions about data required. When evaluating a hypothesis, researchers should ensure that the information collected will be useful in decision making. Notice how the following hypotheses express expected relationships between variables:

■ There is a positive relationship between the presence of younger children in the home and buying on the Internet.

■ Regions that receive less advertising support will report lower sales for salespeople. ■ Consumers will experience cognitive dissonance after the decision to purchase a 3D television. ■ Opinion leaders are more affected by mass media communication sources than are nonleaders. ■ Among nonexporters, the degree of perceived importance of overcoming barriers to exporting is

related positively to general interest in exporting (export intentions).8

Management is often faced with a “go/no go” decision. In such cases, a research question or hypothesis may be expressed in terms of a meaningful barrier that represents the turning point in such a decision. In this case, the research involves a managerial action standard that specifies a specific performance criterion upon which a decision can be based. If the criterion to be measured (for example, sales or attitude changes) turns out to be higher than some predetermined level, man- agement will do A; if it is lower, management will do B.9 In Exhibit 6.6, the specified sales volume of $5 million represents a managerial action standard for the retail grocery chain.

Research objectives also should be limited to a manageable number. Fewer study objectives make it easier to ensure that each will be addressed fully. It becomes easy to lose focus with too many research objectives.

Exhibit 6.7 summarizes how a decision statement (corresponding to a business research prob- lem) leads to research objectives that become a basis for the research design. Once the research has been conducted, the results may show an unanticipated aspect of the problem and suggest a need for additional research to satisfy the main objective. Accomplished researchers who have had the experience of uncovering additional aspects of a particular research problem after finishing fieldwork recommend designing studies that include questions designed to reveal the unexpected.

managerial action standard

A specific performance criterion upon which a decision

can be based.

EXHIBIT 6.7

Influence of Decision Statement of Marketing Problem on Research Objectives and Research Designs

Specific objective – 1

Research design

Results

Statement of marketing problem

Broad research

objectives

Exploratory research (optional)

Specific objective – 2

Specific objective – 3

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122 • PART TWO Beginning Stages of the Research Process

How Much Time Should Be Spent on Problem Definition? Like almost every other decision in business research, budget constraints usually influence how much effort is spent on problem definition. Business situations can be complex and numerous variables may be relevant. Searching for every conceivable cause and minor influence is impractical. The more important the decision faced by management, the more resources should be allocated toward problem definition. While not a guarantee, allowing more time and spending more money will help make sure the research objectives that result are relevant and can demonstrate which influences management should focus on.

Managers, being responsible for decision making, may wish the problem-definition process to proceed quickly. Researchers who take a long time to produce a set of research objectives can frustrate managers. However, the time taken to identify the correct problem is usually time well spent.

The Research Proposal The research proposal is a written statement of the research design. It always includes a statement explaining the purpose of the study (in the form of research objectives or deliverables) and a defi- nition of the problem, often in the form of a decision statement. A good proposal systematically outlines the particular research methodology and details procedures that will be used during each stage of the research process. Normally a schedule of costs and deadlines is included in the research proposal. The research proposal becomes the primary communication document between the researcher and the research user.

Exhibit 6.8 illustrates an abbreviated proposal for a short research project conducted for the Internal Revenue Service (IRS) that explores public attitudes toward a variety of tax-related issues.

The Proposal As a Planning Tool Preparation of a research proposal forces the researcher to think critically about each stage of the research process. Vague plans, abstract ideas, and sweeping generalizations about problems or proce- dures must become concrete and precise statements about specific events. Data requirements and research procedures must be specified clearly so others may understand their exact implications. All ambiguities about why and how the research will be conducted must be clarified before the proposal is complete.

The researcher submits the proposal to management for acceptance, modification, or rejection. Research clients (management) evaluate the proposed study with particular emphasis on whether or not it will provide useful information, and whether it will do so within a reasonable resource budget. Initial proposals are almost always revised after the first review.

The proposal helps managers decide if the proper information will be obtained and if the pro- posed research will accomplish what is desired. If the problem has not been adequately translated into a set of specific research objectives and a research design, the client’s assessment of the proposal will help ensure that the researchers revise it to meet the client’s information needs.

An effective proposal communicates exactly what information will be obtained, where it will be obtained, and how it will be obtained. For this reason, it must be explicit about sample selec- tion, measurement, fieldwork, and data analysis. For instance, most proposals involving descriptive research include a proposed questionnaire (or at least some sample questions).

The format for a research proposal follows the stages in the research process outlined in Chapter 4. At each stage, one or more questions must be answered before the researcher can select one of the various alternatives. For example, before a proposal can be completed, the researcher needs to know what is to be measured. A simple statement like “market share” may not be enough;

research proposal

A written statement of the research design.

CHAPTER 6 Problem Definition: The Foundation of Business Research • 123

EXHIBIT 6.8

An Abbreviated Version of a Research Proposal for the IRS

market share may be measured by auditing retailers’ or wholesalers’ sales, using trade association data, or asking consumers what brands they buy. What is to be measured is just one of many impor- tant questions that must be answered before setting the research process in motion. Exhibit 6.9 presents an overview of some of the basic questions that managers and researchers typically must answer when planning a research design.

Current Situation Public perception of the IRS appears to be extremely negative. The IRS is the brunt of jokes, and the public avoids contact with any IRS entity. As a result, taxpayers are more inclined to cheat on their returns and many services provided by the IRS to assist taxpayers in preparing their tax returns and to help them understand ways they can avoid paying unnecessary taxes and penalties go unused. In addition, negative attitude lessens the Service’s ability to effectively lobby for policy changes. The key decision faced by the IRS due to this situation can be stated as,

What steps could be taken to effectively improve consumer perceptions of the IRS and help design more user- friendly services?

Purpose of the Research The general purpose of the study is to determine the taxpaying public’s perceptions of the role of the IRS in administering the tax laws. In defining the limits of this study, the IRS identified the study areas to be addressed. A careful review of those areas led to the identification of the following specific research objectives:

1. To identify the extent to which taxpayers cheat on their returns, their reasons for doing so, and approaches that can be taken to deter this kind of behavior

2. To determine taxpayers’ experience and level of satisfaction with various IRS services

3. To determine what services taxpayers need 4. To develop an accurate profile of taxpayers’ behavior

relative to the preparation of their income tax returns 5. To assess taxpayers’ knowledge and opinions about

various tax laws and procedures

Research Design The survey research method will be the basic research design. Each respondent will be interviewed in his or her home. The personal interviews are generally expected to last between 35 and 45 minutes, although the length will vary depending on the previous tax-related experiences of the respondent. For example, if a respondent has never been audited, questions on audit experience will not be addressed. Or, if a respondent has never contacted the IRS for assistance, certain questions concerning reactions to IRS services will be skipped.

Some sample questions that will be asked are

Did you or your spouse prepare your federal tax return for (year)?

■ Self ■ Spouse ■ Someone else

Did the federal income tax package you received in the mail contain all the forms necessary for you to fill out your return?

■ Yes ■ No

■ Didn’t receive one in the mail ■ Don’t know

If you were calling the IRS for assistance and no one was able to help you immediately, would you rather get a busy signal or be asked to wait on hold?

■ Busy signal ■ Wait on hold ■ Neither ■ Don’t know

During the interview a self-administered questionnaire will be given to the taxpayer to ask certain sensitive questions, such as

Have you ever claimed a dependent on your tax return that you weren’t really entitled to?

■ Yes ■ No

Sample Design A survey of approximately 5,000 individuals located in 50 counties throughout the country will provide the database for this study. The sample will be selected on a probability basis from all households in the continental United States.

Eligible respondents will be adults over the age of 18. Within each household an effort will be made to interview the individual who is most familiar with completing the federal tax forms. When there is more than one taxpayer in the household, a random process will be used to select the taxpayer to be interviewed.

Data Gathering The fieldworkers of a consulting organization will conduct the interviews.

Data Processing and Analysis Standard editing and coding procedures will be utilized. Simple tabulation and cross-tabulations will be utilized to analyze the data.

Report Preparation A written report will be prepared, and an oral presentation of the findings will be made by the research analyst at the convenience of the IRS.

Budget and Time Schedule Any complete research proposal should include a schedule of how long it will take to conduct each stage of the research and a statement of itemized costs.

Based on A General Taxpayer Opinion Survey, Office of Planning and Research, Internal Revenue Service, March

1980.

Source: Based on A General Taxpayer Opinion Survey, “Office of Planning and Research,” Internal Revenue Service, March 1980.

124 • PART TWO Beginning Stages of the Research Process

The Proposal as a Contract When the research will be conducted by a consultant or an outside research supplier, the written proposal serves as that person’s bid to offer a specific service. Typically, a client solicits several com- petitive proposals, and these written offers help management judge the relative quality of alterna- tive research suppliers.

A wise researcher will not agree to do a research job for which no written proposal exists. The proposal also serves as a contract that describes the product the research user will buy. In fact, the proposal is in many ways the same as the final research report without the actual results. Misstate- ments and faulty communication may occur if the parties rely only on each individual’s memory

EXHIBIT 6.9

Basic Points Addressed by Research Proposals

Decisions to Make Basic Questions

Problem definition What is the purpose of the study? How much is already known? Is additional background information necessary? What is to be measured? How? Can the data be made available? Should research be conducted? Can a hypothesis be formulated?

Selection of basic research design What types of questions need to be answered? Are descriptive or causal findings required? What is the source of the data? Can objective answers be obtained by asking people? How quickly is the information needed? How should survey questions be worded? How should experimental manipulations be made?

Selection of sample Who or what is the source of the data? Can the target population be identified? Is a sample necessary? How accurate must the sample be? Is a probability sample necessary? Is a national sample necessary? How large a sample is necessary? How will the sample be selected?

Data gathering Who will gather the data? How long will data gathering take? How much supervision is needed? What procedures will data collectors need to follow?

Data analysis and evaluation Will standardized editing and coding procedures be used? How will the data be categorized? Will computer or hand tabulation be used? What is the nature of the data? What questions need to be answered? How many variables are to be investigated simultaneously? What are the criteria for evaluation of performance? What statistical tools are appropriate?

Type of report Who will read the report? Are managerial recommendations requested? How many presentations are required? What will be the format of the written report?

Overall evaluation How much will the study cost? Is the time frame acceptable? Is outside help needed? Will this research design attain the stated research objectives? When should the research begin?

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CHAPTER 6 Problem Definition: The Foundation of Business Research • 125

of what occurred at a planning meeting. The proposal creates a record, which greatly reduces conflicts that might arise after the research has been conducted. Both the researcher and the research client should sign the proposal indicating agreement on what will be done.

The proposal then functions as a formal, written statement of agreement between marketing executives and researchers. As such, it protects the researcher from criti- cisms such as, “Shouldn’t we have had a larger sample?” or “Why didn’t you use a focus group approach?” As a record of the researcher’s obligation, the proposal also provides a standard for determining whether the actual research was conducted as origi- nally planned.

Suppose in our Deland Trucking case, following the research, David is unhappy with the nature of the results because they

indicate that higher cents per mile do, in fact, impact driver loyalty. This is something that David may not wish to face. In his despair, he complains to James saying,

“What I really wanted was a recruitment expense study, yet you provide results indicating my wages are too low! Why should I pay you?”

James can refer back to the research proposal, which is signed by David. He can point right to the deliverables described above showing that David agreed to a study involving driver loyalty and the organizational characteristics that lead to loyalty. The proposal certainly protects the researcher in this case. In most cases like this, after the initial emotional reaction to unflattering results, the client comes around and realizes the report contents include information that will be helpful. Realize too that the proposal protects David in case James produced a study that failed to address the research objectives included in the proposal.

In basic research efforts, a formal proposal serves much the same purpose. Funded busi- ness research generally refers to basic research usually performed by academic researchers and supported by some public or private institution. Most commonly, researchers pursue federal government grants. A very detailed proposal is usually needed for federal grants, and the agree- ment for funding is predicated on the research actually delivering the results described in the proposal.

One important comment needs to be made about the nature of research proposals. Not all proposals follow the same format. A researcher can adapt his or her proposal to the target audience or situation. An extremely brief proposal submitted by an organization’s internal research depart- ment to its own executives bears little resemblance to a complex proposal submitted by a university professor to a federal government agency to research a basic business issue.

Anticipating Outcomes As mentioned above, the proposal and the final research report will contain much of the same information. The proposal describes the data collection, measurement, data analysis, and so forth, in future tense. In the report, the actual results are presented. In this sense, the proposal anticipates the research outcome.

Experienced researchers know that research often fails because the problem-definition process breaks down or because the research client never truly understood what a research project could or couldn’t do. While it probably seems as though the proposal should make this clear, any shortcoming

funded business research

Refers to basic research usu- ally performed by academic

researchers that is financially supported by some public or

private institution, as in federal government grants.

Congress fights about everything … including how to spend taxpayers’ money on federal research grants.

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126 • PART TWO Beginning Stages of the Research Process

in the proposal can contribute to a communication failure. Thus, any tool that helps communica- tion become as clear as can be is valued very highly.

Dummy Tables One such tool that is perhaps the best way to let management know exactly what kind of results will be produced by research is the dummy table. Dummy tables are placed in research proposals and are exact representations of the actual tables that will show results in the final report with one exception: the results are hypothetical. They get the name because the researcher fills in, or “dum- mies up,” the tables with likely but fictitious data. Dummy tables include the tables that will present hypothesis test results. In this way, they are linked directly to research objectives.

A research analyst can present dummy tables to the decision maker and ask, “Given findings like these, will you be able to make a decision?” If the decision maker says yes, the proposal may be accepted. However, if the decision maker cannot see how results like those in the dummy tables will help make the needed decision(s), it may be back to the drawing board. In other words, the client and researcher need to rethink what research results are necessary to solve the problem. Sometimes, examining the dummy tables may reveal that a key variable is missing or that some dependent variable is really not relevant. In other words, the problem is clarified by deciding on action standards or performance criteria and recognizing the types of research findings necessary to make specific decisions.

Example Dummy Table Exhibit 6.10 shows a dummy table taken from the research proposal for David Deland’s truck- ing company. From it, David can see that it shows what things most determine driver loyalty. If the results turn out as shown in the dummy table, it would suggest that David needs to perhaps increase his compensation or reduce the number of long-haul routes that his drivers must conduct.

dummy tables

Tables placed in research proposals that are exact representations of the actual tables that will show results in the final report with the exception that the results are hypothetical (fictitious).

EXHIBIT 6.10

A Dummy Table for David Deland

Regression Table: Results Showing Which Variables Determine Driver Loyalty

Standardized Regression Coefficient Rank (Importance)

Increase cents/mile .50** 1

Number of long-haul routes (per month)

–.45** 2

Days off (per month) .30** 3

Vehicle quality .25* 4

Benefits provided .15 5

* p-value < .01 ** p-value < .05

While some tables may require some additional explanation from the researcher, every effort should be made to allow tables to stand alone and be interpreted by someone who is not an expe- rienced researcher. In other words, the user should be able to understand the results and surmise implications that the results imply. When the final report is compiled, these tables will be included with the dummy results replaced with the actual research results.

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CHAPTER 6 Problem Definition: The Foundation of Business Research • 127

SUMMARY 1. Explain why proper “problem definition” is essential to useful business research. Problem definition is the process of defining and developing a decision statement and the steps involved in translating it into more precise research terminology, including a set of research objectives. While it is difficult to point to any particular research stage as the most important, a strong case can be made for this, the first stage. If this step falls apart, the entire research design is misguided. Effective problem definition helps make sure the research objectives are relevant and useful—meaning the results will actually be used. If problem definition is glossed over or done poorly, the results are likely irrelevant and potentially harmful.

2. Know how to recognize problems. Problems and opportunities are usually associated with differences. The differences can occur because of changes in some situation, or they can occur because expectations were unrealistic. Problems occur when there is a difference, or gap, between the current situation and a more ideal situation. One very common type of gap is when business performance does not match the expectations of performance in that dimension. In addition, opportunities exist when actual performance in some area does not match the potential perfor- mance. Research can supply information to help close the gap. Thus, problems are noticed by spotting these gaps. While many of these gaps may just be symptoms, further steps are taken to make sure that research addresses relevant issues, not just symptoms.

3. Translate managerial decision statements into relevant research objectives. The problem- definition process outlined in the chapter can help make sure that the research objectives are relevant. A situation analysis is helpful in this regard. In particular, interviews that identify symp- toms and then probe the respondent for potential causes of these symptoms are helpful. One tool to help in this process is the “What has changed?” technique. The research objectives, once written, also indicate what variables are likely needed in the study.

4. Translate research objectives into research questions and/or research hypotheses. Research questions simply restate the research objectives in the form of a question. When the researcher has sufficient theoretical reasoning to make a more specific prediction that includes the direction of any predicted relationship, the research question can be translated into one or more research hypotheses.

5. Outline the components of a research proposal. The research proposal is a written state- ment of the research design that will be followed in addressing a specific problem. The research proposal allows managers to evaluate the details of the proposed research and determine if altera- tions are needed. Most research proposals include the following sections: decision description, purpose of the research including the research objectives, research design, sample design, data gathering and/or fieldwork techniques, data processing and analysis, budget, and time schedule.

6. Construct dummy tables as part of a research proposal. Dummy tables are included in research proposals and look exactly like the real tables that will be included in the final research report. However, they cannot actually contain results since the study has not yet been done. So, they include hypothetical results that look as much as possible like the actual results. These tables are a very good tool for communicating the value of a research project to management because they provide a real sense for implications that may result from the research.

categorical variable, 119 classificatory variable, 119 constant, 118 continuous variable, 119 decision statement, 107 dependent variable, 119 dummy tables, 127

funded business research, 126 independent variable, 119 interrogative techniques, 113 managerial action standard, 122 probing, 114 problem, 111 problem definition, 107

research proposal, 123 research questions, 120 situation analysis, 112 unit of analysis, 118 variable, 118

KEY TERMS AND CONCEPTS

128 Part 2: Beginning Stages of the Research Process

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is a decision statement? How does the focus on an irrel-

evant decision affect the research process? 2. Define problem recognition. How is this process like translating

text from one language into another? What role does “probing” play in this process?

3. List and describe four factors that influence how difficult the problem-definition process can be.

4. What are three types of gaps that exist, indicating that research may be needed to assist a business in making some decision?

5. Examine an article in the Wall Street Journal or a similar source that discusses a business situation of a company in the electron- ics or defense industry. Identify a problem that exists with the company. Develop some research objectives that you believe correspond to the problem.

6. What is a situation analysis? How can it be used to separate symptoms from actual problems?

7. Define unit of analysis in a marketing research context. 8. Find some business journal articles that deal with culture and

international expansion. Find one that lists some hypotheses. What kinds of decisions might be assisted by the results of testing these hypotheses?

9. List and describe at least four terms that can describe the nature of a variable.

10. For each of the following variables, explain why it should be considered either continuous or categorical: a. Whether or not a university played in a football bowl game

during 2011 b. The average wait time a customer has before being served

in a full-service restaurant c. Letter grades of A, B, C, D, or F d. The job satisfaction of a company’s salespeople e. A consumer’s age

11. Write at least three examples of hypotheses that involve a mana- gerial action statement. Provide a corresponding decision state- ment for each.

12. What are the major components of a research proposal? How does a research proposal assist the researcher?

13. The chapter provides an example dummy table for the Deland Trucking vignette. Provide another example dummy table that corresponds to this same situation.

14. Evaluate the following statements of business research prob- lems. For each provide a decision statement and corresponding research objectives: a. A farm implement manufacturer: Our objective is to learn

the most effective form of advertising so we can maximize product line profits.

b. An employees’ credit union: Our problem is to determine the reasons why employees join the credit union, determine members’ awareness of credit union services, and measure attitudes and beliefs about how effectively the credit union is operated.

c. The producer of a television show: We have a marketing problem. The program’s ratings are low. We need to learn how we can improve our ratings.

d. A soft-drink manufacturer: The marketing problem is that we do not know if our bottlers are more satisfied with us than our competitors’ bottlers are with them.

e. A women’s magazine: Our problem is to document the demographic changes that have occurred in recent decades in the lives of women and to put them in historical perspec- tive; to examine several generations of American women through most of this century, tracking their roles as students, workers, wives, and mothers and noting the changes in tim- ing, sequence, and duration of these roles; and to examine at what age and for how long a woman enters various stages of her life: school, work, marriage, childbearing, divorce. This will be accomplished by analyzing demographic data over several generations.

f. A manufacturer of fishing boats: The problem is to deter- mine sales trends over the past five years by product cat- egory and to determine the seasonality of unit boat sales by quarters and by region of the country.

g. The inventor of a tension-headache remedy (a cooling pad that is placed on the forehead for up to four hours): The purpose of this research is (1) to identify the market poten- tial for the product, (2) to identify what desirable features the product should possess, and (3) to determine possible advertising strategies/channel strategies for the product.

15. Comment on the following statements and situations: a. “The best researchers are prepared to rethink and rewrite

their proposals.” b. “The client’s signature is an essential element of the research

proposal.” 16. You have been hired by a group of hotel owners, restaurant

owners, and other people engaged in businesses that benefit from tourism on South Padre Island, Texas. They wish to learn how they can attract a large number of college students to their town during spring break. Define the marketing decision statement.

17. You have been hired by a local Big Brothers and Big Sisters organization to learn how they can increase the number of males who volunteer to become Big Brothers to fatherless boys. Define your research objectives.

RESEARCH ACTIVITIES 1. ’NEt Examine the website for International Communications

Research (http://icrsurvey.com).10 What services do they seem to offer that fall into the problem-definition process?

2. Consider the current situation within your local university music department. Assuming it stages musical productions to

which audiences are invited and for which tickets are sold, describe the marketing situation it faces. Prepare a research proposal that would help it address a key decision. Make sure it includes at least one dummy table.

Chapter 6: Problem Definition: The Foundation of Business Research 129

E-ZPass In the 1990s, a task force was formed among execu- tives of seven regional transportation agencies in the New York–New Jersey area.11 The mission of the task force was to investigate the feasibility and desirability

of adopting electronic toll collection (ETC) for the interregional roadways of the area. Electronic toll collection is accomplished by providing commuters with small transceivers (tags) that emit a tuned radio signal. Receivers placed at tollbooths are able to receive the radio signal and identify the commuter associated with the particular signal. Commuters establish ETC accounts that are debited for each use of a toll road or facility, thus eliminating the need for the com- muter to pay by cash or token. Because the radio signal can be read from a car in motion, ETC can reduce traffic jams at toll plazas by allowing tag holders to pass through at moderate speeds.

At the time the New York and New Jersey agencies were studying the service, electronic toll collection was already being used success- fully in Texas and Louisiana. Even though several of the agencies had individually considered implementing ETC, they recognized that independent adoption would fall far short of the potential benefits achievable with an integrated interregional system.

The task force was most interested in identifying the ideal config- uration of service attributes for each agency’s commuters and deter- mining how similar or different these configurations might be across agencies. The task force identified a lengthy list of attributes that was ultimately culled to six questions:

• How many accounts are necessary and what statements will be received?

• How and where does one pay for E-ZPass? • What lanes are available for use and how are they controlled? • Is the tag transferable to other vehicles? • What is the price of the tag and possible service charge? • What are other possible uses for the E-ZPass tag (airport park-

ing, gasoline purchases, and so forth)?

From a researcher’s perspective, it also seemed important to assess commuter demand for the service. However, the task force was not convinced that it needed a projection of demand, because it was committed to implementing ETC regardless of initial commuter acceptance. The task force considered its primary role to be investigating commuters’ preferences for how the service should be configured ideally.

Questions 1. Evaluate the problem-definition process. Has the problem been

defined adequately so that a relevant decision statement can be written?

2. What type of research design would you recommend for this project?

3. What research questions might be tested? 4. What might a dummy table include in this research proposal?

CASE 6.1

Cane’s Goes International Raising Cane’s is a fast-food chicken finger estab- lishment based in Baton Rouge, Louisiana. Cane’s restaurants are popular throughout the Gulf South. Cane’s recently has been approached by people

interested in opening Cane’s restaurants in other countries. The best contact is an Australian. However, Cane’s has also been approached about outlets in Montreal, Quebec, and in Monterrey, Mexico. Cane’s prepares high-quality fried chicken fingers and

has a limited menu consisting of fingers, fries, slaw, and lemonade (http://www.raisingcanes.com).

Questions 1. Write a decision statement for Raising Cane’s. 2. Write corresponding research objectives and research questions. 3. What role would a proposal play in assisting this research effort

and in assisting Cane’s in improving their business situation?

CASE 6.2

Deland Trucking Based on the case scenario described throughout this chapter, prepare a research proposal that addresses this situation.

CASE 6.3

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131

After studying this chapter, you should be able to

1. List and understand the differences between qualitative research and quantitative research

2. Understand the role of qualitative research in exploratory research designs

3. Describe the basic qualitative research orientations

4. Prepare a focus group interview outline

5. Recognize technological advances in the application of qualitative research approaches

6. Recognize common qualitative research tools and know the advantages and limitations of their use

7. Know the risks associated with acting on only exploratory results

C H A P T E R

I s this shoe too cool? That was really the question asked by VF Corporation when they acquired Vans, the company that makes the shoe shown here.1 Vans traditionally are synonymous with skateboarding and

skateboard culture. Readers that are unfamiliar with skate- boarding may well have never heard of the company. However, a reader that is part of the skateboard culture is probably look- ing down at his or her Vans right now!

Former Vans CEO Gary Schoenfeld points out that a decade before the acquisition (a $396 million deal), Vans was practically a dead brand.2 However, the last 10 years have seen a revival in skateboard interest and Vans has remained the number one skateboard shoe provider. Now, the incoming management team has been given the task of deciding how to raise Vans sales to $500 million per year.

Where will the growth come from? Should the company define itself as a “skateboard footwear” company, a “lifestyle” company, or as the icon for the skate culture? Answering this question will require a deeper interpretation of the meaning of the “Van.”

Skateboarding is a dynamic activity. A study by Board-Trac suggests that today over one in four skateboarders is female, as opposed to fewer than one in ten as recently as 2000.3

So, what exactly is in the mind and heart of a “boarder”? Two important research questions involve “What is the meaning of a pair of Vans?” and “What things define the skateboarding experience?”

Questions like these call for qualitative research methods.4 Not just any researcher is “fit” for this job. One way to collect this data is to hire young, energetic research employees to become “boarders” and immerse themselves into the culture.

They may have to “Casper” like a “flatland techer” while probing for meaning among the discussion and activities of the other boarders. Here, Vans may find that their brand helps identify a boarder and make them feel unique in some ways. If so, Vans may want to investigate increasing their product line beyond shoes and simple apparel.

LEARNING OUTCOMES

Qualitative Research Tools

Chapter Vignette:

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Introduction Chemists sometimes use the term qualitative analysis to mean research that determines what some compound is made of. In other words, the focus is on the inner meaning of the chemical—its qualities. As the word implies, qualitative research is interested more in qualities than quantities. Therefore, qualitative research is not about applying specific numbers to measure variables or using statistical procedures to numerically specify a relationship’s strength.

What Is Qualitative Research? Qualitative business research is research that addresses business objectives through techniques that allow the researcher to provide elaborate interpretations of market phenomena without depend- ing on numerical measurement. Its focus is on discovering true inner meanings and new insights. Qualitative research is very widely applied in practice. There are many research firms that specialize in qualitative research.

Qualitative research is less structured than most quantitative approaches. It does not rely on self-response questionnaires containing structured response formats. Instead, it is more researcher- dependent in that the researcher must extract meaning from unstructured responses, such as text from a recorded interview or a collage representing the meaning of some experience, such as skate- boarding. The researcher interprets the data to extract its meaning and converts it to information.

Uses of Qualitative Research Mechanics can’t use a hammer to fix everything that is broken. Instead, the mechanic has a toolbox from which a tool is matched to a problem. Business research is the same. The researcher has many tools available and the research design should try to match the best tool to the research objec- tive. Also, just as a mechanic is probably not an expert with every tool, each researcher usually has special expertise with a small number of tools. Not every researcher has expertise with tools that would comprise qualitative research.

Generally, the less specific the research objective, the more likely that qualitative research tools will be appropriate. Also, when the emphasis is on a deeper understanding of motivations or on developing novel concepts, qualitative research is often very appropriate. The following list repre- sents common situations that often call for qualitative research:5

1. When it is difficult to develop specific and actionable problem statements or research objec- tives. For instance, if after several interviews with the research client the researcher still can’t determine exactly what needs to be measured, then qualitative research approaches may help with problem definition. Qualitative research is often useful to gain further insight and crystal- lize the research problem.

2. When the research objective is to develop an understanding of some phenomena in great detail and in much depth. Qualitative research tools are aimed at discovering the pri- mary themes indicating human motivations and the documentation of activities is usually very complete. Often qualitative research provides richer information than quantitative approaches.

qualitative business research

Research that addresses business objectives through

techniques that allow the researcher to provide elaborate interpretations of phenomena

without depending on numerical measurement; its focus is on discovering true

inner meanings and new insights.

researcher-dependent

Research in which the researcher must extract

meaning from unstructured responses such as text from a

recorded interview or a collage representing the meaning of

some experience.

Depth interviews of Vans wearers in which people describe in detail why they wear Vans will also be useful. Vans shouldn’t be surprised if they find a significant portion of their shoes are sold to people like Mr. Samuel Teel, a retired attorney from Toledo, Ohio. Sam is completely unaware of

the connection between Vans and skateboarding. He likes them because he doesn’t have to bend to tie his shoes! Maybe there are some secondary segments that could bring growth to Vans. But marketing to them could complicate things—who knows?

132 • PART TWO Beginning Stages of the Research Process

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3. When the research objective is to learn how a phenomena occurs in its natural setting or to learn how to express some concept in colloquial terms. For example, how do consumers actu- ally use a product? Or, exactly how does the accounting department process invoices? While a survey can probably ask many useful questions, observing a product in use or watching the invoice process will usually be more insightful. Qualitative research produces many product and process improvement ideas.

4. When some behavior the researcher is studying is particularly context dependent—meaning the reasons something is liked or some behavior is performed depend very much on the par- ticular situation surrounding the event. Understanding why Vans are liked is probably difficult to determine correctly outside the skating environment.

5. When a fresh approach to studying some problem is needed. This is particularly the case when quantitative research has yielded less than satisfying results. Qualitative tools can yield unique insights, many of which may lead the organization in new directions.

Qualitative “versus” Quantitative Research In social science, one can find many debates about the superiority of qualitative research over quantitative research or vice versa.6 We’ll begin by saying that this is largely a superfluous argument in either direction. The truth is that qualitative research can accomplish research objectives that quantitative research cannot. Similarly truthful, but no more so, quantitative research can accomplish objectives that qualitative research cannot. The key to successfully using either is to match the right approach to the right research context.

Many good research projects combine both qualitative and quantitative research. For instance, developing valid survey measures requires first a deep understanding of the con- cept to be measured and a description of the way these ideas are expressed in everyday language. Both of these are tasks best suited for qualitative research. However, validat- ing the measure formally to make sure it can reliably capture the intended concept will likely require quantitative research.7 Also, qualitative research may be needed to separate symptoms from problems and then quantitative research can follow up to test relation- ships among relevant variables. The Research Snapshot “Discoveries at P&G!” describes one such situation.8

Qualitative researchers can learn about the skating experience by becoming immersed in the culture.

We have been working with the online survey in this class. This survey primarily deals with quantitative information rather than qualitative information. However, the question that asks the respondent to provide suggestions about improving the quality of business education at your school is qualitative in nature. Look over the comments provided by the students in your class. First, read through all the comments. Then, identify the major themes or issues that are present. You should be able to identify a small number of issues that are mentioned by multiple respondents. Based on these comments, what suggestion would you offer administrators at your school for improving the educational environment?

Each of these describes a scenario that may require an exploratory orientation. Previously, we defined exploratory research as appropriate in ambiguous situations or when new insight is needed. We indicated that exploratory research approaches are sometimes needed just to reach the appropriate problem statement and research objectives. While equat- ing qualitative research with exploratory research is an oversimplification, the application of qualitative tools can help clear up ambiguity and provide innovative ideas.

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Quantitative business research can be defined as business research that addresses research objectives through empirical assessments that involve numerical measurement and analysis approaches. Qualitative research is more apt to stand on its own in the sense that it requires less interpretation. For example, quantitative research is quite appropriate when a research objective involves a managerial action standard. For example, a salad dressing company considered changing its recipe.9 The new recipe was tested with a sample of consumers. Each consumer rated the prod- uct using numeric scales. Management established a rule that a majority of consumers rating the new product higher than the old product would have to be established with 90 percent confidence before replacing the old formula. A project like this can involve both quantitative measurement in the form of numeric rating scales and quantitative analysis in the form of applied statistical procedures.

Contrasting Qualitative and Quantitative Methods Exhibit 7.1 illustrates some differences between qualitative and quantitative research. Certainly, these are generalities and exceptions may apply. However, it covers some of the key distinctions. The Research Snapshot “Discoveries at P&G!” also introduces qualitative research.

Quantitative researchers direct a considerable amount of activity toward measuring concepts with scales that either directly or indirectly provide numeric values. The numeric values can then be used in statistical computations and hypothesis testing. As will be described in detail later, this process involves comparing numbers in some way. In contrast, qualitative researchers are more interested in observing, listening, and interpreting. As such, the researcher is intimately involved in the research process and in constructing the results. For these reasons, qualitative research is said to be more subjective, meaning that the results are researcher-dependent. Different researchers may reach different conclusions based on the same interview. In that respect, qualitative research lacks intersubjective certifiability (sometimes called intersubjective verifiability), the ability of differ- ent individuals following the same procedures to produce the same results or come to the same conclusion. This should not necessarily be considered a weakness of qualitative research; rather it is simply a characteristic that yields differing insights. In contrast, when a survey respondent provides

quantitative business research

Business research that addresses research objectives

through empirical assessments that involve numerical

measurement and analysis.

With literally thousands of products to manage, Procter & Gamble (P&G) finds itself in the situation to conduct qualita- tive research almost daily. P&G doesn’t introduce a product that hasn’t been reviewed from nearly every possible angle. Likewise, before taking a product to a new country, you can be confident that the product has been “focus grouped” in that environment.

P&G often uses qualitative research techniques to discover potential problems or opportunities for the company’s prod- ucts. For example, focus groups played a major role in Herbal Essences hair care’s new logo, advertising copy, reformulated ingredients, and new bottle design. The redesigned bottles for shampoo and conditioner bottles are curved in a yin and yang fashion so they can fit together.

At times, P&G seeks outside help for its research. Such was the case when P&G wanted a study of its own business prob- lems. The researchers selected began by applying qualitative research techniques including depth interviews, observational

techniques (shadowing), and focus groups on P&G managers and marketing employees. These interviews gave the research- ers the idea that perhaps P&G was suffering more from a man- agement problem than from a marketing problem. It helped form a general research question that asked whether business problems were really due to low morale among the employees. After a lot of qualitative interviews with dozens and dozens of P&G employees, a quantitative study followed up these findings and supported this idea, leading to suggestions for improving employee morale!

Sources: Nelson, Emily, “Focus Groupies: P&G Keeps Cincinnati Busy with All Its Studies,” Wall Street Journal 239 (January 24, 2002), A1, Eastern Edition; Stengel, J. R., A. L. Dixon, and C. T. Allen, “Listening Begins at Home,” Harvard Business Review (November 2003), 106–116.

Discoveries at P&G!

subjective

Results are researcher- dependent, meaning different

researchers may reach different conclusions based on the same

interview.

intersubjective certifiability

Different individuals following the same procedure will

produce the same results or come to the same conclusion.

RESEARCH SNAPSHOT

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a commitment score on a quantitative scale, it is thought to be more objective because the number will be the same no matter what researcher is involved in the analysis.

Qualitative research seldom involves samples with hundreds of respondents. Instead, a hand- ful of people are usually the source of qualitative data. This is perfectly acceptable in discovery- oriented research. All ideas would still have to be tested before adopted. Does a smaller sample mean that qualitative research is cheaper than quantitative? Perhaps not. Although fewer respon- dents have to be interviewed, the greater researcher involvement in both the data collection and analysis can drive up the costs of qualitative research.

Given the close relationship between qualitative research and exploratory designs, it should not be surprising that qualitative research is most often used in exploratory designs. Small samples, interpretive procedures that require subjective judgments, and the unstructured interview format all make traditional hypotheses testing difficult with qualitative research. Thus, these procedures are not best suited for drawing definitive conclusions, as would be expected from causal designs involving experiments. These disadvantages for drawing inferences, however, become advantages when the goal is to draw out potential explanations because the researcher spends more time with each respondent and is able to explore much more ground due to the flexibility of the procedures.

Contrasting Exploratory and Confirmatory Research Philosophically, research can be considered as either exploratory or confirmatory. Most explor- atory research designs produce qualitative data. Exploratory designs do not usually produce quantitative data, which represent phenomena by assigning numbers in an ordered and mean- ingful way. Rather than numbers, the focus of qualitative research is on stories, visual portrayals, meaningful characterizations, interpretations, and other expressive descriptions. Often, exploratory

qualitative data

Data that are not characterized by numbers, and instead are textual, visual, or oral; focus is on stories, visual portrayals, meaningful characterizations, interpretations, and other expressive descriptions.

quantitative data

Represent phenomena by assigning numbers in an ordered and meaningful way.

EXHIBIT 7.1

Comparing Qualitative and Quantitative Research

Qualitative Research Research Aspect Quantitative Research Discover Ideas, Used in Exploratory Research with General Research Objects

Common Purpose Test Hypotheses or Specific Research Questions

Observe and Interpret Approach Measure and Test

Unstructured, Free-Form Data Collection Approach

Structured Response Categories Provided

Researcher Is Intimately Involved. Results Are Subjective.

Researcher Independence

Researcher Uninvolved Observer. Results Are Objective.

Small Samples—Often in Natural Settings

Samples Large Samples to Produce Generalizable Results (Results That Apply to Other Situations)

Exploratory Research Designs

Most Often Used Descriptive and Causal Research Designs

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CHAPTER 7 Qualitative Research Tools • 135

research may be needed to develop the ideas that lead to research hypotheses. In other words, in some situa- tions the outcome of exploratory research is a testable research hypothesis. Confirmatory research then tests these hypotheses with quantitative data. The results of these tests help decision making by suggesting a spe- cific course of action.

For example, an exploratory researcher is more likely to adopt a qualitative approach that might involve trying to develop a deeper understanding of how families are impacted by changing economic conditions, investigating how people suffering eco- nomically spend scarce resources. This may lead to the development of a hypothesis that during challenging economic times consumers seek low-cost entertain-

ment such as movie rentals, but would not test this hypothesis. In contrast, a quantitative researcher may search for numbers that indicate economic trends. This may lead to hypothesis tests concern- ing how much the economy influences rental movie consumption.

Some types of qualitative studies can be conducted very quickly. Others take a very long time. For example, a single focus group analysis involving a large bottling company’s sales force can likely be conducted and interpreted in a matter of days. This would provide faster results than most descriptive or causal designs. However, other types of qualitative research, such as a participant- observer study aimed at understanding skateboarding, could take months to complete. A qualitative approach can, but does not necessarily, save time.

In summary, when researchers have limited experience or knowledge about a research issue, exploratory research is a useful step. Exploratory research, which often involves qualitative meth- ods, can be an essential first step to a more conclusive, confirmatory study by reducing the chance of beginning with an inadequate, incorrect, or misleading set of research objectives.

Orientations to Qualitative Research Qualitative research can be performed in many ways using many techniques. Orientations to qualitative research are very much influenced by the different fields of study involved in research. These orientations are each associated with a category of qualitative research. The major categories of qualitative research include

1. Phenomenology—originating in philosophy and psychology 2. Ethnography—originating in anthropology 3. Grounded theory—originating in sociology 4. Case studies—originating in psychology and in business research

Precise lines between these approaches are difficult to draw and there are clearly links among these orientations. In addition, a particular qualitative research study may involve elements of two or more approaches. However, each category does reflect a somewhat unique approach to human inquiry and approaches to discovering knowledge. Each will be described briefly below.

Phenomenology What Is a Phenomenological Approach to Research? Phenomenology represents a philosophical approach to studying human experiences based on the idea that human experience itself is inherently subjective and determined by the context in which people live.10 The phenomenological researcher focuses on how a person’s behavior is shaped by the relationship he or she has with the physical environment, objects, people, and situations. Phenomenological inquiry seeks to describe, reflect upon, and interpret experiences.

‘‘The cure for boredom is curiosity. There is no cure for curiosity.’’—DOROTHY PARkER

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phenomenology

A philosophical approach to studying human experiences

based on the idea that human experience itself is inherently

subjective and determined by the context in which

people live.

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Researchers with a phenomenological orientation rely largely on conversational interview tools. When conversational interviews are face to face, they are recorded either with video or audiotape and then interpreted by the researcher. The phenomenological interviewer is careful to avoid asking direct questions when at all possible. Instead, the research respondent is asked to tell a story about some experience. In addition, the researcher must do everything possible to make sure a respondent is comfortable telling his or her story. One way to accomplish this is to become a member of the group (for example, becoming a skateboarder in the scenario described earlier in this chapter). Another way may be to avoid having the person use his or her real name. This might be particularly necessary in studying potentially sensitive topics such as smoking, drug usage, shoplifting, or employee theft.

Therefore, a phenomenological approach to studying the meaning of Vans may require consid- erable time. The researcher may first spend weeks or months fitting in with the person or group of interest to establish a comfort level. During this time, careful notes of conversations are made. If an interview is sought, the researcher would likely not begin by asking a skateboarder to describe his or her shoes. Rather, asking for favorite skateboard incidents or talking about what makes a skate- boarder unique may generate productive conversation. Generally, the approach is very unstructured as a way of avoiding leading questions and to provide every opportunity for new insights.

What Is Hermeneutics? The term hermeneutics is important in phenomenology. Hermeneutics is an approach to under- standing phenomenology that relies on analysis of texts in which a person tells a story about him or herself.12 Meaning is then drawn by connecting text passages to one another or to themes expressed outside the story. These connections are usually facilitated by coding the key meanings expressed in the story. While a full understanding of hermeneutics is beyond the scope of this text, some of the terminology is used when applying qualitative tools. For instance, a hermeneutic unit refers to a text

hermeneutics

An approach to understanding phenomenology that relies on analysis of texts through which a person tells a story about him or herself.

hermeneutic unit

Refers to a text passage from a respondent’s story that is linked with a key theme from within this story or provided by the researcher.

A hermeneutic approach can be used to provide insight into car shopping experiences. The approach involves a small number of consumers providing relatively lengthy stories about recent car shopping experiences. The goal is trying to discover particular reasons why certain car models are eliminated from consider- ation. The consumer tells a story of comparing a Ford and a GM (General Motors) minivan. She describes the two vehicles in great detail and ultimately concludes, “We might have gone with the Ford instead because it was real close between the Ford and the GM.” The Ford was cheaper, but the way the door opened suggested difficulties in dealing with kids and groceries and the like, and so she purchased the GM model. The researcher in this story goes on to interpret the plotline of the story as having to do with the consumer’s responsibility for poor consumption outcomes. Consider the following passage.

“It has got GM defects and that is really frustrating. I mean the transmission had to be rebuilt after about 150 miles …and it had this horrible vibration problem. We took a long vacation where you couldn’t go over 60 miles an hour because the thing started shaking so bad …. I told everybody, ’Don’t buy one of these things.’ We should have known because our Buick—the

Buick that is in the shop right now—its transmission lasted about 3,000 miles. My husband’s parents are GM people and they had one go bad. I keep thinking, When I am going to learn? I think this one has done it. I don’t think I will ever go back to GM after this.”11

The research concludes that a hermeneutic link exists between the phrase “When I am going to learn?” and the plot of self-responsibility. The resulting behavior including no longer considering GM products and the negative word-of-mouth behavior are ways of restoring esteem given the events.

Source: Journal of Marketing Research by Winer, Russ. Copyright 1997 by American Marketing Association (AMA) (CHIC). Reproduced with permission of American Marketing Association (AMA) (CHIC) in the format Textbook via Copyright Clearance Center; Thompson, Craig J., “Interpreting Consumers: A Hermeneutical Framework for Deriving Marketing Insights from the Tests of Consumers’ Consumption Sto- ries,” Journal of Marketing Research, 34 (November 1997), 438–455 (see pp. 443–444 for quotation).

“When Will I Ever Learn?”

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passage from a respondent’s story that is linked with a key theme from within this story or provided by the researcher.13 These passages are an important way in which data are interpreted.

Computerized software exists to assist in coding and interpreting texts and images. ATLAS.ti is one such software package that adopts the term hermeneutic unit in referring to groups of phrases that are linked with meaning. Hermeneutic units and computerized software are also very appropriate in grounded theory approaches. One useful component of computerized approaches is a word counter. The word counter will return counts of how many times words were used in a story. Often, frequently occurring words suggest a key theme. The Research Snapshot “When Will I Ever Learn?” demonstrates the use of hermeneutics in interpreting a story about a consumer shopping for a car.

Ethnography What Is Ethnography? Ethnography represents ways of studying cultures through methods that involve becoming highly active within that culture. Participant-observation typifies an ethnographic research approach. Participant-observation means the researcher becomes immersed within the culture that he or she is studying and draws data from his or her observations. A culture can be either a broad culture, like American culture, or a narrow culture, like urban gangs, Harley-Davidson owners, or skateboarding enthusiasts.14

Organizational culture would also be relevant for ethnographic study.15 At times, researchers have actually become employees of an organization for an extended period of time. In doing so, they become part of the culture and over time other employees come to act quite naturally around the researcher. The researcher may observe behaviors that the employee would never reveal other- wise. For instance, a researcher investigating the ethical behavior of salespeople may have difficulty getting a car salesperson to reveal any potentially deceptive sales tactics in a traditional interview. However, ethnographic techniques may result in the salesperson letting down his or her guard, resulting in more valid discoveries about the car-selling culture.

Observation In Ethnography Observation plays a key role in ethnography. Researchers today sometimes ask households for permis- sion to place video cameras in their home. In doing so, the ethnographer can study the consumer in a

“natural habitat” and use the observations to test new products, develop new product ideas, and develop strategies in general.16

Ethnographic study can be par- ticularly useful when a certain culture is comprised of individuals who can- not or will not verbalize their thoughts and feelings. For instance, ethnography has advantages for discovering insights among children since it does not rely largely on their answers to questions. Instead, the researcher can simply become part of the environment, allow the children to do what they do natu- rally, and record their behavior.17

The opening vignette describing a participant-observer approach to learn- ing about skateboarding culture repre- sents an ethnographic approach. Here, the researcher would draw insight from observations and personal experiences with the culture.

ethnography

Represents ways of studying cultures through methods that involve becoming highly active

within that culture.

participant-observation

Ethnographic research approach where the researcher

becomes immersed within the culture that he or she is

studying and draws data from his or her observations.

Ethnographic (participant- observation) approaches may be useful to understanding how children obtain value from their experiences with toys.

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‘‘I never predict. I just look out the window and see what is visible—but not yet seen’’.—PETER DRUCkER

138 • PART TWO Beginning Stages of the Research Process

Grounded Theory What Is Grounded Theory? Grounded theory is probably applied less often in business research than is either phenomenology or ethnography.18

Grounded theory represents an inductive investigation in which the researcher poses questions about information provided by respondents or taken from historical records. The researcher asks the questions to him or herself and repeatedly questions the responses to derive deeper explana- tions. Grounded theory is particularly applicable in highly dynamic situations involving rapid and significant change. Two key questions asked by the grounded theory researcher are “What is hap- pening here?” and “How is it different?”19 The distinguishing characteristic of grounded theory is that it does not begin with a theory but instead extracts one from whatever emerges from an area of inquiry.20

How Is Grounded Theory Used? Consider a company that approaches a researcher to study whether or not its sales force is as effec- tive as it was five years ago. The researcher uses grounded theory to discover a potential explana- tion. A theory is inductively developed based on text analysis of dozens of sales meetings that had been recorded over the previous five years. By questioning the events discussed in the sales inter- views and analyzing differences in the situations that may have led to the discussion, the researcher is able to develop a theory. The theory suggests that with an increasing reliance on e-mail and other technological devices for communication, the salespeople do not communicate with each other informally as much as they did five years previously. As a result, the salespeople had failed to bond into a close-knit “community.”21

Computerized software also can be useful in developing grounded theory. In our Vans example, the researcher may interpret skateboarders’ stories of good and bad skating experiences by ques- tioning the events and changes described. These may yield theories about the role that certain brands play in shaping a good or bad experience. Alternatively, grounded theorists often rely on visual representations. Thus, the skateboarder could develop collages rep- resenting good and bad experiences. Just as with the text, questions can be applied to the visuals in an effort to develop theory.

Case Studies What Are Case Studies? Case studies simply refer to the documented history of a par- ticular person, group, organization, or event. Typically, a case study may describe the events of a specific company as it faces an important decision or situation, such as introducing a new product or dealing with some management crisis. Textbook cases typify this kind of case study. Clinical interviews of managers, employees, or customers can represent a case study.

The case studies can then be analyzed for important themes. Themes are identified by the frequency with which the same term (or a synonym) arises in the narrative description. The themes may be useful in discovering variables that are relevant to potential explanations.

How Are Case Studies Used? Case studies are commonly applied in business. For instance, case studies of brands that sell “luxury” products helped provide ©

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Qualitative research reveals that products that are perceived as “authentic” offer more value for consumers.

grounded theory

Represents an inductive investigation in which the researcher poses questions about information provided by respondents or taken from historical records; the researcher asks the questions to him or herself and repeatedly questions the responses to derive deeper explanations.

case studies

The documented history of a particular person, group, organization, or event.

themes

Identified by the frequency with which the same term (or a synonym) arises in the narrative description.

CHAPTER 7 Qualitative Research Tools • 139

140

insight into what makes up a prestigious brand. A business researcher carefully conducted case (no pun intended) studies of higher end wine labels (such as Penfold’s Grange) including the methods of production and distribution. This analysis suggested that a key ingredient to a prestige brand may well be authenticity. When consumers know something is authentic, they attach more esteem to that product or brand.22

Case studies often overlap with one of the other categories of qualitative research. The Research Snapshot “A Sensory Safari Provides Play Time (and Good Research) for Time Warner Cable” illustrates how observation was useful in discovering insights leading to important business changes.

A primary advantage of the case study is that an entire organization or entity can be inves- tigated in depth with meticulous attention to detail. This highly focused attention enables the researcher to carefully study the order of events as they occur or to concentrate on identify- ing the relationships among functions, individuals, or entities. Conducting a case study often requires the cooperation of the party whose history is being studied. This freedom to search for whatever data an investigator deems important makes the success of any case study highly dependent on the alertness, creativity, intelligence, and motivation of the individual performing the case analysis.

Common Techniques Used in Qualitative Research Qualitative researchers apply a nearly endless number of techniques. These techniques overlap more than one of the orientations previously discussed, although each category may display a pref- erence for certain techniques. Exhibit 7.2 lists characteristics of some common qualitative research techniques. Each is then described.

Traditional focus groups are used by businesses for a variety of reasons, most notably for the opportunity to see how potential consumers will react to new products or new services. But new focus group techniques are bringing this experience to another level. Welcome to the world of Spark, a New York-based market research firm that seeks to engage all of the senses of consumers as a way to understand potential products or services. Consumers are put in rooms with all kinds of materi- als, including Styrofoam, toys, fresh flowers, and even coffee beans and asked to build collages that reflect how they feel regarding a new product. They may create any montage they feel, and are encouraged to get as messy as they want. This signature program, the Sensory Safari, gives a new meaning to “play time.”

When Time Warner Cable, a company that provides computer communication and cable television content to resi- dential users, wished to see the impact of a new ad campaign, the company let Spark take some potential users on a Sensory Safari. Prior to the ads, users provided collages that included

such images as knots of rope and roller coasters, with associated negative imagery and discus- sions. Subsequent to the ads, the users provided different collages, including a heart-shaped image with technology in the middle of it.

This nonverbal information proved to be critically important to Time Warner, which realized that it can be hard to “talk about how you feel” concerning technology in a traditional focus group. When people are allowed to fully engage all of their senses, they can “say a lot.” Who knew play time could be so profitable?

Source: Spark-NYC, www.spark-nyc.com; Vega, Tanzina, “Focus Groups That Look Like Play Groups,” The New York Times (May 30, 2011, Page B1).

A Sensory Safari Provides Play Time (and Good Research) for Time Warner Cable

RESEARCH SNAPSHOT

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Tool Description

Type of Approach (Category) Key Advantages Key Disadvantages

Focus Group Interviews

Small group discussions led by a trained moderator

Ethnography, case studies

• Can be done quickly • Gain multiple

perspectives • Flexibility

• Results dependent on moderator

• Results do not generalize to larger population

• Difficult to use for sensitive topics

• Expensive

Depth Interviews One-on-one, probing interview between a trained researcher and a respondent

Ethnography, grounded theory, case studies

• Gain considerable insight from each individual

• Good for understanding unusual behaviors

• Result dependent on researcher’s interpretation

• Results not meant to generalize

• Very expensive

Conversations Unstructured dialogue recorded by a researcher

Phenomenology, grounded theory

• Gain unique insights from enthusiasts

• Can cover sensitive topics

• Less expensive than depth interviews or focus groups

• Easy to get off course

• Interpretations are very researcher- dependent

Semistructured Interviews

Open-ended questions, often in writing, that ask for short essay- type answers from respondents

Grounded theory, ethnography

• Can address more specific issues

• Results can be easily interpreted

• Cost advantages over focus groups and depth interviews

• Lack the flexibility that is likely to produce truly creative or novel explanations

Word Association/ Sentence Completion

Records the first thoughts that come to a consumer in response to some stimulus

Grounded theory, case studies

• Economical • Can be done quickly

• Lack the flexibility that is likely to produce truly creative or novel explanations

Observation Recorded notes describing observed events

Ethnography, grounded theory, case studies

• Can be unobtrusive • Can yield actual

behavior patterns

• Can be very expensive with participant-observer series

Collages Respondent assembles pictures that represent their thoughts/feelings

Phenomenology, grounded theory

• Flexible enough to allow novel insights

• Highly dependent on the researcher’s interpretation of the collage

Thematic Apperception/ Cartoon Tests

Researcher provides an ambiguous picture and respondent tells about the story

Phenomenology, grounded theory

• Projective, allows to get at sensitive issues

• Flexible

• Highly dependent on the researcher’s interpretation

EXHIBIT 7.2

Four Common Qualitative Research Tools

What Is a Focus Group Interview? The focus group interview is so widely used that many advertising and research companies do nothing but focus group interviews. In that sense, it is wrongly synonymous with qualitative research. Nonetheless, focus groups are a very important qualitative research technique and deserve considerable discussion.

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CHAPTER 7 Qualitative Research Tools • 141

A focus group interview is an unstructured, free-flowing interview with a small group of people, usually between six and ten. Focus groups are led by a trained moderator who follows a flexible format encouraging dialogue among respondents. Common focus group topics include employee programs, employee satisfaction, brand meanings, problems with products, advertising themes, or new product concepts.

The group meets at a central location at a designated time. Participants may range from con- sumers talking about hair coloring, petroleum engineers talking about problems in the “oil patch,” children talking about toys, or employees talking about their jobs. A moderator begins by providing some opening statement to broadly steer discussion in the intended direction. Ideally, discussion topics emerge at the group’s initiative, not the moderator’s. Consistent with phenomenological approaches, moderators should avoid direct questioning unless absolutely necessary.

Advantages of Focus Group Interviews Focus groups allow people to discuss their true feelings, anxieties, and frustrations, as well as the depth of their convictions, in their own words. While other approaches may also do much the same, focus groups offer several advantages:

1. Relatively fast 2. Easy to execute 3. Allow respondents to piggyback off each other’s ideas 4. Provide multiple perspectives 5. Flexibility to allow more detailed descriptions 6. High degree of scrutiny

SPEED AND EASE In an emergency situation, three or four group sessions can be conducted, analyzed, and reported in a week or so. The large number of research firms that conduct focus group interviews makes it easy to find someone to host and conduct the research. Practically every state in the United States contains multiple research firms that have their own focus group facilities. Companies with large research departments likely have at least one qualified focus group moderator so that they need not outsource the focus group.

PIGGYBACKING AND MULTIPLE PERSPECTIVES Furthermore, the group approach may produce thoughts that would not be produced other- wise. The interplay between respondents allows them to piggyback off of each other’s ideas. In

other words, one respondent stimu- lates thought among the others and, as this process continues, increasingly creative insights are possible. A com- ment by one individual often triggers a chain of responses from the other par- ticipants. The social nature of the focus group also helps bring out multiple views as each person shares a particular perspective.

FLEXIBILITY The flexibility of focus group inter- views is advantageous, especially when compared with the more structured and rigid survey format. Numerous top- ics can be discussed and many insights can be gained, particularly with regard to the variations in consumer behavior in different situations. Responses that

Focus group facilities typically include a comfortable room for respondents, recording equipment, and a viewing room via a two-way mirror.

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focus group interview

An unstructured, free-flowing interview with a small group of around six to ten people.

Focus groups are led by a trained moderator who follows

a flexible format encouraging dialogue among respondents.

piggyback

A procedure in which one respondent stimulates thought

among the others; as this process continues, increasingly

creative insights are possible.

142 • PART TWO Beginning Stages of the Research Process

would be unlikely to emerge in a survey often come out in group interviews: “If the day is hot and I have to serve the whole neighborhood, I make Kool-Aid; otherwise, I give them Dr Pepper or Coke” or “Usually I work on my projects at home in the evenings, but when it is a team project we set aside time on Monday morning and all meet in the conference room.”

If a researcher is investigating a target group to determine who consumes a particular beverage or why a consumer purchases a certain brand, situational factors must be included in any interpre- tations of respondent comments. For instance, in the preceding situation, the fact that a particular beverage is consumed must be noted. It would be inappropriate to say that Kool-Aid is preferred in general. The proper interpretation is situation specific. On a hot day the whole neighborhood gets Kool-Aid. When the weather isn’t hot, the kids may get nothing, or if only a few kids are around, they may get lucky and get Dr Pepper. Thus, Kool-Aid can be interpreted as appropriate for satisfying large numbers of hot kids while Dr Pepper is a treat for a select few. Similarly, indi- vidual assignments are worked on at home in the evenings, while team projects are worked on in the morning in the conference room.

SCRUTINY A focus group interview allows closer scrutiny in several ways. First, the session can be observed by several people, as it is usually conducted in a room containing a two-way mirror. The respon- dents and moderator are on one side, and an invited audience that may include both researchers and decision makers is on the other. If the decision makers are located in another city or country, the session may be shown via a live video hookup. Either through live video or a two-way mirror, some check on the eventual interpretations is provided through the ability to actually watch the research being conducted. If the observers have questions that are not being asked or want the moderator to probe on an issue, they can send a quick text message with instructions to the mod- erator. Second, focus group sessions are generally recorded on audio or videotape. Later, detailed examination of the recorded session can offer additional insight and help clear up disagreements about what happened.

Focus Group Illustration Focus groups often are used for concept screening and concept refinement. The concept may be continually modified, refined, and retested until management believes it is acceptable. While RJR’s initial attempts at smokeless cigarettes failed in the United States, Philip Morris is developing a smokeless cigarette for the U.K. market. Focus groups are being used to help understand how the product will be received and how it might be improved.23 The voluntary focus group respondents are presented with samples of the product and then they discuss it among themselves. The inter- view results suggest that the key product features that must be conveyed are the fact that it pro- duces no ashes, no side smoke, and very little odor. These beliefs are expected to lead to a positive attitude. Focus group respondents show little concern about how the cigarette actually functioned. Smokers believe they will use the product if nonsmokers are not irritated by being near someone using the “electronic cigarette.” Thus, the focus groups are useful in refining the product and devel- oping a theory of how it should be marketed.

Group Composition The ideal size of the focus group is six to ten people. If the group is too small, one or two members may intimidate the others. Groups that are too large may not allow for adequate participation by each group member.

Homogeneous groups seem to work best because they allow researchers to concentrate on consumers with similar lifestyles, experiences, and communication skills. The session does not become rife with too many arguments and different viewpoints stemming from diverse back- grounds. Also, from an ethnographic perspective, the respondents should all be members of a unique and identifiable culture. Vans may benefit from a focus group interview comprised only of skateboard enthusiasts. Perhaps participants can be recruited from a local skate park. However, additional group(s) of participants that are not boarders might be useful in gaining a different perspective.

CHAPTER 7 Qualitative Research Tools • 143

When the Centers for Disease Con- trol and Prevention tested public service announcements about AIDS through focus groups, it discovered that single- race groups and racially diverse groups reacted differently. By conducting sepa- rate focus groups, the organization was able to gain important insights about which creative strategies were most appropriate for targeted versus broad audiences.

For example, for focus groups regarding employee satisfaction, we might want to recruit homogeneous groups based on position in the orga- nization. The researcher may find that entry-level employees have very dif- ferent perspectives and concerns than those of middle or upper-level manage- ment. Also, it is fully understandable that

employees might be hesitant to criticize their supervisors. Therefore, researchers may consider interviewing different levels of employees in separate groups.

Finding quality focus group participants can also be a challenge and can lead to issues as well. For example, a research client observed a focus group interview being conducted by a research supplier that had previously performed several other projects for the client, each dealing with a quite unique topic. During the interview, the client noticed that some focus group respondents looked familiar.

A few days later, the client reviewed video recordings of the session alongside videotapes from two previous focus groups outsourced to the same company. She found that eight of the ten respondents in the latest focus group had appeared in one of the previous interviews as well. She was furious and considered whether or not she should pay for the interview or bother having a report prepared.

The focus group researcher had taken this approach to make sure the session went smoothly. The moderator solicited subjects who in the past had been found to be very articulate and talk- ative. In this case, the focus group respondents are more or less “professional,” paid participants. It is questionable whether such “professional respondents” can possibly offer relevant opinions on all these topics. The question is, has the research firm acted in an ethical manner?

Researchers who wish to collect information from different types of people should conduct several focus groups. A diverse overall sample may be obtained by using different groups even though each group is homogeneous. For instance, in discussing household chores, four groups might be used:

■ Married men ■ Married women ■ Single men ■ Single women

Although each group is homogeneous, by using four groups, researchers obtain opinions from a wide degree of respondents.

Environmental Conditions A focus group session may typically take place at the research agency in a room specifically designed for this purpose. Research suppliers that specialize in conducting focus groups operate from com- mercial facilities that have videotape cameras in observation rooms behind two-way mirrors and microphone systems connected to tape recorders and speakers to allow greater scrutiny as discussed

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Imagine the differences in reactions to legislation further restricting smoking behavior that would be found among a group of smokers compared to a group of nonsmokers.

144 • PART TWO Beginning Stages of the Research Process

previously. Refreshments are provided to help create a more relaxed atmosphere conducive to a free exchange of ideas. More open and intimate reports of personal experiences and sentiments can be obtained under these conditions.

The Focus Group Moderator The moderator essentially runs the focus group and plays a critical role in its success. There are several qualities that a good moderator must possess:

1. The moderator must be able to develop rapport with the group to promote interaction among all participants. The moderator should be someone who is really interested in people, who listens carefully to what others have to say, and who can readily establish rapport, gain people’s confidence, and make them feel relaxed and eager to talk.

2. The moderator must be a good listener. Careful listening is especially important because the group interview’s purpose is to stimulate spontaneous responses. Without good listening skills, the moderator may direct the group in an unproductive direction.

3. The moderator must try not to interject his or her own opinions. Good moderators usually say less rather than more. They can stimulate productive discussion with generalized follow-ups such as, “Tell us more about that incident,” or “How are your experiences similar or different from the one you just heard?” The moderator must be particularly careful not to ask leading questions such as “You are happy to work at Acme, aren’t you?”

4. The moderator must be able to control discussion without being overbearing. The modera- tor’s role is also to focus the discussion on the areas of concern. When a topic is no longer generating fresh ideas, the effective moderator changes the flow of discussion. The moderator does not give the group total control of the discussion, but he or she normally has prepared questions on topics that concern management. However, the timing of these questions in the discussion and the manner in which they are raised are left to the moderator’s discretion. The term focus group thus stems from the moderator’s task. He or she starts out by asking for a general discussion but usually focuses in on specific topics during the session.

Planning the Focus Group Outline Focus group researchers use a discussion guide to help control the interview and guide the dis- cussion into product areas. A discussion guide includes written introductory comments inform- ing the group about the focus group purpose and rules and then outlines topics or questions to be addressed in the group session. Thus, the discussion guide serves as the focus group outline. Some discussion guides will have only a few phrases in the entire document. Others may be more detailed. The amount of content depends on the nature and experience of the researcher and the complexity of the topic.

A cancer center that wanted to warn the public about the effects of the sun used the discus- sion guide in Exhibit 7.3. The business researchers had several objectives for this discussion guide:

■ The first question was very general, asking that respondents describe their feelings about being out in the sun. This opening question aimed to elicit the full range of views within the group. Some individuals might view being out in the sun as a healthful practice, whereas others view the sun as deadly. The hope is that by exposing the full range of opinions, respondents would be motivated to fully explain their own position. This was the only question asked specifically of every respondent. Each respondent had to give an answer before free discussion began. In this way, individuals experience a nonthreatening environment encouraging their free and full opinion. A general question seeking a reaction serves as an effective icebreaker.

■ The second question asks whether participants could think of any reason they should be warned about sunlight exposure. This question was simply designed to introduce the idea of a warning label.

■ Subsequent questions were asked and became increasingly specific. They were first asked about possible warning formats that might be effective. Respondents are allowed to react to any formats suggested by any other respondent. After this discussion, the moderator will introduce some specific formats the cancer center personnel have in mind.

moderator

A person who leads a focus group interview and ensures that everyone gets a chance to speak and contribute to the discussion.

discussion guide

A focus group outline that includes written introductory comments informing the group about the focus group purpose and rules and then outlines topics or questions to be addressed in the group session.

CHAPTER 7 Qualitative Research Tools • 145

■ Finally, the “bottom-line” question is asked: “What format would be most likely to induce people to take protective measures?” There would be probing follow-ups of each opinion so that a respondent couldn’t simply say something like, “The second one.” All focus groups fin- ish up with a catchall question asking for any comments including any thoughts they wanted passed along to the sponsor (which in this case was only then revealed as the Houston-based cancer center).

Researchers who planned the outline established certain objectives for each part of the focus group. The initial effort was to break the ice and establish rapport within the group. The logical flow of the group session then moved from general discussion about sunbathing to more focused discussion of types of warnings about danger from sun exposure.

In general, the following steps should be used to conduct an effective focus group discussion guide:

1. Welcome and introductions should take place first. 2. Begin the interview with a broad icebreaker that does not reveal too many specifics about the

interview. Sometimes, this may even involve respondents providing some written story or their reaction to some stimulus like a photograph, film, product, or advertisement.

EXHIBIT 7.3

Discussion Guide for a Focus Group Interview

Thank you very much for agreeing to help out with this research. We call this a focus group; let me explain how it works, and then please let me know if something isn’t clear.

This is a discussion, as though you were sitting around just talking. You can disagree with each other, or just comment. We do ask that just one person talk at a time, because we tape-record the session to save me from having to take notes. Nothing you say will be associated with you or your church— this is just an easy way for us to get some people together.

The subject is health risk warnings. Some of you may remember seeing a chart in a newspaper that gives a pollen count or a pollution count. And you’ve heard on the radio sometimes a hurricane watch or warning. You’ve seen warnings on cigarette packages or cigarette advertising, even if you don’t smoke. And today we’re going to talk about warnings about the sun. Before we start, does anybody have a question?

1. OK, let’s go around and talk about how often you spend time in the sun, and what you’re likely to be doing. (FOR PARENTS): What about your kids—do you like them to be out in the sun?

2. OK, can you think of any reason that somebody would give you a warning about exposure to the sun?

(PROBE: IS ANY SUN EXPOSURE BAD, OR ONLY A CERTAIN DEGREE OF EXPOSURE, AND IF SO, WHAT IS IT? OR IS THE SUN GOOD FOR YOU?)

3. What if we had a way to measure the rays of the sun that are associated with skin problems, so that you could find out which times of the day or which days are especially dangerous? How could, say, a radio station tell you that information in a way that would be useful?

4. Now let me ask you about specific ways to measure danger. Suppose somebody said, “We monitored the sun’s rays at noon, and a typical fair-skinned person with unprotected skin will burn after 40 minutes of direct exposure.” What would you think?

5. Now let me ask you about another way to say the same kind of thing. Suppose somebody said, “The sun’s rays at noon today measured 10 times the 8:00 A.M. baseline level of danger.” What would you think?

6. OK, now suppose that you heard the same degree of danger expressed this way: “The sun’s rays at noon today measured ‘8’ on a sun danger scale that ranges from one to ten.” What would you think?

7. What if the danger scale wasn’t in numbers, but words? Suppose you heard, “The sun’s rays at noon showed a moderate danger reading,” or “The sun’s rays showed a high danger reading.” What would you think?

8. And here’s another possibility: What if you heard “Here’s the sun danger reading at noon today—the unprotected skin of a typical fair-skinned person will age the equivalent of one hour in a ten-minute period.”

9. OK, what if somebody said today is a day to wear long sleeves and a hat, or today is a day you need sunscreen and long sleeves? What would you think?

10. OK, here’s my last question. There are really three things you can do about sun danger: You can spend less time in the sun, you can go out at less dangerous times of day, like before 10:00 in the morning or after 4:00 in the afternoon, and you can cover your skin by wearing a hat or long sleeves, or using protective sunscreen lotion. Thinking about yourself listening to the radio, what kind of announcement would make you likely to do one or more of those things? (PARENTS: WHAT WOULD MAKE YOU BE SURE THAT YOUR CHILD WAS PROTECTED?)

11. And what would you be most likely to do to protect yourself? (YOUR CHILD?)

12. Before we break up, is there anything else you think would be useful for M. D. Anderson’s people to know? Do you have any questions about any aspect of this interview?

OK, thank you very much for your help.

Source: Gelb, Betsy D. and Michael P. Eriksen, “Market Research May Help Prevent Cancer,” Marketing Research (September 1991), 46. Published by American Marketing Association. Reprinted with permission.

146 • PART TWO Beginning Stages of the Research Process

3. Questions become increasingly more specific as the interview proceeds. However, the mod- erator will notice that a good interview will cover the specific question topics before they have to be asked. This is preferable as respondents are clearly not forced to react to the specific issue; it just emerges naturally.

4. If there is a very specific objective to be accomplished, such as explaining why a respondent would either buy or not buy a product, that question should probably be saved for last.

5. A debriefing statement should provide respondents with the actual focus group objectives and answer any questions they may have. This is also a final shot to gain some insight from the group.

Focus Group As Diagnostic Tools Focus groups are perhaps the predominant means by which business researchers implement exploratory research designs. Focus groups also can be helpful in later stages of a research project, particularly when the findings from surveys or other quantitative techniques raise more questions than they answer. Managers who are puzzled about the meaning of survey research results may use focus groups to better understand what survey results indicate. In such a situation, the focus group supplies diagnostic help after quantitative research has been conducted.

Focus groups are also excellent diagnostic tools for spotting problems with ideas. For instance, idea screening is often done with focus groups. An initial concept is presented to the group and then they are allowed to comment on it in detail. This usually leads to lengthy lists of potential product problems and some ideas for overcoming them. Mature products can also be “focus- grouped” in this manner.

Videoconferencing and Focus Groups With the widespread utilization of videoconferencing, the number of companies using these systems to conduct focus groups has increased. With videoconference focus groups, manag- ers can stay home and watch on television rather than having to take a trip to a focus group facility.

FocusVision (http://www.focusvision.com/) is a business research company that provides vid- eoconferencing equipment and services. The FocusVision system is modular, allowing for easy movement and an ability to capture each group member close up. The system operates via a remote keypad that allows observers in a far-off location to pan the focus group room or zoom in on a particular participant. Managers viewing at remote locations can send the moderator messages during the interview.

Interactive Media and Online Focus Groups Internet applications of qualitative exploratory research are growing rapidly and involve both for- mal and informal applications. Formally, the term online focus group refers to a qualitative research effort in which a group of individuals provides unstructured comments by entering their remarks into an electronic Internet display board of some type, such as a chat-room session or in the form of a blog. Because respondents enter their comments into the computer, transcripts of verbatim responses are available immediately after the group session. Online groups can be quick and cost- efficient. However, because there is less personal interaction between participants, group synergy and snowballing of ideas may be diminished.

Several companies have established a form of informal, “continuous” focus group by establish- ing an Internet blog for that purpose.24 We might call this technique a focus blog when the inten- tion is to mine the site for business research purposes. General Motors, American Express, and Lego all have used ideas harvested from their focus blogs. When operating, the Lego blog can be found at http://legoisfun.blogspot.com. While traditional focus group respondents are generally paid $100 or more to show up and participate for 90 minutes, bloggers and online focus group respondents often participate for absolutely no fee at all! Thus, technology provides some cost advantages over traditional focus group approaches.25

online focus group

A qualitative research effort in which a group of individuals provides unstructured comments by entering their remarks into an electronic Internet display board of some type.

focus blog

A type of informal, “continuous” focus group established as an Internet blog for the purpose of collecting qualitative data from participant comments.

CHAPTER 7 Qualitative Research Tools • 147

Online Versus Face-to-Face Focus Group Techniques A research company can facilitate a formal online focus group by setting up a private chat room for that purpose. Participants in formal and informal online focus groups feel that their anonym- ity is very secure. Often respondents will say things in this environment that they would never say otherwise. For example, a lingerie company was able to get insights into how it could design sexy products for larger women. Online, these women freely discussed what it would take “to feel bet- ter about being naked.”26 One can hardly imagine how difficult such a discussion might be face to face. Increased anonymity can be a major advantage for a company investigating sensitive or embarrassing issues.

Because participants do not have to be together in the same room at a research facility, the number of participants in online focus groups can be larger than in traditional focus groups. Twenty-five participants or more is not uncommon for the simultaneous chat-room format. Participants can be at widely separated locations since the Internet does not have geographi- cal restrictions. Of course, a major disadvantage is that often the researcher does not exercise as much control in precisely who participates. In other words, a person could very easily not match the desired profile or even answer screening questions in a misleading way simply to participate.

A major drawback with online focus groups is that moderators cannot see body language and facial expressions (bewilderment, excitement, boredom, interest, and so forth). Thus, they cannot fully interpret how people are reacting. Also, moderators’ ability to probe and ask additional ques- tions on the spot is reduced in online focus groups. Research that requires focus group members to actually touch something (such as a new easy-opening packaging design) or taste something is not generally suitable for an online format.

Disadvantages of Focus Groups Focus groups offer many advantages as a form of qualitative research. Like practically every other research technique, the focus group has some limitations and disadvantages as well. Problems with focus groups include those discussed below.

First, focus groups require objective, sensitive, and effective moderators. It is very difficult for a moderator to remain completely objective about most topics. In large research firms, the moderator may be provided only enough information to effectively conduct the interview, no more. The focus group interview obviously shouldn’t reduce to, or even be influenced by, the moderator’s opinion. Also, without a good moderator, one or two participants may dominate a session, yielding results that are really the opinion of one or two people, not the group. The moderator has to try very hard to make sure that all respondents feel comfortable giving their opinions and even a timid respondent’s opinion is given due consideration. While many people, even some with little or no background to do so, conduct focus groups, good moderators become effective through a combination of naturally good people skills, training (in qualitative research), and experience.

Second, some unique sampling problems arise with focus groups. Researchers often select focus group participants because they have similar backgrounds and experiences or because screen- ing indicates that the participants are more articulate or gregarious than the typical consumer. Such participants may not be representative of the entire target market. Thus, focus group results are not intended to be representative of a larger population.

Third, although not so much an issue with online formats where respondents can remain anonymous, traditional face-to-face focus groups may not be useful for discussing sensitive topics. A focus group is a social setting and usually involves people with little to no familiarity with each other. Therefore, issues that people normally do not like to discuss in public may also prove difficult to discuss in a focus group.

Fourth, focus groups do cost a considerable amount of money, particularly when they are not conducted by someone employed by the company desiring the focus group. As research projects go, there are many more expensive approaches, including a full-blown mail survey using a national

‘‘Necessity, mother of invention.’’—WILLIAM WYCHERLEY

148 • PART TWO Beginning Stages of the Research Process

random sample. This may cost thousands of dollars to conduct and thousands of dollars to analyze and disseminate. Focus group prices vary regionally, but the following figures provide a rough guideline:

Renting facilities and equipment $500 Recruiting of respondents ($75 person) $750 Paying respondents ($100/person) $1,000 Researcher costs

■ Preparation $750 ■ Moderating $1,000 ■ Analysis and report preparation $1,500

Miscellaneous expenses $250

Thus, a client can expect a professional focus group to cost over $5,000 in most situations. Fur- ther, most business topics will call for multiple focus groups. There is some cost advantage in this, as some costs will not change proportionately just because there are multiple interviews. Preparation costs may be the same for one or more interviews; the analysis and report preparation will likely only increase slightly because two or three interviews are included instead of one.

Depth Interviews An alternative to a focus group is a depth interview. A depth interview is a one-on-one interview between a professional researcher and a research respondent. Depth interviews are much the same as a psychological, clinical interview, but with a different purpose. The researcher asks many ques- tions and follows up each answer with probes for additional elaboration. An excerpt from a depth interview is given in Exhibit 7.4.

Like focus group moderators, the interviewer’s role is critical in a depth interview. He or she must be a highly skilled individual who can encourage the respondent to talk freely without influ- encing the direction of the conversation. Probing questions are critical.

Laddering is a term used for a particular approach to probing, asking respondents to compare differences between brands at different levels. What usually results is that the first distinctions are

EXHIBIT 7.4

Excerpt from a Depth Interview

An interviewer (I) talks with Marsha (M) about furniture purchases. Marsha indirectly indicates she delegates the buying responsibility to a trusted antique dealer. She has already said that she and her husband would write the dealer telling him the piece they wanted (e.g., bureau, table). The dealer would then locate a piece that he considered appropriate and would ship it to Marsha from his shop in another state.

M: … We never actually shopped for furniture since we state what we want and (the antique dealer) picks it out and sends it to us. So we never have to go looking through stores and shops and things.

I: You depend on his (the antique dealer’s) judgment? M: Uh, huh. And, uh, he happens to have the sort of taste

that we like and he knows what our taste is and always finds something that we’re happy with.

I: You’d rather do that than do the shopping? M: Oh, much rather, because it saves so much time and it

would be so confusing for me to go through stores and stores looking for things, looking for furniture. This is so easy that I just am very fortunate.

I: Do you feel that he’s a better judge than … M: Much better.

I: Than you are? M: Yes, and that way I feel confident that what I have is very,

very nice because he picked it out and I would be doubtful if I picked it out. I have confidence in him, (the antique dealer) knows everything about antiques, I think. If he tells me something, why I know it’s true—no matter what I think. I know he is the one that’s right.

This excerpt is most revealing of the way in which Marsha could increase her feeling of confidence by relying on the judgment of another person, particularly a person she trusted. Marsha tells us quite plainly that she would be doubtful (i.e., uncertain) about her own judgment, but she “knows” (i.e., is certain) that the antique dealer is a good judge, “no matter what I think.” The dealer once sent a chair that, on first inspection, did not appeal to Marsha. She decided, however, that she must be wrong, and the dealer right, and grew to like the chair very much.

depth interview

A one-on-one interview between a professional researcher and a research respondent conducted about some relevant business or social topic.

laddering

A particular approach to probing, asking respondents to compare differences between brands at different levels that produces distinctions at the attribute level, the benefit level, and the value or motivation level.

CHAPTER 7 Qualitative Research Tools • 149

attribute-level distinctions, the second are benefit-level distinctions, and the third are at the value or motivation level. Laddering can then distinguish two brands of skateboarding shoes based on a) the materials they are made of, b) the comfort they provide, and c) the excitement they create.

Each depth interview may last more than an hour. Thus, it is a time-consuming process if multiple interviews are conducted. Not only does the interview have to be conducted, but each interview produces about the same amount of text as does a focus group interview. This has to be analyzed and interpreted by the researcher. A third major issue stems from the necessity of recording both surface reactions and subconscious motivations of the respondent. Analysis and interpretation of such data are highly subjective, and it is difficult to settle on a true interpretation.

Depth interviews provide more insight into a particular individual than do focus groups. In addition, since the setting isn’t really social, respondents are more likely to discuss sensitive topics than are those in a focus group. Depth interviews are particularly advantageous when some unique or unusual behavior is being studied. For instance, depth interviews have been usefully applied to reveal characteristics of adolescent behavior, ranging from the ways they get what they want from their parents to shopping, smoking, and shoplifting.27

Depth interviews are similar to focus groups in many ways. The costs are similar if only a few interviews are conducted. However, if a dozen or more interviews are included in a report, the costs are higher than focus group interviews due to the increased interviewing and analysis time.

Conversations Holding conversations in qualitative research is an informal data-gathering approach in which the researcher engages a respondent in a discussion of the relevant subject matter. This approach is almost completely unstructured and the researcher enters the conversation with few expectations. The goal is to have the respondent produce a dialogue about his or her lived experiences. Meaning will be extracted from the resulting dialogue.

A conversational approach to qualitative research is particularly appropriate in phenomeno- logical research and for developing grounded theory. In our Vans experience, the researcher may simply tape-record a conversation about becoming a “skater.” The resulting dialogue can then be analyzed for themes and plots. The result may be some interesting and novel insight into the con- sumption patterns of skaters, for example, if the respondent said,

“I knew I was a real skater when I just had to have Vans, not just for boarding, but for wearing.”

This theme may connect to a rite-of-passage plot and show how Vans play a role in this process. Technology is also influencing conversational research. Online communications such as the

reviews posted about book purchases at http://www.barnesandnoble.com can be treated as a con- versation. Companies may discover product problems and ideas for overcoming them by analyzing these computer-based consumer dialogues.28

A conversational approach is advantageous because each interview is usually inexpensive to conduct. Respondents often need not be paid. They are relatively effective at getting at sensitive issues once the researcher establishes a rapport with them. Conversational approaches, however, are prone to produce little relevant information since little effort is made to steer the conversation. Additionally, the data analysis is very much researcher-dependent.

Semistructured Interviews Semistructured interviews usually come in written form and ask respondents for short essay responses to specific open-ended questions. Respondents are free to write as much or as little as they want. The questions would be divided into sections, typically, and within each section, the opening question would be followed by some probing questions. When these are performed face to face, there is room for less structured follow-ups.

The advantages to this approach include an ability to address more specific issues. Responses are usually easier to interpret than other qualitative approaches. Since the researcher can simply prepare the questions in writing ahead of time, and if in writing, the questions are administered without the presence of an interviewer, semistructured interviews can be relatively cost-effective.

conversations

An informal qualitative data- gathering approach in which

the researcher engages a respondent in a discussion of

the relevant subject matter.

150 • PART TWO Beginning Stages of the Research Process

Some researchers interested in studying car salesperson stereotypes used qualitative semistruc- tured interviews to map consumers’ cognitions (memory). The semistructured interview began with a free-association task:

List the first five things that come into your mind when you think of a “car salesman.”

This was followed up with a probing question:

Describe the way a typical “car salesman” looks.

This was followed with questions about how the car salesperson acts and how the respondent feels in the presence of a car salesperson. The results led to research showing how the information that consumers process differs in the presence of a typical car salesperson, as opposed to a less typi- cal car salesperson.29

Social Networking Social networking is one of the most impactful trends in recent times. For many consumers, par- ticularly younger generations, social networking sites like Facebook and MySpace have become the primary tool for communicating with friends both far and near and known and unknown. Social networking has replaced large volumes of e-mail and, many would say, face-to-face com- munications as well. While the impact that social networking will eventually have on society is an interesting question, what is most relevant to marketing research is the large portion of this infor- mation that discusses marketing and consumer related information.

Companies can assign research assistants to monitor these sites for information related to their particular brands. The information can be coded as either positive or negative. When too much negative information is being spread, the company can try to react to change the opinions. In addition, many companies like P&G and Ford maintain their own social networking sites for the purpose of gathering research data. In a way, these social networking sites are a way that companies can eavesdrop on consumer conversations and discover key information about their products. The textual data that consumers willingly put up becomes like a conversation. When researchers get the opportunity to react with consumers or employees through a social network site, they can function much like an online focus group or interview.

Free-Association/Sentence Completion Method Free-association techniques simply record a respondent’s first cognitive reactions (top-of-mind) to some stimulus. The Rorschach or inkblot test typifies the free-association method. Respondents view an ambiguous figure and are asked to say the first thing that comes to their mind. Free- association techniques allow researchers to map a respondent’s thoughts or memory.

The sentence completion method is based on free-association principles. Respondents simply are required to complete a few partial sentences with the first word or phrase that comes to mind. For example:

People who drink beer are ________________________________________________. A man who drinks a dark beer is ___________________________________________. Imported beer is most liked by ______________________________________________. The woman drinking beer in the commercial ____________________________________.

Answers to sentence-completion questions tend to be more extensive than responses to word- association tests. Although the responses lack the ability to probe for meaning as in other qualitative techniques, they are very effective in finding out what is on a respondent’s mind. They can also do so in a quick and very cost-effective manner. Free-association and sentence-completion tasks are sometimes used in conjunction with other approaches. For instance, they can sometimes be used as effective icebreakers in focus group interviews.

free-association techniques

Record respondents’ first (top- of-mind) cognitive reactions to some stimulus.

CHAPTER 7 Qualitative Research Tools • 151

Observation Observation can be a very important qualitative tool. The participant-observer approach typifies how observation can be used to explore various issues. Meaning is extracted from field notes. Field notes are the researchers’ descriptions of what actually happens in the field. These notes then become the text from which meaning is extracted.

Observation may also take place in visual form. Researchers may observe employees in their workplace, consumers in their home, or try to gain knowledge from photographic records of one type or another. Observation can either be very inexpensive, such as when a research associate sits and simply observes behavior, or it can be very expensive, as in most participant-observer studies. Observational research is keenly advantageous for gaining insight into things that respondents can- not or will not verbalize. Observation research is a common method of data collection and is the focus of Chapter 11.

Collages As seen in the Research Snapshot on Spark-NYC, business researchers sometimes have respon- dents prepare a collage to represent their experiences. The collages are then analyzed for meaning much in the same manner as text dialogues are analyzed. Computer software can even be applied to help develop potential grounded theories from the visual representations.

Harley-Davidson commissioned research in which collages depicting feelings about Harley- Davidson were compared based on whether the respondent was a Harley owner or an owner of a competitor’s brand. The collages of “Hog” owners revealed themes of artwork and the freedom of the great outdoors. These themes did not emerge in the non-Hog groups. This led to confirmatory research that helped Harley continue its growth, appealing more specifically to its diverse market segments.30

Like sentence completion and word association, collages are often used within some other approach, such as a focus group or a depth interview. Collages offer the advantage of flexibility but are also very much subject to the researcher’s interpretations.

Projective Research Techniques A projective technique is an indirect means of questioning enabling respondents to project beliefs and feelings onto a third party, an inanimate object, or a task situation. Projective techniques usually encourage respondents to describe a situation in their own words with little prompting by the inter- viewer. Individuals are expected to interpret the situation within the context of their own experiences, attitudes, and personalities and to express opinions and emotions that may be hidden from others and possibly themselves. Projective techniques are particularly useful in studying sensitive issues.

There is an old story about asking a man why he purchased a Mercedes-Benz. When asked directly why he purchased a Mercedes, he responds that the car holds its value and does not depre- ciate much, that it gets better gas mileage than you’d expect, or that it has a comfortable ride. If you ask the same person why a neighbor purchased a Mercedes, he may well answer, “Oh, that status seeker!” This story illustrates that individuals may be more likely to give true answers (consciously or unconsciously) to disguised questions, and a projective technique provides a way of disguising just who is being described.

Thematic Apperception Test (TAT) A thematic apperception test (TAT), sometimes called the picture interpretation technique, pres- ents subjects with an ambiguous picture(s) and asks the subject to tell what is happening in the picture(s) now and what might happen next. Hence, themes (thematic)are elicited on the basis of the perceptual-interpretive (apperception) use of the pictures. The researcher then analyzes the contents of the stories that the subjects relate. A TAT represents a projective research technique.

Frequently, the TAT consists of a series of pictures with some continuity so that stories may be constructed in a variety of settings. The first picture might portray a person working at their desk; in the second picture, a person that could be a supervisor is talking to the worker; the final picture

field notes

The researcher’s descriptions of what actually happens in the

field; these notes then become the text from which meaning is

extracted.

projective technique

An indirect means of questioning enabling

respondents to project beliefs and feelings onto a third party,

an inanimate object, or a task situation.

thematic apperception test (TAT)

A test that presents subjects with an ambiguous picture(s) in which consumers and products are the center of attention; the investigator asks the subject to

tell what is happening in the picture(s) now and what might

happen next.

152 • PART TWO Beginning Stages of the Research Process

might show the original employee and another having a discussion at the water cooler. A Vans TAT might include several ambiguous pictures of a skateboarder and then show him or her heading to the store. This might reveal ideas about the brands and products that fit the role of skateboarder.

The picture or cartoon stimulus must be sufficiently interesting to encourage discussion but ambiguous enough not to disclose the nature of the research project. Clues should not be given to the character’s positive or negative predisposition. A pretest of a TAT investigating why men might purchase chainsaws used a picture of a man looking at a very large tree. The research respondents were homeowners and weekend woodcutters. They almost unanimously said that they would get professional help from a tree surgeon to deal with this situation. Thus, early in pretesting, the researchers found out that the picture was not sufficiently ambiguous. The tree was too large and did not allow respondents to identify with the tree-cutting task. If subjects are to project their own views into the situation, the environmental setting should be a well-defined, familiar problem, but the solution should be ambiguous.

An example of a TAT using a cartoon drawing in which the respondent suggests a dialogue in which the characters might engage is provided in Exhibit 7.5. This TAT is a purposely ambigu- ous illustration of an everyday occurrence. The two office workers are shown in a situation and the respondent is asked what the woman might be talking about. This setting could be used for discus- sions about the organization’s management, store personnel, particular software products, and so on.

Exploratory Research in Science and in Practice Misuses of Exploratory and Qualitative Research Any research tool can be misapplied. Exploratory research cannot take the place of conclusive, confirmatory research. Thus, since many qualitative tools are best applied in exploratory design, they are likewise limited in the ability to draw conclusive inferences—test hypotheses. One of the biggest drawbacks is the subjectivity that comes along with “interpretation.” In fact, sometimes

EXHIBIT 7.5

An Example of a TAT Picture Do you think

we need to upgrade our word processing

software?

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20 13

CHAPTER 7 Qualitative Research Tools • 153

the term interpretive research is used synonymously with qualitative research. When only one researcher interprets the meaning of what a single person said in a depth interview or similar technique, one should be very cautious before major business decisions are made based on these results. Is the result replicable? Replication means that the same results and conclusions will be drawn if the study is repeated by different researchers with different respondents following the same methods. In other words, would the same conclusion be reached based on another research- er’s interpretation?

Indeed, some qualitative research methodologies were generally frowned upon for years based on a few early and public misapplications during what became known as the “motivational research” era. While many of the ideas produced during this time had some merit, as can sometimes be the case, too few researchers did too much interpretation of too few respondents. Compound- ing this, managers were quick to act on the results, believing that the results peaked inside one’s subliminal consciousness and therefore held some type of extra power. Thus, often the research was flawed based on poor interpretation, and the decision process was flawed because the deciders acted prematurely. As examples, projective techniques and depth interviews were frequently used in the late 1950s and early 1960s, producing some interesting and occasionally bizarre reasons for consumers’ purchasing behavior:

■ A woman is very serious when she bakes a cake because unconsciously she is going through the symbolic act of giving birth.

■ A man buys a convertible as a substitute mistress and a safer (and potentially cheaper) way of committing adultery.

■ Men who wear suspenders are reacting to an unresolved castration complex.31

About two decades later, researchers for the McCann-Erickson advertising agency interviewed low-income women using a form of TAT involving story completion regarding attitudes toward insecticides. Themes noted included:

■ The joy of victory over roaches (watching them die or seeing them dead) ■ Using the roach as a metaphor through which women can take out their hostility toward men

(women generally referred to roaches as “he” instead of “she” in their stories).32

Certainly, some useful findings resulted. Even today, we have the Pillsbury Doughboy as evidence that useful ideas were produced. In many of these cases, interpretations were either misleading or too ambitious (taken too far). However, many companies became frustrated when decisions based upon motivational research approaches proved poor. Thus, researchers moved away from qualitative tools during the late 1960s and 1970s. Today, however, qualitative tools have won acceptance once again as researchers realize they have greater power in discovering insights that would be difficult to capture in typical survey research (which is limited as an exploratory tool).

Scientific Decision Processes Objectivity and replicability are two characteristics of scientific inquiry. Are focus groups objective and replicable? Would three different researchers all interpret focus group data identically? How should a facial expression or nod of the head be interpreted? Have subjects fully grasped the idea or concept behind a nonexistent product? Have respondents overstated their satisfaction because they think their supervisor will read the report and recognize them from their comments? Many of these questions are reduced to a matter of opinion that may vary from researcher to researcher and from one respondent group to another. Therefore, a focus group, or a depth interview, or TAT alone does not best represent a complete scientific inquiry.

However, if the thoughts discovered through these techniques survive preliminary evaluations and are developed into research hypotheses, they can be further tested. These tests may involve survey research or an experiment testing an idea very specifically (for example, if a certain advertis- ing slogan is more effective than another). Thus, exploratory research approaches using qualitative research tools are very much a part of scientific inquiry. However, before making a scientific deci- sion, a research project should include a confirmatory study using objective tools and an adequate sample in terms of both size and how well it represents a population.

replication

The same interpretation will be drawn if the study is repeated

by different researchers with different respondents following

the same methods.

154 • PART TWO Beginning Stages of the Research Process

But is a scientific decision approach always used or needed? In practice, many business deci- sions are based solely on the results of focus group interviews or some other exploratory result. The primary reasons for this are (1) time, (2) money, and (3) emotion.

Time Sometimes, researchers simply are not given enough time to follow up on exploratory research results. Companies feel an increasingly urgent need to get new products to the market faster. Thus, a seemingly good idea generated in a focus group (like Clear, Vanilla, or Cherry Dr Pepper) is simply not tested with a more conclusive study. The risk of delaying a decision may be seen as greater than the risk of proceeding without completing the scientific process. Thus, although the researcher may warn against it, there may be logical reasons for such action. The decision mak- ers should be aware, though, that the conclusions drawn from exploratory research designs are just that—exploratory. Thus, there is less likelihood of good results from the decision than if the research process had involved further testing.

Money Similarly, researchers sometimes do not follow up on exploratory research results because they believe the cost is too high. Realize that tens of thousands of dollars may have already been spent on qualitative research. Managers who are unfamiliar with research will be very tempted to won- der, “Why do I need yet another study?” and “What did I spend all that money for?” Thus, they choose to proceed based only on exploratory results. Again, the researcher has fulfilled the profes- sional obligation as long as the tentative nature of any ideas derived from exploratory research has been relayed through the research report.

Again, this isn’t always a bad approach. If the decision itself does not involve a great deal of risk or if it can be reversed easily, the best course of action may be to proceed to implementation instead of investing more money in confirmatory research. Remember, research shouldn’t be per- formed if it will cost more than it will return.

Emotion Time, money, and emotion are all related. Decision makers sometimes become so anxious to have something resolved, or they get so excited about some novel discovery resulting from a focus group interview, that they may act rashly. Perhaps some of the ideas produced during the motivational research era sounded so enticing that decision makers got caught up in the emotion of the moment and proceeded without the proper amount of testing. Thus, as in life, when we fall in love with something, we are prone to act irrationally. The chances of emotion interfering in this way are lessened, but not reduced, by making sure multiple decision makers are involved in the decision process.

In conclusion, we began this section by suggesting that exploratory, qualitative research cannot take the place of a confirmatory study. However, a confirmatory study cannot take the place of an exploratory, qualitative study either. While confirmatory studies are best for testing specific ideas, a qualitative study is far better suited to developing ideas and practical theories.

SUMMARY 1. List and understand the differences between qualitative research and quantitative research. The chapter emphasized that any argument about the overall superiority of qualitative versus quantitative research is misplaced. Rather, each approach has advantages and disadvan- tages that make it appropriate in certain situations. The presence or absence of numbers is not the key factor discriminating between qualitative and quantitative research. Qualitative research relies more on researchers’ subjective interpretations of text or other visual material. In contrast,

CHAPTER 7 Qualitative Research Tools • 155

the numbers produced in quantitative research are objective in the sense that they don’t change simply because someone else computed them. Thus, we expect quantitative research to have intersubjective certifiability, while qualitative research may not. Qualitative research typically involves small samples while quantitative research usually uses large samples. Qualitative pro- cedures are generally more flexible and produce deeper and more elaborate explanations than quantitative research.

2. Understand the role of qualitative research in exploratory research designs. The high degree of flexibility that goes along with most qualitative techniques makes it very useful in exploratory research designs. Therefore, exploratory research designs most often involve some qualitative research technique.

3. Describe the basic qualitative research orientations. Phenomenology is a philosophical approach to studying human experiences based on the idea that human experience itself is inher- ently subjective and determined by the context within which a person experiences something. It lends itself well to conversational research. Ethnography represents ways of studying cultures through methods that include high involvement with that culture. Participant-observation is a common ethnographic approach. Grounded theory represents inductive qualitative investigation in which the researcher continually poses questions about a respondent’s discourse in an effort to derive a deep explanation of their behavior. Collages are sometimes used to develop grounded theory. Case studies simply are documented histories of a particular person, group, organization, or event.

4. Prepare a focus group interview outline. A focus group outline should begin with introduc- tory comments followed by a very general opening question that does not lead the respondent. More specific questions should be listed until a blunt question directly pertaining to the study objective is included. However, a skilled moderator can often lead the group without having to explicitly state these questions. It should conclude with debriefing comments and a chance for question-and-answers with respondents.

5. Recognize technological advances in the application of qualitative research approaches. Videoconferencing and online chat rooms are more economical ways of trying to do much the same as traditional focus group interviews. Some companies have even established a focus blog that is a source for continuous commentary on a company. While they are certainly cost advanta- geous, there is less control over who participates.

6. Recognize common qualitative research tools and know the advantages and limitations of their use. The most common qualitative research tools include the focus group interview and the depth interview. The focus group has some cost advantage per respondent because it would take 10 times as long to conduct the interview portion(s) of a series of depth interviews compared to one focus group. However, the depth interview is more appropriate for discussing sensitive topics.

7. Know the risks associated with acting on only exploratory results. Companies do make decisions using only exploratory research. There are several explanations for this behavior. The researcher’s job is to make sure that decision makers understand the increased risk that comes along with basing a decision only on exploratory research results.

KEY TERMS AND CONCEPTS case studies, 139 conversations, 150 depth interview, 149 discussion guide, 145 ethnography, 138 field notes, 152 focus blog, 147 focus group interview, 142 free-association techniques, 151 grounded theory, 139

hermeneutic unit, 137 hermeneutics, 137 intersubjective certifiability, 134 laddering, 149 moderator, 145 online focus group, 147 participant-observation, 138 phenomenology, 136 piggyback, 142 projective technique, 152

qualitative business research, 132 qualitative data, 135 quantitative business research, 134 quantitative data, 135 replication, 154 researcher-dependent, 132 subjective, 134 thematic apperception test (TAT), 152 themes, 139

156 Part 2: Beginning Stages of the Research Process

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Define qualitative and quantitative research. Compare and con-

trast the two approaches. 2. Why do exploratory research designs rely so much on qualita-

tive research techniques? 3. Why do causal designs rely so much on quantitative research

techniques? 4. What are the basic orientations of qualitative research? 5. Of the four basic orientations of qualitative research, which do

you think is most appropriate for a qualitative approach designed to better define a business situation prior to conducting confir- matory research?

6. What type of exploratory research would you suggest in the fol- lowing situations? a. A product manager suggests development of a nontobacco

cigarette blended from wheat, cocoa, and citrus. b. A research project has the purpose of evaluating potential

names for a corporate spin-off. c. A human resource manager must determine the most

important benefits of an employee health plan. d. An advertiser wishes to identify the symbolism associated

with cigar smoking. 7. What are the key differences between a focus group interview

and a depth interview?

8. ’NET Visit some websites for large companies like Honda, Qantas Airlines, Target, Tesco, and Marriott. Is there any evidence that they are using their Internet sites in some way to conduct a continuous online focus blog or intermittent online focus groups?

9. What is laddering? How might it be used in trying to under- stand which fast-food restaurant customers prefer?

10. Comment on the following remark by a business consultant: “Qualitative exploration is a tool of research and a stimulant to thinking. In and by itself, however, it does not constitute busi- ness research.”

11. ETHICS A researcher tells a manager of a wine company that he has some “cool focus group results” suggesting that respondents like the idea of a screw cap to top wine bottles. Even before the decision maker sees the report, the manager begins purchasing screw caps and the new bottling equipment. Comment on this situation.

12. A packaged goods manufacturer receives many thousands of customer letters a year. Some are complaints, some are compliments. They cover a broad range of topics. Are these letters a possible source for exploratory research? Why or why not?

RESEARCH ACTIVITIES 1. ’NET How might the following organizations use an Internet

chat room for exploratory research? a. A provider of health benefits b. A computer software manufacturer c. A video game manufacturer

2. Go back to the opening vignette. What if Vans approached you to do a focus group interview that explored the idea of offering casual attire (off-board) aimed at their primary segment (skate- boarders) and offering casual attire for male retirees like Samuel Teel? How would you recommend the focus group(s) proceed? Prepare a focus group outline(s) to accomplish this task.

3. Interview two people about their exercise behavior. In one interview, try to use a semistructured approach by prepar- ing questions ahead of time and trying to have the respondent complete answers for these questions. With the other, try a conversational approach. What are the main themes that emerge in each? Which approach do you think was more insightful? Do you think there were any “sensitive” topics that a respondent was not completely forthcoming about?

In February 2009, bushfires raced across the Austra- lian state of Victoria. This terrible tragedy resulted in the loss of over 300 lives, Australia’s highest ever loss

of life from a bushfire. In addition, more than 2,000 homes were destroyed and insurance losses are estimated to exceed $2 billion.33 While rebuilding will take years, at some point after these disasters, it is time to get back to business. But major catastrophic events are likely to leave permanent changes on consumers and employees in the affected areas.

Suppose you are approached by the owner of several full-service wine stores in Victoria. It is January 2010, and they want to get back to business. But they are uncertain about whether they should simply maintain the same positioning they had previous to the bushfires. They would like to have a report from you within 60 days.

Questions 1. How could each orientation of qualitative research be used

here? 2. What qualitative research tool(s) would you recommend be used

and why? 3. Where would you conduct any interviews and with whom

would you conduct them? 4. ETHICS Are there ethical issues that you should be sensitive to in

this process? Explain. 5. What issues would arise in conducting a focus group interview

in this situation? 6. Prepare a focus group outline.

Disaster and Consumer Value

CASE 7.1

Chapter 7: Qualitative Research Tools 157

Edward Jones is one of the largest investment firms in the United States, with over 4,000 branch offices in this country, Canada, and the United Kingdom.

It is the only major brokerage firm that exclusively targets individual investors and small businesses, and it has nearly 6 million clients.

Edward Jones’ philosophy is to offer personalized services to indi- vidual clients starting with a one-on-one interview. During the inter- view, investment representatives seek to identify each client’s specific goals for investing. Richard G. Miller, one such representative, says that he needs to thoroughly understand what a client wants before he can build an investment strategy for that person. His initial conversation starts with, “Hey, how are you?” Gregory L. Starry, another representa- tive, confirms the Edward Jones philosophy: “Most of my day is spent talking with and meeting clients [rather than placing stock trades].”

Only after learning these goals do the representatives design an investment strategy that will provide a client with income, growth, and safety. Each client’s goals also evolve over time. Young people

are focused on earning enough money to make a down payment on their first home or to buy a car. Clients in the 35 to 45 age range are concerned about getting their children through school and about their own retirement. Those in retirement want to make sure that they have an adequate income level. Miller notes, “It’s not the tim- ing in the market, but the time in the market” that will help clients achieve their goals.

Questions 1. Many people in minority groups, including African Americans,

Hispanic Americans, Asian Americans, and Native Americans, do not invest. What exploratory research should Edward Jones do to develop business with these minority markets?

2. Another group with low investment activity includes those who stopped their education at the high school level. What factors should Edward Jones representatives consider in designing focus groups with these potential clients?

Edward Jones

CASE 7.2

158 Part 2: Beginning Stages of the Research Process

159

C H A P T E R

8 After studying this chapter, you should be able to

1. Discuss the advantages and disadvantages of secondary data

2. Define types of secondary data analysis conducted by business research managers

3. Identify various internal and proprietary sources of secondary data

4. Give examples of various external sources of secondary data

5. Describe the impact of single-source data and globalization on secondary data research

LEARNING OUTCOMES

Secondary Data Research in a Digital Age

A key problem that faces any business research manager is the need to constantly capture relevant data about customers, competitors, and/or market characteristics. The use of secondary data (i.e.,

data that has been collected previously for other purposes) has exploded with the advent of large-scale electronic information sources and the web. One company that has taken full advan- tage of integrating various business related information sources is Nielsen Claritas.

Prior to its merge with The Nielsen Company, Claritas (which in Latin means “brightness”) had a 40-year history of collecting and integrating business-related data from difference sources. Its products include (1) PRIZM, which provides market segmentation information based upon consumer behavior and geographic loca- tion; (2) Consumer Point, a target marketing analysis solution for different industry spaces; and (3) Business-Facts, which provides accurate business data for market support and strategic planning.

Business-Facts holds great promise as a secondary data source for existing companies. Using Standard Industrial Classifi- cation (SIC) and North American Industry Classification (NAICS)1 codes developed through the Census Bureau, characteristics on business ownership, location, employment, and sales are available for 10 major industrial groupings. Data and employee counts within the Business-Facts system represent over

13 million businesses. Examples of these industry groups include construction, manufacturing, and retail sales establishments across the United States. Since business information can become quickly obsolete, Nielsen Claritas spends millions of dollars each year to verify business information on a quarterly basis.

Chapter Vignette:

Business Facts on a Grand Scale

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Introduction Business researchers are always working under time and budget constraints. So, they are wise to ask if the data needed to examine the research questions already exists. If so, the analysis can proceed quickly, efficiently, and at minimum cost. Therefore, research projects should begin with secondary data, which are gathered and recorded by someone else prior to, and for purposes other than, the current project. Secondary data usually are already assembled. They require no access to respondents or subjects.

Advantages of Secondary Data The primary advantage of secondary data is their availability. Obtaining secondary data is almost always faster and less expensive than acquiring primary data. This is particularly true when research- ers use electronic retrieval to access data stored digitally. In many situations, collecting secondary data is instantaneous and free.

Consider the money and time saved by researchers who obtained updated population esti- mates for a town since the 2010 Census. Instead of doing the fieldwork themselves, researchers could acquire estimates from a firm dealing in demographic information or from sources such as Nielsen Claritas or PCensus. As in this example, the use of secondary data eliminates many of the activities normally associated with primary data collection, such as sampling and data processing.

Secondary data are essential in instances when data simply cannot be obtained using primary data collection procedures. For example, a manufacturer of farm implements could not duplicate the information in the Census of Agriculture due to the sheer amount of information and the fact that much of the information (for example, amount of taxes paid) might not be provided to a private firm. Similarly, in India researchers use census estimates to track sensitive topics like child labor rates, which would be simply overwhelming for a private organization to undertake.

Disadvantages of Secondary Data An inherent disadvantage of secondary data is that they were not designed to meet the researchers’ specific needs. Thus, researchers must ask how pertinent the data are to their particular project. To evaluate secondary data, researchers should ask questions such as these:

■ Is the subject matter consistent with our problem definition? ■ Do the data apply to the population of interest? ■ Do the data apply to the time period of interest? ■ Do the secondary data appear in the correct units of measurement? ■ Do the data cover the subject of interest in adequate detail?

Even when secondary information is available, it can be inadequate. Consider the following typical situations:

■ A researcher interested in forklift trucks finds that the secondary data on the subject are included in a broader, less pertinent category encompassing all industrial trucks and tractors. Furthermore, the data were collected five years earlier.

secondary data

Data that have been previously collected for some purpose

other than the one at hand.

‘‘If I have seen farther than others, it is because I have stood on the shoulders of giants.’’—ISAAC NEwTON

The advantages of knowing broadly both the characteristics and location of major customer groups (or potential competi- tors) are very real. Using a sophisticated statistical modeling approach, Nielsen Claritas can link your customers to your exist- ing or proposed locations, in a fashion such that the informa- tion is as timely and applicable as possible.

All of the information sources within Nielsen Claritas add value to business users by satisfying two very critical needs. First,

Nielsen Claritas has expertise in linking different data streams into a cohesive system. This allows users to answer through secondary data sources critical existing business questions. Secondly, their information systems are geographically based, so that businesses can query data to a common point on the globe.

Truly, the integration and utilization of secondary data sources by Nielsen Claritas has put business research “on the map”—both literally and figuratively!

160 • PART TWO Beginning Stages of the Research Process

161

■ An investigator who wishes to study individuals earning more than $100,000 per year finds the top category in a secondary study reported at $75,000 or more per year.

■ A brewery that wishes to compare its per-barrel advertising expenditures with those of com- petitors finds that the units of measurement differ because some report point-of-purchase expenditures with advertising and others do not.

■ Data from a previous warranty card study show where consumers prefer to purchase the prod- uct but provide no reasons why.

While it is wonderful to be able to access secondary data quickly and cheaply, a researcher needs to be careful that it accurately addresses the research issues. Secondary data often do not ade- quately satisfy research needs because of (1) variation in definitions of terms, (2) the use of dif- ferent units of measurement, (3) inadequate information to verify the data’s validity, and (4) data that are too old.

Every primary researcher has the right to define the terms or concepts under investigation to satisfy the purpose of his or her primary investigation. This practice provides little solace, however, to the investigator of the African-American market who finds secondary data reported as “percent nonwhite.” Variances in terms or variable classifications should be scrutinized to determine whether differences are important. The populations of interest must be described in comparable terms. Researchers frequently encounter secondary data that report on a population of interest that is similar but not directly comparable to their population of interest. For example, Arbitron reports its television audience estimates by geographical areas known as ADIs (Areas of Dominant Influence). An ADI is a geographic area consisting of all counties in which the home market commercial television stations receive a preponderance of total viewing hours. This unique population of interest is used exclusively to report television audiences. The geographic areas used in the census of population, such as Metropolitan Statistical Areas, are not comparable to ADIs.

Units of measurement may cause problems if they do not conform exactly to a researcher’s needs as well. For example, lumber shipments in millions of board feet are quite different from billions of ton-miles of lumber shipped on freight cars. Head-of-household income is not the same unit of measure as total family income. Often the objective of the original primary study may dictate that the data be summarized, rounded, or reported. When that happens, even if the original units of measurement were comparable, aggregated or adjusted units of measurement are not suit- able in the secondary study.

When secondary data are reported in a format that does not exactly meet the researcher’s needs, data conversion may be necessary. Data conversion (also called data transformation) is the pro- cess of changing the original form of data to a format more suitable for achieving a stated research

data conversion

The process of changing the original form of the data to a format suitable to achieve the research objective; also called data transformation.

SURVEY

THIS! The data in the online survey provide qualitative and quantitative data based upon responses from students around the world. while some of these data are centered on university expe- riences and attitudes, several data variables are similar to the kinds of data gathered from public opinion research. For example, take a look at some basic results (such as how many people strongly agree) on a few of the items in the online survey related to how a person’s job affects them outside of work. Then, do a Google search on terms like “work tension opinions” and “work stress study.” Look at the linked documents. Do the results obtained from the online survey appear consistent with other opinion study results?

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objective. For example, sales for food products may be reported in pounds, cases, or dollars. An estimate of dollars per pound may be used to convert dollar volume data to pounds or another suitable format.

Another disadvantage of secondary data is that the user has no control over their validity—a topic we will discuss in more detail later. For now, think of this as representing data accuracy or trustworthiness. Although timely and pertinent secondary data may fit the researcher’s require- ments, the data could be inaccurate. The research methods used to collect the data may have some- how introduced bias to the data. For example, media often publish data from surveys to identify the characteristics of their subscribers or viewers. These data will sometimes exclude derogatory data from their reports. Good researchers avoid data with a high likelihood of bias or for which the overall accuracy cannot be determined.

Investigators are naturally more prone to accept data from reliable sources such as the U.S. government. Nevertheless, the researcher must assess the reputation of the organization that gathers the data and critically assess the research design to determine whether the research was correctly implemented. Unfortunately, such evaluation may be impossible without full information that explains how the original research was conducted.

Finally, data must be current to be useful. While technology today allows much faster access to data, our rapidly changing environment also means information quickly becomes outdated. Because the purpose of most studies is to use current data to predict the future, secondary data must be timely to be useful.

Researchers should verify the accuracy of the data whenever possible. Cross-checks of data from multiple sources, similar to what Nielsen Claritas does with its Business-Facts database, should be made to determine the similarity of independent projects. When the data are not consistent, researchers should attempt to identify reasons for the differences or to determine which data are most likely to be correct. If the accuracy of the data cannot be established, the researcher must determine whether using the data is worth the risk. Exhibit 8.1 illustrates a series of questions that should be asked to evaluate secondary data before they are used.

Typical Objectives for Secondary Data Research Designs It would be impossible to identify all the purposes of research using secondary data. However, some common business problems that can be addressed with secondary research designs are useful. Exhibit 8.2 shows three general categories of research objectives: fact-finding, model building, and database marketing.

Fact-Finding The simplest form of secondary data research is fact-finding. A restaurant serving breakfast might be interested in knowing what new products are likely to entice consumers. Secondary data avail- able from National Eating Trends, a service of the NPD Group, show that the most potential may be in menu items customers can eat on the go.2 According to data from the survey of eating trends, the increased prevalence of nutrition and calorie counts on menus has not significantly affected consumer choices. In addition, while overall restaurant sales dropped during the economic down- turn of 2009 and 2010, fast casual restaurants such as Chili’s have seen their sales go up. Another research firm, Market Facts, says almost half of consumers say they would pay extra for cheese. These simple facts would interest a researcher who was investigating today’s dining market.

Identification of Consumer Behavior for a Product Category A typical objective for a secondary research study might be to uncover all available information about consumption patterns for a particular product category or to identify demographic trends that affect an industry. For example, a company called Servigistics offers software that will scan

cross-checks

The comparison of data from one source with data from

another source to determine the similarity of independent

projects.

162 • PART TWO Beginning Stages of the Research Process

Source: The idea for Exhibit 8.1 came from Robert W. Joselyn, Designing the Marketing Research Project (New York: Petrocelli/Charter, 1977).

Do the data help to answer questions set out in the

problem definition?

Do the data apply to the time period of interest?

Do the data apply to the population of interest?

Do other terms and variable classifications presented

apply to the current project?

Are the units of measurement comparable?

Is it possible to go to the original source of the data?

Is the cost of data acquisition worth it?

Is there a possibility of bias?

Can the accuracy of data collection be verified?

Use data

Yes

Yes (accurate)

Stop

Can the data be reworked?

If yes, continue

Stop

(inaccurate or unsure)

Applicability to the current

project

Accuracy of the data

Stop

YesIs using the data worth

the risk?

Yes

EXHIBIT 8.1

Evaluating Secondary Data

a company’s own parts inventory data and compare it with marketing objectives and competitors’ prices to evaluate whether the company should adjust prices for its parts. Kia Motors tried using this service in place of the usual method of marking up cost by a set fraction. By considering secondary data including internal inventory data and external data about competitors’ prices, it was able to make service parts a more profitable segment of its business.3 This example illustrates

CHAPTER 8 Secondary Data Research in a Digital Age • 163

the wealth of factual information about consumption and behavior patterns that can be obtained by carefully collecting and analyzing secondary data.

Trend Analysis Business researchers are challenged to constantly watch for trends in the marketplace and the envi- ronment. Market tracking is the observation and analysis of trends in industry volume and brand share over time. Scanner research services and other organizations provide facts about sales volume to support this work.

Almost every large consumer goods company routinely investigates brand and product cat- egory sales volume using secondary data. This type of analysis typically involves comparisons with competitors’ sales or with the company’s own sales in comparable time periods. It also involves industry comparisons among different geographic areas. Exhibit 8.3 shows information available from secondary sources regarding the largest retail store chains in Europe.

ENVIRONMENTAL SCANNING In many instances, the purpose of fact-finding is simply to study the environment to identify trends. Environmental scanning entails information gathering and fact-finding designed to detect indications of environmental changes in their initial stages of development. The Internet can be used for environmental scanning; however, there are other means, such as periodic review of contemporary publications and reports. For example, environmental scanning has shown many researchers that consumer demand in China is skyrocketing. In the case of beauty products such

as cosmetics, Chinese authorities in the early 1990s stopped discouraging the use of makeup, and sales of these prod- ucts took off—hitting $524 million in 2005. Sales continue to skyrocket, as the total Chinese market for cosmetics and toiletries was RMB 133.24 billion ($19.51 billion) in 2009. The market is up over 11 percent from 2008 despite the economic conditions. Companies including Procter & Gamble, L’Oréal, and Shiseido have captured a sizable share of this market by realizing the potential and developing products to get into the Chinese market early.4

A number of online information services, such as Factiva and LexisNexis, routinely collect news stories about industries, product lines, and other top- ics of interest that have been specified by the researcher. In addition, push technology is an Internet information

market tracking

The observation and analysis of trends in industry volume and

brand share over time.

Broad Objective Specific Research Example

Fact-finding Identifying consumption patterns Tracking trends

Model building Estimating market potential Forecasting sales Selecting trade areas and sites

Database marketing Enhancing customer databases Developing prospect lists

EXHIBIT 8.2

Common Research Objectives for Secondary Data Studies

Secondary data shows which restaurants have seen increases in sales.

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model building

The use of secondary data to help specify relationships between two or more variables; can involve the development of descriptive or predictive equations.

Rank Store Name Home Country Type of Store

1 WalMart United States Discount Store

2 Carrefour France Hypermarket/Supercenter/Superstore

3 Metro AG Germany Cash & Carry/Warehouse Club

4 Tesco plc U. K. Hypermarket/Supercenter/Superstore

5 Schwarz Untemehmens Germany Discount Store

2011 Largest European Retail Chains—Complete List of Biggest Europe Retailers Source: From http://retailindustry.about.com/od/largesteuropeanretailers/a/largest_european_largest_retail_chains-2011-world_rankings_ europes-biggest-retailers.htm, accessed May 12, 2011.

EXHIBIT 8.3

2011 Largest Retail Chains in Europe

technology that automatically delivers content to the researcher’s or manager’s desktop. Push tech- nology uses “electronic smart agents,” custom software that filters, sorts, prioritizes, and stores information for later viewing.5 This service frees the researcher from doing the searching. The true value of push technology is that the researcher who is scanning the environment can specify the kinds of news and information he or she wants, have it delivered to his or her computer quickly, and view it at leisure.

Model Building The second general objective for secondary research, model building, is more complicated than simple fact-finding. Model building involves specifying relationships between two or more vari- ables, perhaps extending to the development of descriptive or predictive equations, a technique that is used by Nielsen Claritas routinely to add value to their secondary data. Models need not include complicated mathematics, though. In fact, decision makers often prefer simple models that everyone can readily understand over complex models that are difficult to comprehend. For exam- ple, market share is company sales divided by industry sales. Although some may not think of this simple calculation as a model, it represents a mathematical model of a basic relationship.

RESEARCH SNAPSHOT

Secondary data, but for its name, may seem to lack power compared to primary data. However, with secondary data, researchers can test research questions that would be difficult to examine any other way. For example, what firms are the market leaders in their industry? what matters when it comes to firm performance? Does customer satisfaction ultimately lead to superior firm performance? Given that firm performance is a property of the company and not of its customers or employ- ees, the researcher cannot directly capture this with surveys. Therefore, researchers turn to secondary data to try to isolate controllable variables that drive firm performance.

Basic research addresses this issue by tapping into second- ary data sources such as the Nielsen database, Compustat, and the American Consumer Satisfaction Index (the ACSI). what should companies emphasize? Advertising, services, value, or satisfaction? Statistical models using secondary data suggest

that advertising, satisfaction, and services all play a role. But, as an industry becomes more competitive, services and value come to the top in shaping a firm’s stock value. Thus, what matters? It seems like firms should allocate scarce resources toward increas- ing services and greater value. That’s a recipe for success.

Sources: Grewal, P., M. Chandrashkaran and A. V. Citrin, “Customer Satisfaction Heterogeneity and Shareholder Value,” Journal of Marketing Research, 47 (August 2010), 612–626. Fang, E., R. W. Palmatier and J. B. Steencamp, “Effect of Service Transition Strategies on Firm Value,” Journal of Marketing, 72 ( September 2008), 1–14. Luo, X. and C. Homburg, “Neglected Outcomes of Consumer Satisfaction,” Journal of Marketing, 71 (April 2007), 133–149.

Does It Matter?

Carrefour, the second largest retailer in the world, has the recipe for success.

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We will illustrate model building by discussing three common objectives that can be satisfied with secondary research: estimating market potential, forecasting sales, and selecting potential facil- ity or expansion sites.

Estimating Market Potential for Geographic Areas Business researchers often estimate their company’s market potential using secondary data. In many cases exact figures may be published by a trade association or another source. However, when the desired information is unavailable, the researcher may estimate market potential by transforming secondary data from two or more sources. For example, managers may find secondary data about market potential for a country or other large geographic area, but this information may not be broken down into smaller geographical areas, such as by metropolitan area, or in terms unique to the company, such as sales territory. In this type of situation, researchers often need to make projec- tions for the geographic area of interest.

Suppose a Belgian beer company is looking for opportunities to expand sales by exporting or investing in other countries. Managers decide to begin by estimating market potential in sev- eral potential target markets. Secondary research uncovered data for per capita beer consumption in numerous sources including reports from Data Monitor, a company that catalogs commercial statistics by country. Population estimates are also available in several places including the census bureau and through the CIA (see www.cia.gov to access the World Factbook). Exhibit 8.4 illus- trates the main findings compiled.

The trade area market potential for the Czech Republic in 2012 is found by multiplying the country’s population estimate6 by the per capita beer consumption:

10,190,000 people × 160 liters/person = 1,630,400,000 liters

That’s over a bottle a day per person. To get a sense of the expected sales volume, the marketer would have to multiply this amount by the price per liter at which beer typically sells. Although the Czech Republic may be an attractive market, greater overall volume might be offered by other markets with larger overall populations. As Exhibit 8.4 reveals, China offers the largest potential market for beer sales in the world.7 Brazil and the U.S. also display relatively high total beer con- sumption. Although those countries aren’t known so much for beer consumption, the sheer size of the markets makes them attractive targets for the brewery.

Of course, the calculated market potential for each country in Exhibit 8.4 is a rough estimate. One obvious problem is that not everyone in a country will be of beer-drinking age. If the researcher can get statistics for each country’s projected adult population, the estimate will be closer. This illustrates one of the weaknesses of secondary data. Also, you might want to consider whether each country is experiencing growth or decline in the demand for beer to estimate whether

Country 2012 Population

Estimate (thousands)

Annual per Capita Beer Consumption

(liters) Market Potential Estimate (k liters)

United States 315,000 85 26,775,000

Germany 81,000 105 8,505,000

Australia 22,000 107 2,354,000

Brazil 205,000 55 11,275,000

China 1,400,000 25 35,000,000

Czech Republic 10,190 160 1,630,400

Sources: https://www.cia.gov/library/publications/the-world-factbook/index.html, accessed April 4, 2010; http://www.just-drinks.com, accessed April 4, 2011.

EXHIBIT 8.4

Market Potential for Select Geographic Areas by Country

166 • PART TWO Beginning Stages of the Research Process

consumption habits are likely to be different in 2012. For example, beer consumption is barely growing in Europe and Japan, but it is expanding in Latin America (at about 4 percent a year) and even faster in China (by at least 6 percent a year).8 Perhaps this information will encourage you to investigate market potential in additional countries where more growth is expected.

Forecasting Sales Sales forecasting is the process of predicting sales totals over a specific time period. Accurate sales forecasts, especially for products in mature, stable markets, frequently come from secondary data research that identifies trends and extrapolates past performance into the future. Business research- ers often use internal company sales records to project sales. A rudimentary model would multiply past sales volume by an expected growth rate. A researcher might investigate a secondary source and find that industry sales normally grow about 10 percent per year; multiplying company sales volume by 10 percent would give a basic sales forecast.

Exhibit 8.5 illustrates trend projection using a moving average projection of growth rates. Aver- age ticket prices for a major-league baseball game are secondary data from Team Marketing Report (http://www.teammarketing.com). The moving average is the sum of growth rates for the past three years divided by 3 (number of years). The resulting number is a forecast of the percentage increase in ticket price for the coming year. Using the three-year average growth rate of 1.9 percent for the 2009, 2010, and 2011 sales periods, we can forecast the average ticket price for 2012 as follows:

$26.92 + ($26.92 × .019) = $27.43

A major league baseball team is probably more interested in financial metrics like revenue. Using the ticket price for any season, one can compute average ticket sales revenue for any

Year Average Ticket

Price ($)

Percentage Rate of Growth (Decline) from

Previous Year

3-Year Moving Average Rate of Growth

(Decline)

1996 11.20 5.2% 3.5%

1997 12.36 10.4% 5.8%

1998 13.59 10.0% 8.5%

1999 14.91 9.7% 10.0%

2000 16.67 11.8% 10.5%

2001 18.99 13.9% 11.8%

2002 18.30 –3.6% 7.4%

2003 19.01 3.9% 4.7%

2004 19.82 4.3% 1.5%

2005 21.17 6.8% 5.0%

2006 22.21 4.9% 5.3%

2007 22.70 2.2% 4.6%

2008 25.43 12.0% 6.4%

2009 26.21 3.0% 5.7%

2010 26.60 1.5% 5.5%

2011 26.92 1.2% 1.9%

EXHIBIT 8.5

Secondary Data for Major League Baseball Ticket Prices with Moving Average

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CHAPTER 8 Secondary Data Research in a Digital Age • 167

upcoming season by multiplying the average major league attendance projection times the num- ber of home games (81) times the average ticket price. For the year 2011, the estimated attendance using the three-year moving average is 31,006. Thus, the estimated revenue for a typical team is:

31,006 tickets/game × 81 games × $26.92 per ticket = $67,609,203

Moving average forecasting is best suited to a static environment. More dynamic situations make other sales forecasting techniques more appropriate.

Often, more sophisticated forecasting approaches are used. We’ll discuss other forecasting methods later in the book but simple moving averages like the three-year moving average are often applied in practice.

Analysis of Trade Areas and Sites Managers routinely examine trade areas and use site analysis techniques to select the best loca- tions for retail or wholesale operations. Secondary data research helps managers make these site selection decisions. Some organizations, especially franchisers, have developed special computer software based on analytical models to select sites for retail outlets. The researcher must obtain the appropriate secondary data for analysis with the computer software.

The index of retail saturation offers one way to investigate retail sites and to describe the rela- tionship between retail demand and supply.9 It is easy to calculate once the appropriate secondary data are obtained:

Index of retail saturation = Local market potential (demand)

Local market retailing space

For example, Exhibit 8.6 shows the relevant secondary data for shoe store sales in a five-mile radius surrounding a Florida shopping center. These types of data can be purchased from vendors of mar- ket information such as Urban Decision Systems. First, to estimate local market potential (demand), we multiply population by annual per capita shoe sales. This estimate, line 3 in Exhibit 8.6, goes in the numerator to calculate the index of retail saturation:

Index of retail saturation = $14,249,000

94,000 = 152

The retailer can compare this index figure with those of other areas to determine which sites have the greatest market potential with the least amount of retail competition. An index value above 200 is considered to indicate exceptional opportunities.

index of retail saturation

A calculation that describes the relationship between retail

demand and supply.

site analysis techniques

Techniques that use secondary data to select the best loca- tion for retail or wholesale

operations.

1. Population 261,7852

2. Annual per capita shoe sales $54.43

3. Local market potential (line 1 × line 2) $14,249,000

4. Square feet of retail space used to sell shoes 94,000 sq. ft.

5. Index of retail saturation (line 3/line 4) 152

EXHIBIT 8.6

Secondary Data for Calculating an Index of Retail Saturation

Data Mining Large corporations’ decision support systems often contain millions or even hundreds of millions of records of data. These complex data volumes are too large to be understood by managers. Con- sider, for example, Capital One, a consumer lending company with nearly 50 million customer accounts, including credit cards and auto loans. Suppose the company collects data on customer purchases, and each customer makes five transactions in a month, or 60 per year. With 50 million

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customers and decades of data (the company was founded in 1988), it’s easy to see how record counts quickly grow beyond the comfort zone for most humans.10

Two points about data volume are important to keep in mind. First, relevant data are often in independent and unrelated files. Second, the number of distinct pieces of information each data record contains is often large. When the number of distinct pieces of information contained in each data record and data volume grows too large, end users don’t have the capacity to make sense of it all. Data mining helps clarify the underlying meaning of the data.

The term data mining refers to the use of powerful computers to dig through volumes of data to discover patterns about an organization’s customers and products. As seen in the Research Snap- shot “Mining Data from Blogs,” this can even apply to Internet content from blogs. It is a broad term that applies to many different forms of analysis. For example, neural networks are a form of artificial intelligence in which a computer is programmed to mimic the way that human brains process information. One computer expert put it this way:

A neural network learns pretty much the way a human being does. Suppose you say “big” and show a child an elephant, and then you say “small” and show her a poodle. You repeat this process with a house and a giraffe as examples of “big” and then a grain of sand and an ant as examples of “small.” Pretty soon she will figure it out and tell you that a truck is “big” and a needle is “small.” Neural networks can similarly generalize by looking at examples.11

Market-basket analysis is a form of data mining that analyzes anonymous point-of-sale trans- action databases to identify coinciding purchases or relationships between products purchased and other retail shopping information.12 Consider this example about patterns in customer purchases: Osco Drugs mined its databases provided by checkout scanners and found that when men go to its drugstores to buy diapers in the evening between 6:00 p.m. and 8:00 p.m., they sometimes walk out with a six-pack of beer as well. Knowing this behavioral pattern, supermarket managers may consider laying out their stores so that these items are closer together.13

Customer discovery is a data-mining application that similarly involves mining data to look for patterns that can increase the value of customers. For example, Macy’s commissioned data-mining techniques looking for patterns of relationships among the huge volumes of previous sales records. In 2011, Macy’s sent out millions of catalogs. Not every customer got the same catalog though and in fact, tens of thousands of version of the catalog were carefully tailored to specific customers.14 Female customers 30 years of age will see more handbags, accessories, shoes, and women’s clothing than will a middle-aged man. Further, if her individual records show purchases of baby clothing,

data mining

The use of powerful computers to dig through volumes of data to discover patterns about an organization’s customers and products; applies to many different forms of analysis.

neural networks

A form of artificial intelligence in which a computer is programmed to mimic the way that human brains process information.

market-basket analysis

A form of data mining that analyzes anonymous point-of- sale transaction databases to identify coinciding purchases or relationships between products purchased and other retail shopping information.

customer discovery

Involves mining data to look for patterns identifying who is likely to be a valuable customer.

RESEARCH SNAPSHOT

One way to find out what people are thinking these days is to read what they are posting on their blogs. But with tens of mil- lions of blogs available on the Internet, there is no way to read them all. One solution: data-mining software designed for the blogosphere.

Umbria Communications, based in Boulder, Colorado, offers a program called Buzz Report, which searches 13 million blogs, looking for messages related to particular products and trends. Marketers can buy the service to find out what people are say- ing about their new products, or they can explore unmet needs in areas they might consider serving. Not only does Buzz Report identify relevant blogs, but it also has a language processor that can identify positive and negative messages and analyze word choices and spelling to estimate the writer’s age range and sex. The company’s CEO, Howard Kaushansky, says the program can even recognize sarcasm.

Most of Umbria’s clients are large makers of consumer products, including Sprint and Electronic Arts. U.S. Cellular used Buzz Report to learn that teenage users of cell phones are par- ticularly worried about using more than their allotted minutes, fearing that parents would take the extra amount from their allowance. Such knowledge is useful for developing new service plans and marketing messages.

Sources: Based on Finn, Bridget, “Mining Blogs for Marketing Insight,” Business 2.0, (September 1, 2005), http://money.cnn.com/magazines/ business2/business2_archive/2005/09/01/8356522/index.htm, accessed August 4, 2011; Martin, Justin, “Blogging for Dollars,” Fortune Small Business (January 20, 2006), http://money.cnn.com/magazines/fsb/fsb_ archive/2005/12/01/8365363/index.htm, accessed August 4, 2011.

Mining Data from Blogs

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she’ll probably see lots of things for toddlers. The end result is that the customer sees more pur- chase possibilities that have a high probability of addressing some current need or desire.

When a company knows the identity of the customer who makes repeated purchases from the same organization, an analysis can be made of sequences of purchases. The use of data mining to detect sequence patterns is a popular application among direct marketers, such as catalog retailers. A catalog merchant has information for each customer, revealing the sets of products that the cus- tomer buys in every purchase order. A sequence detection function can then be used to discover the set of purchases that frequently precedes the purchase of, say, a microwave oven. As another example, a sequence of insurance claims could lead to the identification of frequently occurring medical procedures performed on patients, which in turn could be used to detect cases of medical fraud.

Data mining requires sophisticated computer resources, and it is expensive. That’s why companies like DataMind, IBM, Oracle, Information Builders, and Acxiom Corporation offer data- mining services. Customers send the databases they want analyzed and let the data-mining company do the “number crunching.”

Database Marketing and Customer Relationship Management CRM (customer relationship management) systems are decision support systems that manage the interactions between an organization and its customers. A CRM maintains customer databases containing customers’ names, addresses, phone numbers, past purchases, responses to past promo- tional offers, and other relevant data such as demographic and financial data. Database marketing is the practice of using CRM databases to develop one-to-one relationships and precisely targeted promotional efforts with individual customers. For example, a fruit catalog company CRM con- tains a database of previous customers, including what purchases they made during the Christmas holidays. Each year the company sends last year’s gift list to customers to help them send the same gifts to their friends and relatives.

Because database marketing requires vast amounts of CRM data compiled from numerous sources, secondary data are often acquired for the exclusive purpose of developing or enhancing databases. The transaction record, which often lists the item purchased, its value, customer name, address, and zip code, is the building block for many databases. This may be supplemented with data customers provide directly, such as data on a warranty card, and by secondary data purchased from third parties. For example, credit services may sell databases about applications for loans, credit card payment history, and other financial data. Several companies, such as Infogroup (with its sales and marketing data services) and Nielsen Claritas (with PRIZM), collect primary data and then sell demographic data that can be related to small geographic areas, such as those with a certain zip code. (Remember that when the vendor collects the data, they are primary data, but when the database marketer incorporates the data into his or her database, they are secondary data.)

Now that some of the purposes of secondary data analysis have been addressed, we turn to a discussion of the sources of secondary data.

Sources of Secondary Data Secondary data can be classified as either internal to the organization or external. Modern informa- tion technology makes this distinction seem somewhat simplistic. Some accounting documents are indisputably internal records of the organization. Researchers in another organization cannot have access to them. Clearly, a book published by the federal government and located at a public library is external to the company. However, in today’s world of electronic data interchange, the data that appear in a book published by the federal government may also be purchased from an online informa- tion vendor for instantaneous access and subsequently stored in a company’s decision support system.

Internal data should be defined as data that originated in the organization, or data created, recorded, or generated by the organization. Internal and proprietary data is perhaps a more descriptive term.

database marketing

The use of customer databases to promote one-to-one

relationships with customers and create precisely targeted

promotions.

internal and proprietary data

Secondary data that originate inside the organization.

170 • PART TWO Beginning Stages of the Research Process

Sources of Internal and Proprietary Data Most organizations routinely gather, record, and store internal data to help them solve future problems. An organization’s accounting system can usually provide a wealth of information. Routine documents such as sales invoices allow external financial reporting, which in turn can be a source of data for further analysis. If the data are properly coded into a modular database in the accounting system, the researcher may be able to conduct more detailed analysis using the deci- sion support system. Sales information can be broken down by account or by product and region; information related to orders received, back orders, and unfilled orders can be identified; and sales can be forecast on the basis of past data. Other useful sources of internal data include salespeople’s call reports, customer complaints, service records, warranty card returns, and other records.

Researchers frequently aggregate or disaggregate internal data. For example, a computer ser- vice firm used internal secondary data to analyze sales over the previous three years, categorizing business by industry, product, purchase level, and so on. The company discovered that 60 percent of its customers represented only 2 percent of its business and that nearly all of these customers came through telephone directory advertising. This simple investigation of internal records showed that, in effect, the firm was paying to attract customers it did not want.

Internet technology is making it easier to research internal and proprietary data. Often compa- nies set up intranets so that employees can use web tools to store and share data within the organi- zation. And just as Google’s search software lets people search the entire World Wide Web, Google is offering the enterprise search, which is essentially the same technology in a version that searches a corporate intranet. The enterprise search considers not only how often a particular document has been viewed but also the history of the user’s past search patterns, such as how often that user has looked at particular documents and for how long. In addition, other companies have purchased specialized software, such as Autonomy, which searches internal sources plus such external sources as news and government websites.15

External Data: The Distribution System External data are generated or recorded by an entity other than the researcher’s organization. The government, newspapers and journals, trade associations, and other organizations create or produce information. Traditionally, this information has been in published form, perhaps available from a public library, trade association, or government agency. Today, however, computerized data archives and electronic data interchange make external data as accessible as internal data. Exhibit 8.7 illus- trates some traditional and some modern ways of distributing information.

Information As a Product and Its Distribution Channels Because secondary data have value, they can be bought and sold like other products. And just as bottles of perfume or plumbers’ wrenches may be distributed in many ways, secondary data also flow through various channels of distribution. Many users, such as Fortune 500 corporations, purchase documents and computerized census data directly from the government. However, many small companies get census data from a library or another intermediary or vendor of secondary information.

Libraries Traditionally, libraries’ vast storehouses of information have served as a bridge between users and producers of secondary data. The library staff deals directly with the creators of information, such as the federal government, and intermediate distributors of information, such as abstracting and indexing services. The user need only locate the appropriate secondary data on the library shelves. Libraries provide collections of books, journals, newspapers, and so on for reading and reference. They also stock many bibliographies, abstracts, guides, directories, and indexes, as well as offer access to basic databases.

external data

Data created, recorded, or generated by an entity other than the researcher’s organization.

CHAPTER 8 Secondary Data Research in a Digital Age • 171

The word library typically connotes a public or university facility. However, many major cor- porations and government agencies also have libraries. A corporate librarian’s advice on sources of industry information or the United Nations librarian’s help in finding statistics about international markets can be invaluable.

EXHIBIT 8.7

Information as a Product and Its Distribution Channels

Information producer (Federal government)

Company user

Library (Storage of government

documents and books)

Information producer (Federal government)

Company user

Indirect Channel Using Intermediary

Direct Channel

Information producer A (Federal government–

census data)

Information producer B (Grocery store–

retail scanner data)

Information producer C (Audience research company–

television viewing data)

Vendor/external distributor

(Computerized database integrating all three data sources for any

geographic area)

Company user

Modern Distribution of Secondary Data

Information producers (Just-in-time inventory partner)

computerized database

Company user

Direct, Computerized Distribution

Indirect Computerized Distribution Using an Intermediary

Traditional Distribution of Secondary Data

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172 • PART TWO Beginning Stages of the Research Process

The Internet Today, of course, much secondary data is conveniently available over the Internet. Its creation has added an international dimension to the acquisition of secondary data. For example, Library Spot, at http://www.libraryspot.com, provides links to online libraries, including law libraries, medical libraries, and music libraries. Its reference desk features links to calendars, dictionaries, encyclope- dias, maps, and other sources typically found at a traditional library’s reference desk.

Exhibit 8.8 lists some of the more popular Internet addresses where secondary data may be found.

EXHIBIT 8.8

Selected Internet Sites for Secondary Data

Name Description URL

Yahoo! Portal that serves as a gateway to all kinds of sites on the web.

http://www.yahoo.com

CEOexpress The 80/20 rule applied to the Internet. A series of links designed by a busy executive for busy executives.

http://www.ceoexpress.com

The New York Public Library Home Page

Library resources and links available online.

http://www.nypl.org

Census Bureau Demographic information from the U.S. Census Bureau

http://www.census.gov

Statistical Abstract of the United States

Highlights from the primary reference book for government statistics.

http://www.census.gov/ statab/www

FedWorld Central access gateway to United States government information

http://www.fedworld.gov

Advertising Age magazine Provides content on marketing media, advertising, and public relations.

http://www.adage.com

Inc.com Inc. magazine’s resources for growing a small business.

http://www.inc.com

The Wall Street Journal Online Provides a continually updated view of business news around the world.

http://online.wsj.com

CNN Money Provides business news, information on managing a business and managing money, and other business data.

http://money.cnn.com

NAICS—North American Industry Classification System

Describes the new classification system that replaced the SIC system.

http://www.census.gov/ epcd/www/naics.html

MapQuest Allows users to enter an address and zip code and see a map.

http://www.mapquest.com

Brint.com: The BizTech Network

Business and technology portal and global network for e-business, information, technology, and knowledge management.

http://www.brint.com

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CHAPTER 8 Secondary Data Research in a Digital Age • 173

Vendors The information age offers many channels besides libraries through which to access data. Many external producers make secondary data available directly from the organizations that produce the data or through intermediaries, which are often called vendors. Vendors such as Factiva now allow managers to access thousands of external databases via desktop computers and telecommunications systems. Hoover’s (http://www.hoovers.com) specializes in providing information about thousands of companies’ financial situations and operations.

Producers Classifying external secondary data by the nature of the producer of information yields five basic sources: publishers of books and periodicals, government sources, media sources, trade association sources, and commercial sources. The following section discusses each type of secondary data source.

BOOKS AND PERIODICALS Some researchers consider books and periodicals found in a library to be the quintessential second- ary data source. A researcher who finds books on a topic of interest obviously is off to a good start.

Professional journals, such as the Journal of Marketing, Journal of Management, Journal of the Academy of Marketing Science, The Journal of Business Research, Journal of Advertising Research, American Demographics, and The Public Opinion Quarterly, as well as commercial business peri- odicals such as the Wall Street Journal, Fortune, and BusinessWeek, contain much useful material. Sales and Marketing Management’s Survey of Buying Power is a particularly useful source of infor- mation about markets. To locate data in periodicals, indexing services such as the ABI/INFORM, Business Periodicals Index, and Wall Street Journal Index are very useful. Guides to data sources also are helpful. For example, the American Statistical Index and Business Information Sources are very valuable sources. Most university libraries provide access to at least some of these databases.

GOVERNMENT SOURCES Government agencies produce data prolifically. Most of the data published by the federal govern- ment can be counted on for accuracy and quality of investigation. Most students are familiar with the U.S. Census of Population, which provides a wealth of data.

The Census of Population is only one of many resources that the government provides. Banks and savings and loan companies rely heavily on the Federal Reserve Bulletin and the Economic Report of the President for data relating to research on financial and economic conditions. Builders and contractors use the information in the Current Housing Reports and American Housing Sur- vey for their research. The Statistical Abstract of the United States is an extremely valuable source of information about the social, political, and economic organizations of the United States. It abstracts data available in hundreds of other government publications and serves as a convenient reference to more specific statistical data. FedWorld (http://www.fedworld.gov) provides a central access gateway to government information. FedWorld is administered by the National Technical Information Ser- vice (NTIS) and manages a comprehensive inventory of federal government information.

State, county, and local government agencies can also be useful sources of information. Many state governments publish state economic models and forecasts, and many cities have metropolitan planning agencies that provide data about the population, economy, transportation system, and so on. These are similar to federal government data but are more current and are structured to suit local needs.

Many cities and states publish information on the Internet. Many search engines have directory entries that allow easy navigation to a particular state’s website. A researcher using Yahoo!, for exam- ple, needs only to click Regional Information to find numerous paths to information about states.

MEDIA SOURCES Information on a broad range of subjects is available from broadcast and print media. CNN Financial News and BusinessWeek are valuable sources for information on the economy and many industries. Media frequently commission research studies about various aspects of Americans’ lives, such as financial affairs, and make reports of survey findings available to potential advertisers free of charge.

‘‘The man who does not read good books has no advantage over the man who cannot read them.’’—MARK TwAIN

174 • PART TWO Beginning Stages of the Research Process

175

Data about the readers of magazines and the audiences for broadcast media typically are profiled in media kits and advertisements.

Information about special-interest topics may also be available. Hispanic Business reports that the number of Hispanic-owned companies in the United States is expected to grow at a rate of 55 percent between 2004 and 2010, reaching 3.2 million firms, with revenue growth for the period of 70 percent. According to the magazine, most of these firms are located in 20 states, with over half in California and Florida. For researchers willing to pay a modest $85, Hispanic Business offers a more detailed report about Hispanic-owned businesses.16

Data such as these are plentiful because the media like to show that their vehicles are viewed or heard by advertisers’ target markets. These types of data should be evaluated carefully, however, because often they cover only limited aspects of a topic. Nevertheless, they can be quite valuable for research, and they are generally available free of charge.

TRADE ASSOCIATION SOURCES Trade associations, such as the Food Marketing Institute or the American Petroleum Institute, serve the information needs of a particular industry. The trade association collects data on a number of topics of specific interest to firms, especially data on market size and market trends. Association members have a source of information that is particularly germane to their industry questions. For example, the Newspaper Advertising Bureau (NAB) has catalogued and listed in its computer the specialized sections that are currently popular in newspapers. The NAB has surveyed all daily, Sunday, and weekend newspapers in the United States and Canada on their editorial content and has stored this information, along with data on rates, circulation, and mechanical requirements, in its computer for advertisers’ use.

COMMERCIAL SOURCES Numerous firms specialize in selling and/or publishing information. For example, the Polk Com- pany publishes information on the automotive field, such as average car values and new-car pur- chase rates by zip code. Many of these organizations offer information in published formats and as CD-ROM or Internet databases. The following discussion of several of these firms provides a sampling of the diverse data that are available.

Market Share Data. A number of syndicated services supply either wholesale or retail sales volume data based on product movement. Information Resources, Inc., collects market share data using Universal Product Codes (UPC) and optical scanning at retail store checkouts. INFOSCAN

RESEARCH SNAPSHOT

Bees aren’t the only creatures that buzz. Consumers do too and more and more they create that buzz online. Just think about it, the Internet is filled with billions of consumer conversations. Obviously, these billions of data points contain a lot of useful information. But, a lot of it is useless too. How can a firm make sense of this? One solution: data-mining software designed for the blogosphere.

Buzzmetrics, a part of Nielsen Online, serves firms by monitoring Internet conversations, letting firms know whether conversation about their brand is up or down on any given time period. want to know if a Super Bowl ad had any impact? The buzz an ad creates from the time it becomes public is a good indicator. If there is no buzz, there was probably not much sizzle in terms of market effectiveness. Is Dancing with the Stars still popular? If people aren’t talking about it online then that show

too may be losing its sizzle. For large brands, companies like Buzzmetrics monitor thousands of websites for brand mentions and whether those mentions are positive or negative. Thus, sec- ondary data can provide a buzz that can come with or without a sting based on whether the conversations spread good or bad news about the brand.

Sources: Hargrave, S., “Ears to the Ground,” New Media Age, (January 17, 2008), 21. Notarantonio, E. M., “The Effectiveness of a Buzz Marketing Approach Compared to Traditional Advertising: An Exploration,”Journal of Promotion Management, 15 (Oct–Dec 2009), 455–464. Alahnah, M. and D. Khazanchi, “The Importance of Buzz,” Marketing Research, 22 (Summer 2010), 20–25.

What’s That Buzzing Sound?

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is a syndicated store tracking service that collects scanner data weekly from more than 32,000 supermarket, drug, and mass merchandiser outlets across the United States. Sales in France, Germany, Greece, Italy, the Netherlands, Spain, and the United Kingdom also are tracked by INFOSCAN.

Although it is best known for its television rating operations, Nielsen also has a scanner-based marketing and sales information service called ScanTrack. This service gathers sales and marketing data from a sample of more than 4,800 stores representing more than 800 retailers in 50 major U.S. markets. As part of Nielsen’s Retail Measurement Service, auditors visit the stores at regular inter- vals to track promotions to customers, retail inventories, displays, brand distribution, out-of-stock conditions, and other retail activity. Scanner data allow researchers to monitor sales data before, during, and after changes in advertising frequency, price changes, distribution of free samples, and similar marketing tactics.

Walmart operates its own in-store scanner system called RetailLink. Key suppliers can have online access to relevant data free of charge.17 The Market Share Reporter is produced each year, made available for sale, and provides market share data for most industries.

Many primary data investigations use scanner data to measure the results of experimental manipulations such as altering advertising copy. For example, scanning systems combined with consumer panels are used to create electronic test-markets. Systems based on UPCs (bar codes) and similar technology have been implemented in factories, warehouses, and transportation companies to research inventory levels, shipments, and the like.

Demographic and Census Updates. A number of firms, such as CACI Marketing Systems and Urban Information Systems, offer computerized U.S. census files and updates of these data broken down by small geographic areas, such as zip codes. Many of these research suppliers provide in-depth information on minority customers and other market segments.

Consumer Attitude and Public Opinion Research. Many research firms offer specialized syn- dicated services that report findings from attitude research and opinion polls. For example, Yankelovich provides custom research, tailored for specific projects, and several syndicated services. Yankelovich’s public opinion research studies, such as the voter and public attitude surveys that appear in Time and other news magazines, are a source of secondary data. One of the firm’s services is the Yankelovich MONITOR, a syndicated annual census of changing social values and an analysis of how they can affect consumer marketing. The MONITOR charts the growth and spread of new social values, characterizes the types of customers who support the new values and those who continue to support traditional values, and outlines the ways in which people’s values affect purchasing behavior.

Harris Interactive is another public opinion research firm that provides syndicated and custom research for business. One of its services is its ABC News/Harris survey. This survey, released three times per week, monitors the pulse of the American public on topics such as inflation, unemploy- ment, energy, attitudes toward the president, elections, and so on.

Consumption and Purchase Behavior Data. NPD’s National Eating Trends (NET) is the most detailed database available on consumption patterns and trends for more than 4,000 food and beverage products. This is a syndicated source of data about the types of meals people eat and when and how they eat them. The data, called diary panel data, are based on records of meals and diaries kept by a group of households that have agreed to record their consumption behavior over an extended period of time.

National Family Opinion (NFO), Marketing Research Corporation of America (MRCA), and many other syndicated sources sell diary panel data about consumption and purchase behav- ior. Since the advent of scanner data, diary panels are more commonly used to record purchases of apparel, hardware, home furnishings, jewelry, and other durable goods, rather than purchases of nondurable consumer packaged goods. More recently, services have been tracking consumer behavior online, collecting data about sites visited and purchases made over the Internet.

Advertising Research. Advertisers can purchase readership and audience data from a number of firms. W. R. Simmons and Associates measures magazine audiences; Arbitron measures radio audiences; and Nielsen Media Measurement estimates television audience ratings. By specializing in collecting and selling audience information on a continuing basis, these commercial sources provide a valuable service to their subscribers.

176 • PART TWO Beginning Stages of the Research Process

Other syndicated services provide assistance in measuring magazine readership and magazine advertising effectiveness. Affinity’s American Magazine Study (AMS) provides insight into maga- zine readership and reader profiles. Simmons National Consumer Study (NCS) assesses consumer attitudes, behaviors, and brand preferences. Combined, these organizations have created Affinity AMS Experian Simmons to provide a complete data base regarding magazine readership.

Burke Marketing Research provides a service that measures the extent to which respondents recall television commercials aired the night before. It provides product category norms, or average DAR (day-after recall) scores, and DAR scores for other products.

An individual advertiser would be unable to monitor every minute of every television pro- gram before deciding on the appropriate ones in which to place advertising. However, numerous clients, agencies, television networks, and advertisers can purchase the Nielsen television ratings service.

Single-Source Data-Integrated Information The Nielsen Company offers data from both its television meters and scanner operations. The inte- gration of these two types of data helps marketers investigate the impact of television advertising on retail sales. In other ways as well, users of data find that merging two or more diverse types of data into a single database offers many advantages.

PRIZM by Nielsen Claritas and other syndicated databases report product purchase behavior, media usage, demographic characteristics, lifestyle variables, and business activity by geographic area such as zip code. Although such data are often called geodemographic, they cover such a broad range of phenomena that no one name is a good description. These data use small geographic areas as the unit of analysis.

The data and information industry uses the term single-source data for diverse types of data offered by a single company. Exhibit 8.9 identifies three major marketers of single-source data.

single-source data

Diverse types of data offered by a single company; usually integrated on the basis of a common variable such as geo- graphic area or store.

EXHIBIT 8.9

Examples of Single-Source Databases

CACI Marketing Systems http://www.caci.co.uk

Provides industry-specific marketing services, such as customer profiling and segmentation, custom target analysis, emographic data reports and maps, and site evaluation and selection. CACI offers demographics and data on businesses, lifestyles, consumer spending, purchase potential, shopping centers, traffic volumes, and other statistics.

PRIZM by Nielsen Claritas http://www.claritas.com/ MyBestSegments/Default.jsp

PRIZM, which stands for Potential Rating Index for Zip Markets, is based on the “birds-of-a-feather” assumption that people live near others who are like themselves. PRIZM combines census data, consumer surveys about shopping and lifestyle, and purchase data to identify market segments. Colorful names such as Young Suburbia, Shot Guns, and Pickups describe 40 segments that can be identified by zip code.

GfK MRI Survey of the American Consumer™ http://www.gfkmri.com/

Provides “the most reliable and comprehensive consumer marketing and media database available.” Research covers media choices, demographics, lifestyles and attitudes, and product usage of almost 6,000 products.

Sources for Global Research As business has become more global, so has the secondary data industry. The Japan Management Association Research Institute, Japan’s largest provider of secondary research data to government and industry, maintains an office in San Diego. The Institute’s goal is to help U.S. firms access its enormous store of data about Japan to develop and plan their business there. The office in

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CHAPTER 8 Secondary Data Research in a Digital Age • 177

178

San Diego provides translators and acts as an intermediary between Japanese researchers and U.S. clients.

Secondary data compiled outside the United States have the same limitations as domestic secondary data. However, international researchers should watch for certain pitfalls that frequently are associated with foreign data and cross-cultural research. First, data may simply be unavailable in certain countries. Second, the accuracy of some data may be called into question. This is especially likely with official statistics that may be adjusted for the political purposes of foreign governments. Finally, although economic terminology may be standardized, various countries use different defi- nitions and accounting and recording practices for many economic concepts. For example, differ- ent countries may measure disposable personal income in radically different ways. International researchers should take extra care to investigate the comparability of data among countries. The Research Snapshot “Around the World of Data” provides some of the many website locations for data from around the world.

The U.S. government and other organizations compile databases that may aid international secondary data needs. For example, the website for the delegation of the European Union to the United States (http://www.eurunion.org) reports on historical and current activity in the European Union providing a comprehensive reference guide to information about laws and regulations. It profiles in detail each European Union member state, investment opportunities, sources of grants and other funding, and other information about business resources.

The U.S. government offers a wealth of data about foreign countries. The CIA’s World Factbook and the National Trade Data Bank (NTDB) are especially useful. Both can be accessed using the Internet. The NTDB, the U.S. government’s most comprehensive source of world trade data, illustrates what is available.

The NTDB was established by the Omnibus Trade and Competitiveness Act of 1988.18 Its purpose was to provide “reasonable public access, including electronic access” to an export promo- tion data system that was centralized, inexpensive, and easy to use.

The U.S. Department of Commerce has the responsibility for operating and maintaining the NTDB and works with federal agencies that collect and distribute trade information to keep the NTDB up-to-date. The NTDB has been published monthly on CD-ROM since 1990. Over one thousand public and university libraries offer access to the NTDB through the Federal Depository Library Program.

The NTDB consists of 133 separate trade- and business-related programs (databases). By using it, small- and medium-sized companies get immediate access to information that until now only Fortune 500 companies could afford.

with the Internet, we can quickly go around the world and find data. Many countries have websites that summarize basic characteristics with data tables. Here are just a few of the many websites that make finding data about different parts of the world easier:

■ United States http://www.fedworld.gov

■ South Africa http://www.statssa.gov.za

■ Australia http://www.nla.gov.au/oz/stats.html

■ Japan http://www.stat.go.jp

■ U.K. http://www.statistics.gov.uk

■ France http://www.insee.fr

■ South America http://www.internetworldstats.com/south.htm

■ Norway http://www.ssb.no

■ United Nations http://www.un.org/esa

Around the World of Data

RESEARCH SNAPSHOT

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EXHIBIT 8.10

Examples of Information Contained in the NTDB

Agricultural commodity production and trade

Basic export information

Calendars of trade fairs and exhibitions

Capital markets and export financing

Country reports on economic and social policies and trade practices

Energy production, supply, and inventories

Exchange rates

Export licensing information

Guides to doing business in foreign countries

International trade terms directory

How-to guides

International trade regulations/agreements

International trade agreements

Labor, employment, and productivity

Maritime and shipping information

Market research reports

Overseas contacts

Overseas and domestic industry information

Price indexes

Small business information

State exports

State trade contacts

Trade opportunities

U.S. export regulations

U.S. import and export statistics by country and commodity

U.S. international transactions

World Factbook

World minerals production

Topics in the NTDB include export opportunities by industry, country, and product; foreign companies or importers looking for specific products; how-to market guides; demographic, political, and socioeconomic conditions in hundreds of countries; and much more. NTDB offers one-stop shopping for trade information from more than 20 federal sources. You do not need to know which federal agency produces the information: All you need to do is consult NTDB.

Some of the specific information that can be obtained from the NTDB is listed in Exhibit 8.10.

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CHAPTER 8 Secondary Data Research in a Digital Age • 179

SUMMARY

1. Discuss the advantages and disadvantages of secondary data. Secondary data are data that have been gathered and recorded previously by someone else for purposes other than those of the current researcher. The chief advantage of secondary data is that they are almost always less expensive to obtain than primary data. Generally they can be obtained rapidly and may provide information not otherwise available to the researcher. The disadvantage of secondary data is that they were not intended specifically to meet the researcher’s needs. The researcher must examine secondary data for accuracy, bias, and soundness. One way to do this is to cross-check various available sources.

2. Define types of secondary data analysis conducted by business research managers. Secondary research designs address many common business research problems. There are three general categories of secondary research objectives: fact-finding, model building, and database marketing. A typical fact-finding study might seek to uncover all available information about consumption patterns for a particular product category or to identify business trends that affect an industry. Model building is more complicated; it involves specifying relationships between two or more variables. The practice of database marketing, which involves maintaining customer databases with customers’ names, addresses, phone numbers, past purchases, responses to past promotional offers, and other relevant data such as demographic and financial data, is increasingly being supported by business research efforts.

3. Identify various internal and proprietary sources of secondary data. Managers often get data from internal proprietary sources such as accounting records. Data mining is the use of powerful computers to dig through volumes of data to discover patterns about an orga- nization’s customers and products. It is a broad term that applies to many different forms of analysis.

4. Give examples of various external sources of secondary data. External data are generated or recorded by another entity. The government, newspaper and journal publishers, trade asso- ciations, and other organizations create or produce information. Traditionally this information has been distributed in published form, either directly from producer to researcher, or indirectly through intermediaries such as public libraries. Modern computerized data archives, electronic data interchange, and the Internet have changed the distribution of external data, making them almost as accessible as internal data. Push technology is a term referring to an Internet informa- tion technology that automatically delivers content to the researcher’s or manager’s desktop. This service helps in environmental scanning.

5. Describe the impact of single-source data and globalization on secondary data research. The marketing of multiple types of related data by single-source suppliers has radically changed the nature of secondary data research. Businesses can measure promotional efforts and related buyer behavior by detailed customer characteristics. As business has become more global, so has the secondary data industry. International researchers should watch for pitfalls that can be associated with foreign data and cross-cultural research, such as problems with the availability and reliability of data.

KEY TERMS AND CONCEPTS cross-checks, 162 customer discovery, 169 data conversion, 161 data mining, 169 database marketing, 170

external data, 171 index of retail saturation, 168 internal and proprietary data, 170 market tracking, 164 market-basket analysis, 169

model building, 165 neural networks, 169 secondary data, 160 single-source data, 177 site analysis techniques, 168

180 Part 2: Beginning Stages of the Research Process

RESEARCH ACTIVITIES 1. Use secondary data to learn the size of the U.S. golf market and

to profile the typical golfer. 2. ’NET Where could a researcher working for the U.S. Marine

Corps (http://www.marines.com) find information that would identify the most productive areas of the United States in which to recruit? What would you recommend?

3. ’NET POPClocks estimate the U.S. and world populations. Go to the Census Bureau home page (http://www.census.gov), navigate to the population section, and find today’s estimate of the U.S. and world populations.

4. ’NET Try to find the U.S. market share for the following com- panies within 30 minutes: a. Home Depot b. Burger King c. Marlboro d. Was this a difficult task? If so, why do you think it is this

difficult? 5. ’NET Use the Internet to learn what you can about Indonesia.

a. Check the corruption index for Indonesia at http://www. transparency.org.

b. What additional kinds of information are available from the following sources?

• Go to http://freetheworld.com/member.html and view info for Indonesia.

• Visit the CIA’s World Factbook at https://www.cia.gov/ library/publications/the-world-factbook/index.html.

• Go to Google, Yahoo! Search, or another search engine, and use “Indonesia” as a search word.

6. ’NET Go to Statistics Norway at http://www.ssb.no. What data, if any, can you obtain in English? What languages can be used to search this website? What databases might be of interest to the business researcher?

7. ’NET Go to Statistics Canada at http://www.statcan.gc.ca. What languages can be used to search this website? What data- bases might be of interest to the business researcher?

8. ’NET Suppose you were working for a company that wanted to start a business selling handmade acoustic guitars that are reproductions of classic vintage guitars. Pricing is a big part of the decision. Secondary information is available via the Inter- net. Use eBay (http://ebay.com) to identify four key brands of acoustic guitars by studying the vintage acoustic guitars listed for sale. Since the company wishes to charge premium prices, they will model after the most expensive brand. What brand seems to be associated with the highest prices?

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Secondary data have been called the first line of attack for busi-

ness researchers. Discuss this description. 2. Suppose you wish to learn about the size of the soft-drink mar-

ket, particularly root beer sales, growth patterns, and market shares. Indicate probable sources for these secondary data.

3. What is push technology? 4. Identify some typical research objectives for secondary data studies. 5. How might a researcher doing a job for a company such as Pulte

Homes (http://www.pultehomes.com) or David Weekley Homes (http://www.davidweekley.com) use secondary data and data mining?

6. What would be a source for the following data? a. Population, average income, and employment rates for

Oregon b. Maps of U.S. counties and cities c. Trends in automobile ownership d. Divorce trends in the United States e. Median weekly earnings of full-time, salaried workers for

the previous five years f. Annual sales of the top 10 fast-food companies g. Top 10 websites ranked by number of unique visitors h. Attendance at professional sports events

7. Suppose you are a business research consultant and a client comes to your office and says, “I must have the latest informa- tion on the supply of and demand for Maine potatoes within the next 24 hours.” What would you do?

8. Find the following data in the Survey of Current Business: a. U.S. gross domestic product for the second quarter of 2008

and the first quarter of 2011 b. Exports of goods and services for the second quarter of 2008

and the first quarter of 2011 c. Imports of goods and services for the second quarter of 2008

and the first quarter of 2011 9. ETHICS A newspaper reporter finds data in a study that sur-

veyed children that reports a high percentage of children can match cartoon characters with the products they represent. For instance, they can match cereal with Captain Crunch and Ronald McDonald with a Big Mac. The reporter used this to write a story about the need to place limits on the use of cartoon characters. However, the study also provided data suggesting that matching the cartoon character and the product did not lead to significantly higher consumption. Would this be a proper use of secondary data?

Chapter 8: Secondary Data Research in a Digital Age 181

Demand for Gas Guzzlers In fall 2005, Hurricanes Katrina and Rita churning in the Gulf of Mexico damaged oil rigs and refineries, contributing to a spike in oil prices. Many observ-

ers expressed confidence that those events were the long-expected trigger that would kill off demand for SUVs and other gas-guzzling vehicles.19 They were only partly right.

In the months leading up to the hurricanes, sales of SUVs had already been falling, according to data from Automotive News. Auto- makers had been shifting ad dollars away from these products. CNW Market Research said that in August 2005, consumers had for the first time placed fuel economy ahead of performance when rank- ing factors for choosing a new vehicle. When gas prices approached three dollars a gallon in September 2005, marketers felt sure that fuel economy would remain a top concern. Advertisers began creating more ads featuring vehicles’ gas mileage.

But by the end of the year, attitudes were shifting again. The National Automobile Dealers Association surveyed consumers visiting its website for information about car purchases, and it

learned they ranked price as most important, followed by make and model, then performance. Fuel economy ranked last, with 3 percent considering it most important and 11 percent consider- ing it least important. What’s a carmaker to do? General Motors gathers data from the shoppers who visit websites such as http:// www.kbb.com to look up information, and it is analyzing the data to identify the price of fuel at which car buyers adjust their priorities.

Questions 1. From the standpoint of an automobile company, what sources

of information in this article offer secondary data? 2. Suggest two or three other sources of data that might be of

interest to auto companies interested in forecasting demand. 3. Online or at your library, look for information about recent

trends in SUV purchases. Report what you learned, and fore- cast whether SUV sales are likely to recover or continue their decline. What role do gas prices play in your forecast?

CASE 8.1

182 Part 2: Beginning Stages of the Research Process

P a r t t H r E E

Research Methods for Collecting Primary Data

CHAPTER 9 Survey Research: An Overview

CHAPTER 10 Survey Research: Communicating with Respondents

CHAPTER 11 Observation Methods

CHAPTER 12 Experimental Research

184

After studying this chapter, you should be able to

1. Define surveys and explain their advantages

2. Describe the type of information that may be gathered in a survey

3. Identify sources of error in survey research

4. Distinguish among the various categories of surveys

5. Discuss the importance of survey research to total quality management programs

C H A P T E R

W hat’s next in the world of electronic com- munication? In just a few years, we have seen cellular phones move from simple devices to talk with someone to smart-

phones like Apple’s iPhone, which serve as portable music and video players with the ability to directly download music and movies, as well as watch television. We keep our calendars and all our contact information in our smartphones. During this same time, our in-home phones have advanced little. That is about to change, according to research conducted by In-Stat (http://www.in-stat.com). In-Stat claims to be “the leading provider of actionable research, market analysis and forecasts of advanced communications services, infrastructure, end-user devices and semiconductors.”

While you may have never heard the term media phone, you could very well have one in your home in the next four years:

The media phone represents a new category of broadband multimedia device that has the potential to become the fourth screen in the home, complementing the PC, TV, and mobile handset. The media phone delivers direct access to Internet-based entertainment and applications using a large

(6–10 inch) color touchscreen display with a high-quality speakerphone. The media phone c ombines the power of a PC with the always-on functionality of the home telephone. Most units are configured with one or more cordless DECT voice handsets, suitable for any room in the home.

In-Stat research has found that the most popular consumer Internet activities include viewing online information ser- vices (news updates, weather forecasts, recipes, directory

LEARNING OUTCOMES

Survey Research An Overview

Media Phones—The Next Wave of Communication Technology?

9 Chapter Vignette:

Touch Revolution’s NIMble platform media phone powered by Android.

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Introduction The purpose of survey research is to collect primary data—data gathered and assembled specifically for the project at hand. This chapter, the first of two on survey research, defines the subject. It also discusses typical research objectives that may be accomplished with surveys and various advantages of the survey method. The chapter explains many potential errors that researchers must be careful to avoid. Finally, it classifies the various survey research methods.

Often research entails asking people—called respondents—to provide answers to written or spoken questions. These interviews or questionnaires collect data through the mail, on the telephone, online, or face-to-face. Thus, a survey is defined as a method of collecting primary data based on communication with a representative sample of individuals. Surveys provide a snapshot at a given point in time. The more formal term, sample survey, emphasizes that the purpose of contacting respondents is to obtain a representative sample, or subset, of the target population.

Using Surveys The type of information gathered in a survey varies considerably depending on its objectives. Typi- cally, surveys attempt to describe what is happening or to learn the reasons for a particular business activity.

Identifying characteristics of target markets, measuring customer attitudes, and describing consumer purchase patterns are all common business research objectives. Most business surveys have multiple objectives; few gather only a single type of factual information. In the opening vignette, In-Stat asked questions about product use and desirable features that can help with product development and advertising messages. Geographic, demographic, and media exposure information were also collected to help plan a market segmentation strategy. A study commissioned by eBay provides another example of the information that can be gleaned from survey research. eBay learned that almost 60 percent of respondents receive unwanted gifts, and 15 percent of them had sold an unwanted gift online, suggesting a possible source of demand for eBay’s auction

respondents

People who verbally answer an interviewer’s questions or provide answers to written questions.

sample survey

A more formal term for a survey.

searches), and accessing entertainment content (YouTube videos, movie clips, and music). Media phones offer always- on, one-touch access to this popular media content. Other applications, such as a digital picture frame, TV electronic program guide, local purchases (e.g., pizza or movie tickets) and the integration of mobile features (e.g., visual voice mail, network-based address book, and location-based services) will help make the media phone an indispensable part of every broadband household. [http://www.instat .com/ promos/09/dl/media_phone_3ufewaCr.pdf]

In-Stat has conducted extensive survey research to learn consumer perceptions, attitudes, and desires regarding media phones. The company estimates the consumer market for media phones could reach nearly 50 million units and $8 billion in worldwide revenue by 2013. In addition to identifying the primary uses of the media phone noted above, In-Stat research determined the characteristics of the product customers desired

across consumer age groups and geographic locations. For example, U.K. customers place much greater importance on the television component of the media phone than French custom- ers. Further, the research shows (perhaps not surprisingly) that younger customers have a much higher level of interest in the media phone than older customers. Younger customers also planned on putting the media phone in their living room or bedroom, while older customers would place it in their home office. Older customers also show a strong preference for charging their media phone handsets in a cradle, while younger customers want to plug theirs in like a cell phone.

In-Stat’s survey research obviously has significant benefits for an electronics manufacturer. Not only will this research help with product design and development, but with produc- tion planning, pricing, promotion, and distribution. An invest- ment in this research upfront can save millions of dollars down the road. In a few years, we may all have a media phone in our home!1

CHAPTER 9 Survey Research: An Overview • 185

186

services.2 In addition, the survey indicated that selling unwanted gifts online was twice as com- mon among 25- to 34-year-olds. Although consumer surveys are a common form of business research, not all survey research is conducted with the ultimate consumer. Frequently, studies focus on wholesalers, retailers, industrial buyers, or within the organization itself. For example, a survey could be used to determine an organization’s commitment to the environment. Also, measuring employee job satisfaction and describing the risk aversion of financial investors may be important survey objectives.

Because most survey research is descriptive research, the term survey is most often associated with quantitative findings. Although most surveys are conducted to quantify certain factual infor- mation, some aspects of surveys may also be qualitative. In new product development, a survey often has a qualitative objective of refining product concepts. Stylistic, aesthetic, or functional changes may be made on the basis of respondents’ suggestions. In our example of an organization’s environmental attitudes, a survey might be used to gather qualitative information regarding activi- ties that could make the company more “green.”

Evaluating the nature of advertising may also be an objective of survey research, as in the following story told to advertiser Michael Arlen about testing a rough commercial for AT&T:

We called it “Fishing Camp.” The idea was this: These guys go off to a fishing camp in the north woods, somewhere far away, where they’re going to have a terrific time together and do all this great fishing, only what happens is that it rains all the time and the fishing is a bust. Mind you, this was a humorous ad. The emphasis was on the humor. Anyway, the big moment occurs when the fishing guys are talking on the phone to their jealous friends back home—who naturally want to know how great the fishing is—and what you see are the fishing guys, huddled in this cabin, with the rain pouring down outside, and one of the guys is staring at a frying pan full of hamburgers sizzling on the stove while he says into the phone, “Boy, you should see the great trout we’ve got cooking here.”3

However, much to the advertisers’ astonishment, when they tested the advertisement and gave subjects a questionnaire, respondents recalled that what was cooking was trout. To counteract this misimpression, said the advertiser, “We ended up making it, but what we had to do was, when we came to that segment, we put the camera almost inside the frying pan, and in the frying pan we put huge, crude chunks of hamburger that were so raw they were almost red.”

Advantages of Surveys Surveys provide a quick, inexpensive, efficient, and accurate means of assessing information about a population. The examples given earlier illustrate that surveys are quite flexible and, when properly conducted, extremely valuable to the manager.

SURVEY

THIS! This chapter introduces survey research, discussing several different types of error and bias that may be present in survey research. Review the online survey designed for the busi- ness research course we are using for this class, and then address the following questions:

1. Can you find areas within the survey or sur- vey process that may result in error or bias? Identify at least three different sources of potential error or bias and offer suggestions on how this error can be reduced.

2. How would you classify this survey on the structured, disguised, and temporal dimensions? 3. How could this survey help your academic institution implement a total quality management program?

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187

As we discussed in Chapter 1, business research has proliferated in recent years. The growth of survey research is related to the simple idea that to find out what someone thinks, you need to ask them.4

Over the last 50 years and particularly during the last two decades, survey research techniques and standards have become quite scientific and accurate. When properly conducted, surveys offer managers many advantages. However, they can also be used poorly when researchers do not follow research principles, such as careful survey and sample design. Sometimes even a well-designed and carefully executed survey is not helpful because the results are delivered too late to inform decisions.

The disadvantages of specific forms of survey data collection—personal interview, telephone, mail, Internet, and other self-administered formats—are discussed in Chapter 10. However, errors are common to all forms of surveys, so it is appropriate to describe them generally.

Errors in Survey Research A manager who is evaluating the quality of a survey must estimate its accuracy. Exhibit 9.1 outlines the various forms of survey error. There are two major sources: random sampling error and sys- tematic error.

RESEARCH SNaPSHOt

Intuit, maker of Quicken, QuickBooks, and Turbo Tax soft- ware for accounting and tax preparation, has enjoyed years of growth and profits, thanks in part to its efforts to learn what customers want. One of its most important marketing research tools is called a “net promoter survey.” That survey is extremely simple. Researchers simply ask customers, “On a scale of 0 to 10 [with 10 being most likely], how likely is it that you would recommend our product to your friends or colleagues?” Customers who respond with a 9 or 10 are called “promot- ers,” and customers who respond with 0 through 6 are called “detractors.” Subtracting the percentage of respondents who are detractors from the percentage who are promoters yields the net promoter score.

Intuit’s CEO, Steve Bennett—who says he believes that “anything that can be measured can be improved”— encourages the ongoing collection of net promoter scores as a way to improve products and customer service and thereby build revenues and profits. Of course, making improvements requires that the company not only know whether customers are satisfied or dissatisfied but also know why. To learn more, the company asks survey respondents who are promoters to go online and provide more detailed opinions. For example, Intuit learned that claiming rebates was an annoying process (the company has simplified it) and that discount stores were offering some products for less than the prices offered online to frequent buyers (the company plans to adjust prices).

For even more in-depth information, Intuit supplements survey research with direct observation of customers. One year the company sent hundreds of employees, including CEO Bennett, to visit customers as they worked at their computers.

The observers learned that a significant number of small- business owners were struggling with the accounting know- how they needed to use QuickBooks and were mystified by terms such as accounts payable and accounts receivable. In response, the company introduced QuickBooks: Simple Start Edition, which replaces the financial jargon with simple terms like cash in and cash out. In the first year after its launch, Simple Start Edition sold more copies than any other accounting software except the standard QuickBooks.

This Research Snapshot not only illustrates Intuit’s reliance on survey research to enhance products and monitor customer satisfaction and loyalty, but also shows the close relationship between qualitative and quantitative research. As we discussed in Chapter 7, qualitative research is often used in exploratory business research to set the stage for quantitative research, such as surveys. Qualitative research can also be used to pro- vide richer information, to bring the quantitative research numbers to life. Intuit recognizes the value of both research approaches.

Sources: Darlin, Damon, “The Only Question That Matters,” Business 2.0 (September 2005), http://money.cnn.com/magazines/business2/business2_ archive/2005/09/01/8356514/index.htm; Kirkpatrick, David, “Throw It at the Wall and See if It Sticks,” Fortune (December 12, 2005), http://money.cnn .com/magazines/fortune/fortune_archive/2005/12/12/8363128/index.htm; McGregor, Jena, “Would You Recommend Us?” Business Week ( January 30, 2006), http://www.businessweek.com/magazine/content/06_05/b3969090 .htm; Reicheld, Frederick F., “The One Number You Need to Grow,” Harvard Business Review (December 1, 2003).

Intuit Gets Answers to Satisfy Customers

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Random Sampling Error Most surveys try to portray a representative cross section of a particular target population. Even with technically proper random probability samples, however, statistical errors will occur because of chance variation in the elements selected for the sample. These statistical problems are unavoidable without very large samples (>400). However, the extent of random sampling error can be estimated. Chapters 16 and 17 will discuss these errors and ways they can be estimated in more detail.

Systematic Error The other major source of survey error, systematic error, results from some imperfect aspect of the research design or from a mistake in the execution of the research. Because systematic errors include all sources of error other than those introduced by the random sampling procedure, these errors or biases are also called nonsampling errors. A sample bias exists when the results of a sample show a persistent tendency to deviate in one direction from the true value of the popula- tion parameter. The many sources of error that in some way systematically influence answers can be divided into two general categories: respondent error and administrative error.

random sampling error

A statistical fluctuation that occurs because of chance variation in the elements

selected for a sample.

systematic error

Error resulting from some imperfect aspect of the

research design that causes respondent error or from a mistake in the execution of

the research.

sample bias

A persistent tendency for the results of a sample to deviate

in one direction from the true value of the population

parameter.

EXHIBIT 9.1

Categories of Survey Errors

Total error

Random sampling error

Systematic error (bias)

Respondent error

Administrative error

Sample selection

error

Interviewer error

Interviewer cheating

Data processing

error

Nonresponse error

Response bias

Deliberate falsification

Unconscious misrepresentation

Extremity bias

Interviewer bias

Auspices bias

Acquiescence bias

Social desirability

bias

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188 • PART THREE Research Methods for Collecting Primary Data

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Respondent Error Surveys ask people for answers. If people cooperate and give truthful answers, a survey will likely accomplish its goal. If these conditions are not met, nonresponse error or response bias, the two major categories of respondent error, may cause sample bias.

Nonresponse Error Few surveys have 100 percent response rates. In fact, surveys with relatively low response rates may still accurately reflect the population of interest. However, a researcher who obtains a 1 percent response to a five-page e-mail questionnaire concerning various brands of spark plugs may face a serious problem. To use the results, the researcher must believe that consumers who responded to the questionnaire are representative of all consumers, including those who did not respond. The statistical differences between a survey that includes only those who responded and a survey that also included those who failed to respond are referred to as nonresponse error. This problem is especially acute in mail and Internet surveys, but nonresponse also threatens telephone and face- to-face interviews.

People who are not contacted or who refuse to cooperate are called nonrespondents. A non- response occurs if no one answers the phone at the time of both the initial call and any subsequent callbacks. The number of no contacts in telephone survey research has been increasing because of the proliferation of answering machines and growing use of caller ID to screen telephone calls.5 The respondent who is not at home when called or visited should be scheduled to be interviewed at a different time of day or on a different day of the week. Refusals occur when people are unwilling to participate in the research. A parent who must juggle the telephone and a half-diapered child and refuses to participate in the survey because he or she is too busy also is a nonresponse. After receiving a refusal from a potential respondent, an interviewer can do nothing other than be polite.

A research team reviewed 50 mail surveys of pedia tricians conducted by the American Acad- emy of Pediatrics (AAP) between 1994 and 2002 and found that response rates declined over the

respondent error

A category of sample bias resulting from some respondent action or inaction such as nonresponse or response bias.

nonresponse error

The statistical differences between a survey that includes only those who responded and a perfect survey that would also include those who failed to respond.

nonrespondents

People who are not contacted or who refuse to cooperate in the research.

no contacts

People who are not at home or who are otherwise inaccessible on the first and second contact.

refusals

People who are unwilling to participate in a research project.

When companies conduct surveys to learn about customer satisfaction, they face an important challenge: Do the responses represent a cross section of customers? Maybe just the happiest or most angry customers participate. This problem also occurs when the “customers” are the patients of a health-care provider.

To investigate this issue, a group of researchers in Massachusetts studied data from patient satisfaction surveys that rated 6,681 patients’ experiences with 82 primary-care physicians (internists and family practitioners) at a health maintenance organization. These ratings represented response rates ranging from 11 to 55 percent, depending on the physician being rated. The researchers compared their informa- tion about response rates with a set of simulated data for which they knew the underlying distribution of responses. They found that the actual data closely matched simulated data in which responses were biased so that responses were more likely when satisfaction was higher.

The researchers concluded that there was a significant correlation between the response rate and average (mean) satisfaction rating. In other words, more-satisfied patients were more likely to complete and return the survey. Thus, if the HMO were to use the data to evaluate how satisfied patients are with their doctors, it would overestimate satisfaction. Also, it would have less information about its lower-performing doctors. The researchers therefore concluded that it is important to follow up with subjects to encourage greater response from less-satisfied patients.

Source: Based on Mazor, Kathleen M., Brian E. Clauser, Terry Field, Robert A. Yood, and Jerry H. Gurwitz, “A Demonstration of the Impact of Response Bias on the Results of Patient Satisfaction Surveys,” Health Services Research 37(5) (October 2002), 1403–1417.

Overestimating Patient Satisfaction

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period studied. In the early years of the study period, an average of 70 percent of pediatricians returned completed surveys; the response rate fell to an average of 63 percent in the second half of the period.6 No contacts and refusals can seriously bias survey data. In the case of the pediatricians, the researchers found little difference in the response rates attributable to differences in such easy-to-measure variables as age, sex, and type of membership in the AAP, leaving them to wonder whether the cause of refusals was some unknown but important difference among these doctors.

Because of this problem, researchers investigate the causes of nonresponse. For example, a study analyzed a large database col- lected by AT&T and found that the effort required to participate in an ongoing study contributes to the problem.7 People tend not to respond to questions that are difficult to answer. When they are asked to participate in a long-term panel, the rate of nonresponse to individual items grows over time, and eventually some people stop participating altogether. However, eventually it becomes easier to keep answering the same kinds of panel ques- tions, and nonresponse rates level off.

With a mail survey, the researcher never really knows whether a nonrespondent actually received the survey, has refused to par- ticipate, or is just indifferent. Researchers know that those who

are most involved in an issue are more likely to respond to a mail survey. Self-selection bias is a problem that frequently plagues self-administered questionnaires. In a restaurant, for example, a customer on whom a waiter spilled soup, a person who was treated to a surprise dinner, or others who feel strongly about the service are more likely to complete a self-administered questionnaire left at the table than individuals who are indifferent about the restaurant. Self-selection biases dis- tort surveys because they overrepresent extreme positions while underrepresenting responses from those who are indifferent. Several techniques will be discussed later for encouraging respondents to reply to mail and Internet surveys.

Comparing the demographics of the sample with the demographics of the target population is one means of inspecting for possible biases in response patterns. If a particular group, such as older citizens, is underrepresented or if any potential biases appear in a response pattern, additional efforts should be made to obtain data from the underrepresented segments of the population. For example, telephone surveys may be used instead of mail surveys or personal interviews may be used instead of telephone interviews in an attempt to increase participation of underrepresented segments.

Response Bias A response bias occurs when respondents tend to answer questions with a certain slant. People may consciously or unconsciously misrepresent the truth. If a distortion of measurement occurs because respondents’ answers are falsified or misrepresented, either intentionally or inadvertently, the resulting sample bias will be a response bias. When researchers identify response bias, they should include a corrective measure.

DELIBERATE FALSIFICATION Occasionally people deliberately give false answers. It is difficult to assess why people knowingly misrepresent answers. A response bias may occur when people misrepresent answers to appear intelligent, conceal personal information, avoid embarrassment, and so on. For example, respon- dents may be able to remember the total amount of money spent grocery shopping, but they may forget the exact prices of individual items that they purchased. Rather than appear ignorant or unconcerned about prices, they may provide their best estimate and not tell the truth—namely, that they cannot remember. Sometimes respondents become bored with the interview and provide answers just to get rid of the interviewer. At other times respondents try to appear well informed by providing the answers they think are expected of them. On still other occasions, they give answers simply to please the interviewer.

self-selection bias

A bias that occurs because people who feel strongly about a subject are more

likely to respond to survey questions than people who feel

indifferent about it.

response bias

A bias that occurs when respondents either consciously

or unconsciously tend to answer questions with a

certain slant that misrepresents the truth.

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Many e-mail addresses are actually inactive. Inactive e-mails contribute to low response rates.

190 • PART THREE Research Methods for Collecting Primary Data

One explanation for conscious and deliberate misrepresentation of facts is the so-called average-person hypothesis. Individuals may prefer to be viewed as average, so they alter their responses to conform more closely to their perception of the average person. Average-person effects have been found in response to questions about such topics as savings account balances, car prices, voting behavior, and hospital stays.

UNCONSCIOUS MISREPRESENTATION Even when a respondent is consciously trying to be truthful and cooperative, response bias can arise from the question format, the question content, or some other stimulus. For example, bias can be introduced by the situation in which the survey is administered. The results of two in-flight surveys concerning aircraft preference illustrate this point. Passengers flying on B-747s preferred B-747s to L-1011s (74 percent versus 19 percent), while passengers flying on L-1011s preferred L-1011s to B-747s (56 percent versus 38 percent). Managers may be tempted to conclude that the results demonstrate a preference for B-747s over L-1011s. But perhaps respondents were influ- enced by other factors besides the airplane or had not experienced flying in the other type of plane. Respondents’ satisfaction scores may simply be a simple response to their overall satisfaction with the flying experience. Also, airlines have fleets that consist predominantly of one brand of aircraft. Thus, the data appearing to support Boeing may really be showing greater satisfaction for the airlines that happen to be flying Boeing and would have higher satisfaction no matter what planes were in their fleet.8

Respondents who misunderstand questions may unconsciously provide biased answers. Or they may be willing to answer but unable to do so because they have forgotten the exact details. Asking “When was the last time you attended a concert?” may result in a best-guess estimate because the respondent has forgotten the exact date.

A bias may also occur when a respondent has not thought about an unexpected question. Many respondents will answer questions even though they have given them little thought. For example, in most investigations of consumers’ buying intentions, the predictability of their inten- tions depends on how close the subject is to making a purchase. The intentions of subjects who have little knowledge of the brand or the store alternatives being surveyed and the intentions of subjects who have not yet made any purchase plans cannot be expected to predict purchase behav- ior accurately.

In many cases consumers cannot adequately express their feelings in words. The cause may be questions that are vague or ambiguous. Researchers may ask someone to describe his or her frustration when using a computer. The problem is, the researcher may be interested in software problems while the respondent is thinking of hardware issues. Language differences also may be a source of misunderstanding. A survey in the Philippines found that, despite seemingly high toothpaste usage, only a tiny percentage of people responded positively when asked, “Do you use toothpaste?” As it turned out, people in the Philippines tend to refer to toothpaste by using the brand name Colgate. When researchers returned and asked, “Do you use Colgate?” the positive response rate soared.

As the time following a purchase or a shopping event increases, people become more likely to underreport information about that event. Time lapse influences people’s ability to precisely remember and communicate specific factors.

Unconscious misrepresentation bias may also occur because consumers unconsciously avoid facing the realities of a future buying situation. Housing surveys record that Americans overwhelm- ingly continue to aspire to own detached, single-family dwellings (preferably single-level, ranch- type structures that require two to five times the amount of land per unit required for apartments). However, builders know that apartment (condo) purchases by first buyers are more common than respondents report, despite their aspirations.

TYPES OF RESPONSE BIAS Response bias falls into four specific categories: acquiescence bias, extremity bias, interviewer bias, and social desirability bias. These categories overlap and are not mutually exclusive. A single biased answer may be distorted for many complex reasons, some distortions being deliberate and some being unconscious misrepresentations.

CHAPTER 9 Survey Research: An Overview • 191

Acquiescence Bias. Some respondents are very agreeable. They seem to agree to practically every statement they are asked about. A tendency to agree (or disagree) with all or most questions is known as acquiescence bias. This bias is particularly prominent in new product research. Questions about a new product idea generally elicit some acquiescence bias because respondents give positive connotations to most new ideas. For example, consumers responded favorably to survey questions about pump baseball gloves (the pump inserts air into the pocket of the glove, providing more cush- ioning). However, when these expensive gloves hit the market, they sat on the shelves. When con- ducting new product research, researchers should recognize the high likelihood of acquiescence bias.

Agreement or disagreement can also be influenced by a respondent’s feelings toward the orga- nization identified as conducting or sponsoring the research. Auspices bias occurs when answers reflect the person’s liking or disliking of the sponsor organization rather than simply the relevant opinions.

Extremity Bias. Some individuals tend to use extremes when responding to questions. For example, they may choose only 1 or 10 on a 10-point scale. Others consistently refuse to use extreme positions and tend to respond more neutrally—“I never give a 10 because nothing is really perfect.” Response styles vary from person to person, and extreme responses may cause an extremity bias in the data.9

Interviewer Bias. Response bias may arise from the interplay between interviewer and respon- dent. If the interviewer’s presence influences respondents to give untrue or modified answers, the survey will be marred by interviewer bias. Many homemakers and retired people welcome an interviewer’s visit as a break in routine activities. Other respondents may give answers they believe will please the interviewer rather than the truthful responses. Respondents may wish to appear intel- ligent and wealthy—of course they read Scientific American rather than The National Enquirer!

The interviewer’s age, sex, style of dress, tone of voice, facial expressions, or other nonverbal characteristics may have some influence on a respondent’s answers. If an interviewer smiles and makes a positive statement after a respondent’s answers, the respondent will be more likely to give similar responses. In a research study on sexual harassment against saleswomen, male interviewers might not yield as candid responses from saleswomen as female interviewers would.

Many interviewers, contrary to instructions, shorten or rephrase questions to suit their needs. This potential influence on responses can be avoided to some extent if interviewers receive train- ing and supervision that emphasize the necessity of appearing neutral.

If interviews go on too long, respondents may feel that time is being wasted. They may answer as abruptly as possible with little forethought.

Social Desirability Bias. Social desirability bias may occur either consciously or unconsciously because the respondent wishes to create a favorable impression or save face in the presence of an interviewer. Incomes may be inflated, education overstated, or perceived respectable answers given to gain prestige. In contrast, answers to questions that seek factual information or responses about

matters of public knowledge (zip code, number of children, and so on) usually are quite accurate. An interviewer’s presence may increase a respondent’s tendency to give inaccurate answers to sensitive questions such as “Did you vote in the last election?” or “Do you have termites or roaches in your home?” or “Do you color your hair?”

Social desirability bias is especially significant in the case of research that addresses sensitive or per- sonal topics, including respondents’ sexual behavior. A group of researchers recently evaluated responses to questions about homosexual sexual activity, col- lected by the National Opinion Research Center’s long-running General Social Survey.10 The research- ers found that over time, as attitudes toward homo- sexual conduct have softened, the frequency of repeated female-female sexual contacts increased

extremity bias

A category of response bias that results because some

individuals tend to use extremes when responding to questions.

interviewer bias

A response bias that occurs because the presence of

the interviewer influences respondents’ answers.

social desirability bias

Bias in responses caused by respondents’ desire, either

conscious or unconscious, to gain prestige or appear in a

different social role.

acquiescence bias

A tendency for respondents to agree with all or most

questions asked of them in a survey.

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The more people are susceptible to interpersonal influence, the more likely a response bias will occur. One example of this can be found in adolescents’ buying behavior.

192 • PART THREE Research Methods for Collecting Primary Data

dramatically, suggesting the possibility that reporting levels have been subject to social desirabil- ity bias. However, the researchers noted that rates of male-male sexual contact were fairly steady over the period and that the rate of change for female-female sexual contact persisted even when adjusted for measures of greater tolerance. This evidence suggests that the data reflect more phe- nomena than mere social desirability bias.

Administrative Error The result of improper administration or execution of the research task is called an administrative error. Administrative errors are caused by carelessness, confusion, neglect, omission, or some other blunder. Four types of administrative error are data-processing error, sample selection error, inter- viewer error, and interviewer cheating.

Data-Processing Error Processing data by computer, like any arithmetic or procedural process, is subject to error because data must be edited, coded, and entered into the computer by people. The accuracy of data pro- cessed by computer depends on correct data entry and programming. Data-processing error can be minimized by establishing careful procedures for verifying each step in the data-processing stage.

Sample Selection Error Many kinds of error involve failure to select a representative sample. Sample selection error is systematic error that results in an unrepresentative sample because of an error in either the sample design or the execution of the sampling procedure. Executing a sampling plan free of procedural error is difficult. A firm that selects its sample from the phone book will have some systematic error, because those people with only cell phones or with unlisted numbers are not included. Stopping respondents during daytime hours in shopping centers largely excludes working people or those who primarily shop by mail, Internet, or telephone. In other cases, researchers interview the wrong person. Consider a political pollster who uses random-digit dialing to select a sample, rather than a list of registered voters. Unregistered 17-year-olds may be willing to give their opin- ions, but they are the wrong people to ask because they cannot vote.

Interviewer Error Interviewers’ abilities vary considerably. Interviewer error is introduced when interviewers record answers but check the wrong response or are unable to write fast enough to record answers verbatim. Also, selective perception may cause interviewers to misrecord data that do not support their own attitudes and opinions.

Interviewer Cheating Interviewer cheating occurs when an interviewer falsifies entire questionnaires or fills in answers to questions that have been intentionally skipped. Some interviewers cheat to finish an interview as quickly as possible or to avoid questions about sensi- tive topics. Often interviewers are paid by the completed survey, so you can see the motivation to complete a survey that is left with some questions unanswered.

If interviewers are suspected of faking questionnaires, they should be told that a

administrative error

An error caused by the improper administration or execution of the research task.

data-processing error

A category of administrative error that occurs because of incorrect data entry, incorrect computer programming, or other procedural errors during data analysis.

sample selection error

An administrative error caused by improper sample design or sampling procedure execution.

interviewer error

Mistakes made by interviewers failing to record survey responses correctly.

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interviewer cheating

The practice of filling in fake answers or falsifying questionnaires while working as an interviewer.

CHAPTER 9 Survey Research: An Overview • 193

small percentage of respondents will be called back to confirm whether the initial interview was actually conducted. This practice should discourage interviewers from cheating. The term curbstoning is sometimes used to refer to interviewers filling in responses for respondents that do not really exist.

Rule-of-Thumb Estimates for Systematic Error The techniques for estimating systematic, or nonsampling, error are less precise than many sample statistics. Researchers have established conservative rules of thumb based on experience to estimate systematic error. In the case of consumer research, experienced researchers might determine that only a certain percentage of people who say they will definitely buy a new product actually do so. Evidence for a mere-measurement effect (see the Research Snapshot “The ‘Mere-Measurement’ Effect”) suggests that in some situations, researchers might conclude that respondents’ own buying behavior will exaggerate overall sales. Thus, researchers often present actual survey findings and their interpretations of estimated purchase response based on estimates of nonsampling error. For example, one pay-per-view cable TV company surveys geographic areas it plans to enter and esti- mates the number of people who indicate they will subscribe to its service. The company knocks down the percentage by a “ballpark 10 percent” because experience in other geographic areas has indicated that there is a systematic upward bias of 10 percent on this intentions question.

What Can Be Done to Reduce Survey Error? Now that we have examined the sources of error in surveys, you may have lost some of your optimism about survey research. Don’t be discouraged! The discussion emphasized the bad news because it is important for managers to realize that surveys are not a panacea. There are, however, ways to handle and reduce survey errors. For example, Chapter 15 on questionnaire design dis- cusses the reduction of response bias; Chapters 16 and 17 discuss the reduction of sample selection and random sampling error. Indeed, much of the remainder of this book discusses various tech- niques for reducing bias in business research. The good news lies ahead!

Classifying Survey Research Methods Now that we have introduced some advantages and disadvantages of surveys in general, we turn to a discussion of classification of surveys according to several criteria. Surveys may be classified based on the method of communication, the degrees of structure and disguise in the questionnaire, and the time frame in which the data are gathered (temporal classification). Chapter 10 classifies surveys according to method of communicating with the respondent, covering topics such as personal interviews, telephone interviews, mail surveys, and Internet surveys. The classifications based on structure and disguise and on time frame will be discussed in the remainder of this chapter.

Structured/Unstructured and Disguised/ Undisguised Questionnaires In designing a questionnaire (or an interview schedule), the researcher must decide how much structure or standardization is needed.11 A structured question limits the number of allowable responses. For example, the respondent may be instructed to choose one alternative response such as under 18, 18–35, or over 35 to indicate his or her age. An unstructured question does not restrict the respondent’s answers. An open-ended, unstructured question such as, “Why do you shop at Walmart?” allows the respondent considerable freedom in answering.

The researcher must also decide whether to use undisguised questions or disguised questions. A straightforward, or undisguised, question such as, “Do you have dandruff problems?” assumes that the respondent is willing to reveal the information. However, researchers know that some questions

structured question

A question that imposes a limit on the number of allowable

responses.

unstructured question

A question that does not restrict the respondents’

answers.

undisguised questions

Straightforward questions that assume the respondent is

willing to answer.

disguised questions

Indirect questions that assume the purpose of the study must be hidden from the

respondent.

194 • PART THREE Research Methods for Collecting Primary Data

195

are threatening to a person’s ego, prestige, or self-concept. So, they have designed a number of indirect techniques of questioning to disguise the purpose of the study.

Questionnaires can be categorized by their degree of structure and degree of disguise. For example, interviews in exploratory research might use unstructured-disguised questionnaires. The projective techniques discussed in Chapter 7 fall into this category. Other classifications are structured-undisguised, unstructured-undisguised, and structured-disguised. These classifications have two limitations: First, the degree of structure and the degree of disguise vary; they are not clear-cut categories. Second, most surveys are hybrids, asking both structured and unstructured questions. Recognizing the degrees of structure and disguise necessary to meet survey objectives will help in the selection of the appropriate communication medium for conducting the survey.

Temporal Classification Although most surveys are for individual research projects conducted only once over a short time period, other projects require multiple surveys over a long period. Thus, surveys can be classified on a temporal basis.

Cross-Sectional Studies Do you make New Year’s resolutions? A Harris Interactive survey conducted in November 2008 indicates that women (74 percent) are more likely than men (58 percent) to actually make a New Year’s resolution. However, more men than women “always or often keep their resolutions” (22 percent of men compared to 14 percent of women).12 This was a cross-sectional study because it collected the data at a single point in time. That is, the survey asked people to reflect on their past behavior, rather than ask them if they made a resolution, then follow up a year later to see if the resolution was kept. Such a study samples various segments of the population to investigate relationships among variables by cross-tabulation. Most business research surveys fall into this cat- egory. We can think of cross-sectional studies as taking a snapshot of the current situation.

Will you eat high-fat food this week? Will you floss your teeth? Researchers have found that answering survey questions like these can actually shift your behavior. This influence, called the mere-measurement effect, means that simply answering a question about intentions will increase the likelihood of the underlying behavior—if the behavior is seen as socially desir- able. If the behavior is considered undesirable, answering the question tends to decrease the likelihood of the behavior.

To test this, a group of business school professors conducted a series of surveys in which certain subjects were asked about their intentions to eat fatty food or to floss. In follow-up surveys, they found that subjects ate less fatty food and flossed more often if they were asked about those behaviors. However, the mere-measurement effect did not occur if the surveys indicated that they were sponsored by groups that would be likely to want to persuade the subjects (in this case, the American Fruit Grow- ers Association and the Association of Dental Products Manufac- turers). In fact, subjects decreased their frequency of flossing if they took the supposedly manipulative survey that asked about flossing. Follow-up experiments verified that changes to behavior

were genuine, not merely a survey bias.

The research- ers propose that the mere-measurement effect occurs because subjects of a survey generally do not think the questions are an attempt to persuade them. If they receive information that puts them on their guard against persuasion, the mere-measurement effect is lessened and sometimes even generates the opposite behavior. Their results suggest a need for caution when surveys attempt to predict future behavior.

Source: Williams, Patti, Gavan J. Fitzsimons, Lauren G. Block, “When Consumers Do Not Recognize ‘Benign’ Intention Questions as Persuasion Attempts,” © 2004 by Journal of Consumer Research, Inc. 31 (December 2004). All rights reserved. Reprinted with permission by the University of Chicago Press.

The “Mere-Measurement” Effect

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cross-sectional study

A study in which various segments of a population are sampled and data are collected at a single moment in time.

The typical method of analyzing a cross-sectional survey is to divide the sample into appropri- ate subgroups. For example, if a winery expects income levels to influence attitudes toward wines, the data are broken down into subgroups based on income and analyzed to reveal similarities or differences among the income subgroups. If a manager thinks that length of time an employee has been with the organization will influence their attitudes toward corporate policies, employees might be broken into different groups based on tenure (e.g., less than 5 years, 5–9 years, 10–14 years, and 15 years or more) so their attitudes can be examined.

Longitudinal Studies In a longitudinal study respondents are questioned at multiple points in time. The purpose of lon- gitudinal studies is to examine continuity of response and to observe changes that occur over time. Many syndicated polling services, such as Gallup, conduct regular polls. For example, the Bureau of Labor Statistics conducts the National Longitudinal Survey of Youth, interviewing the same sample of individuals repeatedly since 1979. (Respondents, who were “youth” at the beginning of the study, are now in their 40s.) Research scientist Jay Zagorsky recently analyzed the longitudinal data from that study to determine that those who married and stayed with their spouse accumu- lated almost twice as much wealth as single and divorced people in the study.13 The Yankelovich MONITOR has been tracking American values and attitudes for more than 30 years. This survey is an example of a longitudinal study that uses successive samples; its researchers survey several different samples at different times. Longitudinal studies of this type are sometimes called cohort studies, because similar groups of people who share a certain experience during the same time interval (cohorts) are expected to be included in each sample. Exhibit 9.2 illustrates the results of a longitudinal study by Harris Interactive, which since 1966 has been asking five questions related to powerlessness and isolation to create an “alienation index.” We can think of longitudinal studies as taking a movie of an evolving situation.

‘‘Time is but the stream I go a-fishing in.’’—HENRY DAVID THOREAU

longitudinal study

A survey of respondents at different times, thus allowing

analysis of response continuity and changes over time.

EXHIBIT 9.2

Longitudinal Research from a Harris Poll

Source: “Americans Feel More Isolated, Less Empowered, Poll Shows,” Wall Street Journal (December 8, 2005), http://online.wsj.com.

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In applied business research, a longitudinal study that uses successive samples is called a tracking study because successive waves are designed to compare trends and identify changes in variables such as consumer satisfaction, brand image, or advertising awareness. These studies are useful for assessing aggregate trends but do not allow for tracking changes in individuals over time.

Conducting surveys in waves with two or more sample groups avoids the problem of response bias resulting from a prior interview. A respondent who was interviewed in an earlier survey about a certain brand may become more aware of the brand or pay more attention to its advertising after being interviewed. Using different samples eliminates this problem. However, researchers can never be sure whether the changes in the variable being measured are due to a different sample or to an actual change in the variable over time.

CONSUMER PANEL A longitudinal study that gathers data from the same sample of individuals or households over time is called a consumer panel. Consider the packaged-goods marketer that wishes to learn about brand-switching behavior. A consumer panel that consists of a group of people who record their purchasing habits in a diary over time will provide the manager with a continuous stream of information about the brand and product class. Diary data that are recorded regularly over an extended period enable the researcher to track repeat-purchase behavior and changes in pur- chasing habits that occur in response to changes in price, special promotions, or other aspects of business strategy.

Panel members may be contacted by telephone, in a personal interview, by mail questionnaire, or by e-mail. Typically respondents complete media exposure or purchase diaries and mail them back to the survey organization. If the panel members have agreed to field test new products, face-to-face or telephone interviews may be required. The nature of the problem dictates which communication method to use.

Because establishing and maintaining a panel is expensive, panels often are managed by contractors who offer their services to many organizations. A number of commercial firms, such as National Family Opinion (NFO), Inc. and Markettools, Inc., specialize in maintaining consumer panels. In recent years Internet panels have grown in popularity. Because clients of these firms need to share the expenses with other clients to acquire longitudinal data at a reasonable cost, panel members may be asked questions about a number of different issues.

The first questionnaire a panel member is asked to complete typically includes questions about product ownership, product usage, pets, family members, and demographic data. The purpose of such a questionnaire is to gather the behavioral and demographic data that will be used to identify heavy buyers, difficult-to-reach customers, and so on for future surveys. Individuals who serve as members of consumer panels usually are compensated with cash, attractive gifts, or the chance to win a sweepstakes.

Marketers whose products are purchased by few households find panels an economical means of reaching respondents who own their products. A two-stage process typically is used. A panel composed of around 15,000 households can be screened with a one-question statement attached to another project. For example, a question in an NFO questionnaire screens for owner- ship of certain uncommon products, such as snowmobiles and motorcycles. This information is stored in a database. Then households with the unusual item can be sampled again with a longer questionnaire.

Total Quality Management and Customer Satisfaction Surveys Total quality management is a business strategy that emphasizes market-driven quality as a top priority. Total quality management involves implementing and adjusting the firm’s business activities to assure customers’ satisfaction with the quality of goods and services.

Many U.S. organizations adopted total quality management in the 1980s when an increase in high-quality foreign competition challenged their former dominance. Today companies continue

tracking study

A type of longitudinal study that uses successive samples to compare trends and identify changes in variables such as consumer satisfaction, brand image, or advertising awareness.

consumer panel

A longitudinal survey of the same sample of individuals or households to record their attitudes, behavior, or purchasing habits over time.

total quality management

A business philosophy that emphasizes market-driven quality as a top organizational priority.

CHAPTER 9 Survey Research: An Overview • 197

to recognize the need for total quality management programs. Executives and production workers are sometimes too far removed from the customer. Companies need a means to bridge this gap with feedback about quality of goods and services. This means conducting research. In an organi- zation driven by the quality concept, business research plays an important role in the management of total product quality.

What Is Quality? Organizations used to define quality by engineering standards. Most companies no longer see quality that way. Some managers say that having a quality product means that the good or service conforms to consumers’ requirements and that the product is acceptable. Effective executives who subscribe to a total quality management philosophy, however, believe that the product’s quality must go beyond acceptability for a given price range. Rather than merely being relieved that noth- ing went wrong, consumers should experience some delightful surprises or reap some unexpected benefits. In other words, quality assurance is more than just meeting minimum standards. The level of quality is the degree to which a good or service truly is seen as good or bad.

Obviously, a BMW 750i does not compete directly with a Kia Forte. Buyers of these automo- biles are in different market segments, and their expectations of quality differ widely. Nevertheless, managers at BMW and Kia both try to establish the quality that is acceptable given the cost of ownership.

Internal and External Customers Organizations that have adopted the total quality management philosophy believe that a focus on customers must include more than external customers. They believe that everyone in the organiza- tion has customers, and that the development of sound, comprehensive customer relationships is a key factor to business success.14

Total quality management programs work most effectively when every employee knows exactly who his or her customers are and what output internal and external customers expect. Also, it is important to know how customers perceive their needs are being met. All too often differences between perceptions and reality are not understood.

Implementing Total Quality Management Implementing a total quality management program requires considerable survey research. A firm must routinely ask customers to rate it against its competitors. It must periodically measure employee knowledge, attitudes, and expectations. It must monitor company performance against benchmark standards. It must determine whether customers found any delightful surprises or major disappointments. In other words, a total quality management strategy expresses the convic- tion that to improve quality, an organization must regularly conduct surveys to evaluate quality improvement.

Exhibit 9.3 illustrates the total quality management process. The exhibit shows that over- all tracking of quality improvement requires longitudinal research. The process begins with a commitment and exploration stage, during which management makes a commitment to total quality assurance and researchers explore external and internal customers’ needs and beliefs. The research must discover what product features customers value, what problems customers are hav- ing with the product, what aspects of product operation or customer service have disappointed customers, what the company is doing right, and what the company may be doing wrong.

After internal and external customers’ problems and desires have been identified, the bench- marking stage begins. Research must establish quantitative measures that can serve as bench- marks or points of comparison against which to evaluate future efforts. The surveys must establish initial measures of overall satisfaction, of the frequency of customer problems, and of quality

198 • PART THREE Research Methods for Collecting Primary Data

ratings for specific attributes. Researchers must identify the company’s or brand’s position rela- tive to competitors’ quality positions. For example, when Anthony Balzarini became food-service manager at Empire Health Services in Spokane, Washington, he became responsible for serving meals to the patients of the company’s two hospitals, plus retail food service (sales to visitors and employees who eat in the hospitals). He began tracking quality according to several measures, including satisfaction scores on patient surveys and sales volume and revenue on the retail side. Sales measurements include comparing the average sale with other locations, including restaurants, in the Spokane area.15

The initial quality improvement stage establishes a quality improvement process within the organization. Management and employees must translate quality issues into the internal vocabu- lary of the organization. The company must establish performance standards and expectations for improvement. For Balzarini, this stage included training food-service employees in providing

EXHIBIT 9.3

Longitudinal Research for Total Quality Management

Tracking wave 1 to measure and compare what is actually happening with what should be happening. Establish whether the company is conforming to its quality standards.

Exploratory study to determine the quality the customer wants, discover customer problems, and identify the importance of specific product attributes.

Establish marketing objective that the customer should define quality.

Benchmarking study to measure overall satisfaction and quality ratings of specific attributes.

Tracking wave 1 to measure trends in satisfaction and quality ratings.

Tracking wave 2 to measure trends in satisfaction and quality ratings.

Improve quality; reward performance.

Identify brand’s position relative to competitors’ satisfaction and quality rating; establish standards for customer satisfaction.

Benchmarking to measure employees’ actual performance and perceptions about performance.

Exploratory study to determine (1) whether internal customers, such as service employees, are aware of the need for service quality as a major means to achieve customer satisfaction and (2) whether they know the quality standards for their jobs. Establish whether employees are motivated and trained. Identify road blocks that prevent employees from meeting customer needs.

Time 1

Commitment and exploration stage

Time 2

Benchmarking stage

Time 3

Initial quality improvement stage

Time 4

Continuous quality improvement

Tracking wave 2 to measure trends in quality improvement.

Marketing Research Activity with External Consumers (Customers)

Marketing Management Activity

Marketing Research Activity with Internal Consumers (Employees)

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CHAPTER 9 Survey Research: An Overview • 199

patient service. He began holding meetings twice daily to identify any problems to be resolved. Managers were each assigned to one floor of the hospital and charged with building a close working relationship with the nursing staff there. They are expected to visit their floor every week and conduct 15 interviews with patients to learn about what they like and dislike. On the retail side, the manager is expected to revise menus every 12 weeks to offer more variety. Waste is literally weighed and categorized to identify which types of food are rejected by patients and customers.

After managers and employees have set quality objectives and implemented procedures and standards, the firm continues to track satisfaction and quality ratings in successive waves. The pur- pose of tracking wave 1 is to measure trends in satisfaction and quality ratings. Business researchers determine whether the organization is meeting customer needs as specified by quantitative stan- dards. At one of Empire’s two hospitals, one of the food-service managers learned that a patient on a liquid diet disliked the broth he was being served. An investigation showed that the recipe had been changed, and a taste test confirmed that the original recipe was superior, so the hospital switched back to the original recipe.

The next stage, continuous quality improvement, consists of many consecutive waves with the same purpose—to improve over the previous period. Continuous quality improvement requires that management allow employees to initiate problem solving without a lot of red tape. Employees should be able to initiate proactive communications with consumers. In tracking wave 2, manage- ment compares results with those of earlier stages. Quality improvement management continues. At Empire, improvements have been reflected in rising patient satisfaction scores and growing sales in retail operations.

Management must also reward performance. At Empire, Balzarini set up a program called “You Rock.” Any employee who observes an excellent action by another employee, beyond mere job requirements, acknowledges the good work with a card awarding points redeemable in the hospitals’ retail areas. Balzarini also sends weekly thank-you cards to workers who showed out- standing performance.

Exhibit 9.3 shows that total quality management programs measure performance against cus- tomers’ standards—not against standards determined by quality engineers within the company. All changes within the organization are oriented toward improvement of customers’ perceptions of quality. The exhibit indicates the need for integration of establishing consumer requirements, quantifying benchmark measures, setting objectives, conducting research studies, and making adjustments in the organization to improve quality. Continuous quality improvement is an ongo- ing process.

The activities outlined in Exhibit 9.3 work for providers of both goods and services. However, service products and customer services offered along with goods have some dis- tinctive aspects. We will first discuss the quality of goods and then consider the quality of services.

In general, consumer and industrial goods providers track customer satisfaction to inves- tigate customer perceptions of product quality by measuring perceptions of the product characteristics listed in Exhibit 9.4.16 These studies measure whether a firm’s perceptions about product characteristics conform to customers’ expectations and how these perceptions change over time. For example, any customer satisfaction survey will investigate a good’s per- formance by asking, “How well does the product perform its core function?” To determine the quality of a recycling lawn mower, a researcher might ask, “How well does the mower cut grass and eliminate the need for bagging clippings?” The researcher may ask questions to determine whether the product’s quality of performance was a delightful surprise, something well beyond expected performance. Similar questions will cover the other major product characteristics.

Measuring service disconfirmation involves comparing expectations with performance. Favorable quality and better-than-expected performance leads to satisfaction. Time after time, studies have shown differences between what customers expected and what the front- line service personnel delivered. Researchers direct a lot of effort toward assessing consumer expectations.

In organizations that wish to improve service quality, managers must identify and analyze customer service needs and then establish specifications for the level of service. They must then

200 • PART THREE Research Methods for Collecting Primary Data

train frontline personnel and give them the responsibility for quality service. Frontline personnel need to be motivated and encouraged to deliver the service that goes beyond consumer expecta- tions. Finally, regular surveys with both external customers and internal employees measure results against standards.

SUMMARY

1. Define surveys and explain their advantages. The survey is a common tool for asking respondents questions. Surveys can provide quick, inexpensive, and accurate information for a variety of objectives. The term sample survey is often used because a survey is expected to obtain a representative sample of the target population.

EXHIBIT 9.4

Quality Dimensions for Goods and Services

Quality Dimension Characteristic Example

Goods

Performance The product performs its core function. A razor gives a close shave.

Features The product has auxiliary dimensions that provide secondary benefits.

A motor oil comes in a convenient package.

Conformance with specifications There is a low incidence of defects. Napa Valley wine comes from Napa Valley.

Reliability The product performs consistently. A lawn mower works properly each time it is used.

Durability The economic life of the product is within an acceptable range.

A motorcycle runs fine for many years.

Serviceability The system for servicing the product is efficient, competent, and convenient.

A computer software manufacturer maintains a toll-free phone number staffed by technical people who can answer questions quickly and accurately.

Aesthetic design The product’s design makes it look and feel like a quality product.

A snowmobile is aerodynamic.

Services

Access Contact with service personnel is easy. A visit to the dentist does not involve a long wait.

Communication The customer is informed and understands the service and how much it will cost.

A computer technician explains needed repairs without using overly technical terms.

Competence The service providers have the required skills. A tax accountant has a CPA certification.

Courtesy Personnel are polite and friendly. Bank tellers smile and wish the customer a “good day” at the close of each transaction.

Reliability The service is performed consistently and personnel are dependable.

Employees of the office cleaning service arrive on schedule every Friday evening after working hours.

Credibility Service providers have integrity. The doctor who is performing a heart transplant is trustworthy and believable.

Source: Adapted from Aaker, David A., Managing Brand Equity (New York: Macmillan, 1991), 90–95.

CHAPTER 9 Survey Research: An Overview • 201

2. Describe the type of information that may be gathered in a survey. The typical survey is a descriptive research study with the objective of measuring awareness, knowledge, behavior, opinions, and attitudes, both inside and outside of the organization. Common survey populations include customers, employees, suppliers and distributors.

3. Identify sources of error in survey research. Two major forms of error are common in survey research. The first, random sampling error, is caused by chance variation and results in a sample that is not absolutely representative of the target population. Such errors are inevitable, but they can be predicted using the statistical methods discussed in later chapters on sampling. The second major category of error, systematic error, takes several forms. Nonresponse error is caused by subjects’ failing to respond to a survey. This type of error can be identified by comparing the demographics of the sample population with those of the target population and reduced by making a special effort to contact underrepresented groups. In addition, response bias occurs when a response to a questionnaire is falsified or misrepresented, either intentionally or inadvertently. There are four specific categories of response bias: acquiescence bias, extrem- ity bias, interviewer bias, and social desirability bias. An additional source of survey error comes from administrative problems such as inconsistencies in interviewers’ abilities, cheating, coding mistakes, and so forth.

4. Distinguish among the various categories of surveys. Surveys may be classified according to methods of communication, by the degrees of structure and disguise in the questionnaires, and on a temporal basis. Questionnaires may be structured, with limited choices of responses, or unstruc- tured, to allow open-ended responses. Disguised questions camouflage the real purpose and may be used to probe sensitive topics. Surveys may consider the population at a given moment or fol- low trends over a period of time. The first approach, the cross-sectional study, usually is intended to separate the population into meaningful subgroups. The second type of study, the longitudinal study, can reveal important population changes over time. Longitudinal studies may involve con- tacting different sets of respondents or the same ones repeatedly. One form of longitudinal study is the consumer panel. Consumer panels are expensive to conduct, so firms often hire contractors who provide services to many companies, thus spreading costs over many clients.

5. Discuss the importance of survey research to total quality management programs. Total quality management is the process of implementing and adjusting a firm’s business strategy to assure customers’ satisfaction with the quality of goods or services. The level of quality is the degree to which a good or service is seen as good or bad by customers. Business research provides companies with feedback about the quality of goods and services. Implementing a total quality management program requires considerable survey research, conducted routinely, to ask custom- ers to rate a company against its competitors. It also measures employee attitudes and monitors company performance against benchmark standards. After identifying customer problems and desires, the firm tracks satisfaction and quality ratings in successive waves. Total quality manage- ment research is an ongoing process for continuous quality improvement that works for both marketers of goods and service providers.

KEY TERMS AND CONCEPTS acquiescence bias, 192 administrative error, 193 consumer panel, 197 cross-sectional study, 195 data-processing error, 193 disguised questions, 194 extremity bias, 192 interviewer bias, 192 interviewer cheating, 193 interviewer error, 193

longitudinal study, 196 no contacts, 189 nonrespondents, 189 nonresponse error, 189 random sampling error, 188 refusals, 189 respondent error, 189 respondents, 185 response bias, 190 sample bias, 188

sample selection error, 193 sample survey, 185 self-selection bias, 190 social desirability bias, 192 structured question, 194 systematic error, 188 total quality management, 197 tracking study, 197 undisguised questions, 194 unstructured question, 194

202 Part 3: Research Methods for Collecting Primary Data

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Name several nonbusiness applications of survey research. 2. What is self-selection bias? How might we avoid this? 3. Do surveys tend to gather qualitative or quantitative data? What

types of information are commonly measured with surveys? 4. Give an example of each type of error listed in Exhibit 9.1. 5. In a survey, chief executive officers (CEOs) indicated that they

would prefer to relocate their businesses to Atlanta (first choice), San Diego, Tampa, Los Angeles, or Boston. The CEOs who said they planned on building new office space in the following year were asked where they were going to build. They indicated they were going to build in New York, Los Angeles, San Fran- cisco, or Chicago. Explain the difference between these two responses.

6. What potential sources of error might be associated with the fol- lowing situations? a. In a survey of frequent fliers age 50 and older, researchers

concluded that price does not play a significant role in air- line travel because only 25 percent of the respondents check off price as the most important consideration in determining where and how they travel.

b. A survey of voters finds that most respondents do not like negative political ads—that is, advertising by one political candidate that criticizes or exposes secrets about the oppo- nent’s “dirty laundry.”

c. Researchers who must conduct a 45-minute personal inter- view decide to offer $25 to each respondent because they believe that people who will sell their opinions are more typical than someone who will talk to a stranger for 45 minutes.

d. A company’s sales representatives are asked what percentage of the time they spend making presentations to prospects, traveling, talking on the telephone, participating in meet- ings, working on the computer, and engaging in other on-the-job activities.

e. A survey comes with a water hardness packet to test the hardness of the water in a respondent’s home. The packet

includes a color chart and a plastic strip to dip into hot water. The respondent is given instructions in six steps on how to compare the color of the plastic strip with the color chart that indicates water hardness.

7. A researcher investigating public health issues goes into a junior high school classroom and asks the students if they have ever smoked a cigarette. The students are asked to respond orally in the presence of other students. What types of error might enter into this process? What might be a better approach?

8. A survey conducted by the National Endowment for the Arts asked, “Have you read a book within the last year?” What response bias might arise from this question?

9. Name some common objectives of cross-sectional surveys. 10. Give an example of a political situation in which longitudinal

research might be useful. Name some common objectives for a longitudinal study in a business situation.

11. What are the advantages and disadvantages of using consumer panels?

12. Search either your local newspaper, the Wall Street Journal, or USA Today to find some news story derived from sur- vey research results. Often, these stories deal with public opinions about product complaints, product consumption, job-related issues, marriage and family, public policy issues, or politics. Was the study’s methodology appropriate to draw conclusions?

13. Suppose you are the research director for your state’s tourism bureau. Assess the state’s information needs, and identify the information you will collect in a survey of tourists who visit your state.

14. EtHICS A researcher sends out 2,000 questionnaires via e-mail. Fifty are returned because the addresses are inaccurate. Of the 1,950 delivered questionnaires, 100 are completed and e-mailed back. However, 40 of these respondents wrote that they did not want to participate in the survey. The researcher indicates the response rate was 5.0 percent. Is this the right thing to do?

RESEARCH ACTIVITIES 1. ’NEt Go to SurveyMonkey (http://www.surveymonkey.com).

Then, visit http://www.mysurvey.com. What is the difference between the two websites in terms of the services they provide to users?

2. ’NEt The National Longitudinal Surveys (NLS) conducted by the Bureau of Labor Statistics provide data on the labor force experience (current labor force and employment status, work history, and characteristics of current or last job) of five groups of the U.S. population. Go to http://www.bls.gov/opub/hom/ to learn about the objectives and methodology for this study. How accurate do you believe the information reported here really is? What sources of error might be present in the data?

3. Ask a small sample of students at your university to report their GPA. Then, try to find the average GPA of students at your school. If you have to, ask several professors to give their opinion. Does it seem that the student data are subject to error? Explain.

4. ’NEt Located at the University of Connecticut, the Roper Center is the largest library of public opinion data in the world. An online polling magazine and the methodology and findings of many surveys may be found at http://www .ropercenter.uconn.edu. Report on an article or study of your choice.

Chapter 9: Survey Research: An Overview 203

The SAT and ACT college entrance exams once were completely multiple choice, but both tests recently began including an essay portion (which is

optional for the ACT). Some researchers have investigated how the essay tests are used by one group they serve: the admissions offices of the colleges that look at test results during the selection process.17

Early survey research suggests that some admissions officers har- bor doubts about the essay tests. ACT, Inc. reported that among the schools it surveyed, only about one-fifth are requiring that applicants take the writing portion of the exam. Another one-fifth merely rec- ommend (but don’t require) the essay.

Kaplan, Inc., which markets test preparation services, conducted surveys as well. Kaplan asked 374 colleges whether they would be using the SAT writing test in screening candidates. Almost half (47 percent) said they would not use the essay at all. Another

22 percent said they would use it but give it less weight than the math and verbal SAT scores.

Kaplan also surveys students who take the exams for which it provides training. On its website, the company says, “More than 25 percent of students ran out of time on the essay!”

Questions 1. What survey objectives would ACT have in asking colleges

how they use its essay test? What objectives would Kaplan have for its survey research?

2. If you were a marketer for the College Board (the SAT’s com- pany) or ACT, Inc., what further information would you want to gather after receiving the results described here?

3. What sources of error or response bias might be present in the surveys described here?

SAT and ACT Writing Tests CASE 9.1

The Walker Information Group is among the largest research companies in the world. Walker’s clients include many Fortune 500 and blue chip industry

leaders such as Cummins Engine Company, Lenscrafters, Continen- tal Cablevision, Florida Power and Light, and Oglethorpe Power Corporation.

The Indianapolis-based company was founded in 1939 as a field interviewing service by Tommie Walker, grandmother of Steven F. Walker, the current chairman and chief executive officer of the organization. In the 1920s Tommie Walker’s late husband worked for a bank that was considering sponsoring an Indianapolis radio show featuring classical music. The bank wanted to know who was listen- ing to this show. Tommie was hired to do the interviewing, and she threw herself into the work. After that, referrals brought her more interviewing work for surveys. During an interview with a woman whose husband was a district sales manager for the A&P grocery chain, she learned that A&P was looking for a surveyor in the Midwest. A&P’s sales manager liked Tommie, but wouldn’t hire any- one without a formal company, a field staff, and insurance. Tommie founded Walker Marketing Research on October 20, 1939, and her business with A&P lasted 17 years.

Today, the Walker Information Group specializes in business, health care, and consumer research, as well as database marketing. The com- pany is organized around several services that it provides to its clients.

Walker Information conducts traditional market research ser- vices that range from questionnaire design and data collection

to advanced analysis and consultation. Walker has expertise in helping companies measure how their actions are perceived by the audiences most important to them, and how these perceptions affect their image, reputation, corporate citizenship, recruiting, sales, and more.

Walker Global Reach spans across the globe, with partnerships in virtually every major business market. It is the first international network of professional research and consulting businesses dedi- cated to customer satisfaction measurement and management. The Global Reach program assures that multicountry customer satisfac- tion research is consistent by taking into account local conditions and cultural norms. Network members are trained to use consistent methods that allow standardization and comparability of information from country to country.

Questions 1. What type of custom survey research projects might Walker

Information, Inc. (www.walkerinfo.com) conduct for its clients?

2. What stages are involved in conducting a survey? For which stages might a client company hire a research supplier like Walker Research? Data Source?

3. What is the purpose of customer satisfaction measurement? 4. What measures, other than findings from surveys, might a com-

pany use to evaluate the effectiveness of a total quality manage- ment program?

The Walker Information Group CASE 9.2

204 Part 3: Research Methods for Collecting Primary Data

205

10 C H A P T E R

After studying this chapter, you should be able to

1. Summarize ways researchers gather information through interviews

2. Compare the advantages and disadvantages of conducting door-to-door, mall intercept, and telephone interviews

3. Evaluate the advantages and disadvantages of distributing questionnaires through the mail, the Internet, and by other means

4. Discuss the importance of pretesting questionnaires

5. Describe ethical issues that arise in survey research

LEARNING OUTCOMES

Survey Research Communicating with Respondents

T he ubiquitous use of cell phones as communi- cation and information management devices has led to new ways to capture the opinions of others. In particular, the widespread use of SMS

(short message service) text messaging has not been lost on the business research market.

Mobile surveying technologies now integrate SMS text messaging with electronic surveys. Recipients of a mobile survey receive a text message, where they can answer single or mul- tiple choice questions, or even provide open-ended responses to questions, anytime or anywhere. The use of these types of “instant feedback” survey responses can have many different business applications.

For example, business researchers may wish to capture consumer reactions to products over time, or may wish to get an instant “first impression” as they use a product initially. Perhaps a firm wishes to capture instant feedback from a train- ing exercise, or may wish to capture or understand respondent attitudes to a particular part of a meeting or event. In fact, cur- rent researchers interested in experiential surveying use mobile surveys to capture people’s feelings at that particular instant, and thus can create a longitudinal understanding of people’s attitudes and emotional states over time.

Mobile surveying is an efficient method to capture data on respondents, no matter where they are. Texting is here to stay— perhaps the next time you see someone furiously texting on their cell phone, they are responding to a mobile survey “on the go”!

Chapter Vignette:

Mobile Surveys Catching On, and Catching Respondents “On the Go”!

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Introduction During most of the twentieth century, obtaining survey data involved inviting individuals to answer questions asked by human interviewers (interviews) or questions they read themselves (questionnaires). Interviewers communicated with respondents face-to-face or over the telephone, or respondents filled out self-administered paper questionnaires, which were typically distributed by mail. These media for conducting surveys remain popular with business researchers. However, as the preceding vignette suggests, digital technology is having a profound impact on society in general and on business research in particular. Its greatest impact is in the creation of new forms of communications media.

Interviews as Interactive Communication When two people engage in a conversation, human interaction takes place. Human interac- tive media are a personal form of communication. One human being directs a message to and interacts with another individual (or a small group). When most people think of interview- ing, they envision two people engaged in a face-to-face dialogue or a conversation on the telephone.

Electronic interactive media allow researchers to reach a large audience, personalize individual messages, and interact using digital technology. To a large extent, electronic interactive media are controlled by the users themselves. No other human need be present. Survey respondents today are not passive audience members. They are actively involved in a two-way communication using electronic interactive media.

The Internet has radically altered many organizations’ research strategies, providing a promi- nent example of electronic interactive media. Consumers determine what information they will be exposed to by choosing what sites to visit and by blocking or closing annoying pop-up ads. Electronic interactive media also include CD-ROM and DVD materials, touch-tone telephone sys-

tems, touch-screen interactive kiosks in stores, and other forms of digital technology.

Noninteractive Media The traditional questionnaire received by mail and completed by the respondent does not allow a dialogue or an exchange of information pro- viding immediate feedback. So, from our perspective, self-administered questionnaires printed on paper are noninteractive. This fact does not mean that they are without merit, just that this type of survey is less flex- ible than surveys using interactive communication media.

Each technique for conducting surveys has merits and shortcomings. The purpose of this chapter is to present the advantages and disadvantages of different approaches to data collection and explain when researchers should use different types of surveys. The chapter begins with a discus- sion of surveys that use live interviews. Then we turn to noninteractive, self-administered questionnaires. Finally, we discuss how the Internet and digital technology are used in survey research.

Personal Interviews To conduct interviews, the researcher may communicate with indi- viduals in person by going door-to-door or intercepting them in shop- ping malls, or interviews may take place over the telephone. While ©

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206 • PART THREE Research Methods for Collecting Primary Data

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traditionally researchers recorded interview results using paper and pencil, computers now commonly support survey research. In this section, we examine the general characteristics of face-to-face personal interviews, then compare the characteristics of door-to-door personal interviews and personal interviews conducted in shopping malls. The next section examines telephone interviews.

A personal interview is a form of direct communication in which an interviewer asks respon- dents questions face-to-face. This versatile and flexible method is a two-way conversation between interviewer and respondent.

Gathering information through face-to-face contact with individuals goes back many years. Periodic censuses were used to set tax rates and aid military conscription in the ancient empires of Egypt and Rome.1 During the Middle Ages, the merchant families of Fugger and Roths- child prospered in part because their far-flung organizations enabled them to get information before their competitors could.2 Today, survey researchers typically present themselves in shopping centers and street corners throughout the United States and announce, “Good afternoon, my name is _________________. I am with _________________ Company, and we are conducting a survey on _________________.”

Advantages of Personal Interviews Business researchers find that personal interviews offer many unique advantages. One of the most important is the opportunity for detailed feedback.

Opportunity for Feedback Personal interviews, similar to those mentioned in the Research Snapshot “The Challenge of Assessing Adult Literacy,” provide the opportunity for feedback and clarification. For example, if a consumer is reluctant to provide sensitive information, the interviewer may offer reassurance that his or her answers will be strictly confidential. Personal interviews offer the lowest chance that respondents will misinterpret questions, because an interviewer who senses confusion can clarify the instruction or questions. Circumstances may dictate that at the conclusion of an inter- view, the respondent be given additional information concerning the purpose of the study. This clarification is easily accomplished with a personal interview. If the feedback indicates that some question or set of questions is particularly confusing, the researcher can clarify those questions with the respondent and make changes that make the questionnaire easier to understand for future interviews.

personal interview

Face-to-face communication in which an interviewer asks a respondent to answer questions.

SURVEY

THIS! Surveys can be classified a number of different ways. For instance, they can be interactive or noninteractive. They can also be classified based on the media used to collect the information. How would you describe the “Survey This!” survey approach? Is it interactive? Also, consider the likely response rate if this survey were used to randomly study college students like yourself via e-mail solicitation. Try sending the link to the survey to 10 friends and check to see how many actually respond to it (friends not taking this class with you). What factors of this survey contribute to either a relatively high or relatively low response rate?

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Probing Complex Answers Another important characteristic of personal interviews is the opportunity to follow up by probing. If a respondent’s answer is too brief or unclear, the researcher may request a more comprehensive or clearer explanation. In probing, the interviewer asks for clarification with standardized ques- tions such as “Can you tell me more about what you had in mind?” (See Chapter 7 on qualitative research for an expanded discussion of probing.) Although interviewers are expected to ask ques- tions exactly as they appear on the questionnaire, probing allows them some flexibility. Depending on the research purpose, personal interviews vary in the degree to which questions are structured and in the amount of probing required. The personal interview is especially useful for obtaining unstructured information. Skilled interviewers can handle complex questions that cannot easily be asked in mail surveys or even on the telephone.

Length of Interview If the research objective requires an extremely lengthy questionnaire, personal interviews may be the only option. A general rule of thumb on mail surveys is that they should not exceed six pages, and telephone interviews typically last less than 10 minutes. In contrast, a personal interview can be much longer, perhaps an hour and a half, as was the case for the U.S. National Adult Literacy Assessment.

The need to understand and address functional adult literacy is an important one. The term func- tionally literate is often misunderstood—in reality, it is the degree to which adults can adequately “function” with written materials they are exposed to in their daily lives. Illiteracy creates clear and in some instances not-so-clear challenges for adults. While it is clear that a person’s literacy is certainly tied to their ability to obtain a good job, they are also challenged by the everyday use of printed and written information such as newspapers, bank statements, and even medication prescription instructions. The question is how to understand the level of illiteracy in a population when those individuals you are most interested in (i.e., the adult illiterate) may not or cannot respond to a written questionnaire in the first place.

In the United States, the 1992 National Adult Literacy Survey (NALS) and the 2003 National Assessment of Adult Literacy (NAAL) recognized this challenge and developed one of the most comprehensive personal interview and assessment programs ever attempted. The NAAL conducted in-home personal interviews that took approximately 90 minutes to complete with a stratified sample of over eighteen thousand adults. During these inter- views, a multistage assessment was conducted. Respondents were given a short and very simple screening questionnaire, which would determine if they could proceed. If the respondent could not complete the short screening tool, they were not required to go further with the literacy assessment, and were scored on a normed scale as functionally illiterate. For those that

could complete the screening questions, example materials— items such as store coupons, telephone bills, and driving directions—were carefully presented, with the respondents answering a questionnaire related to those everyday items.

The direct and interactive nature of the assessment was critically important to the NAAL’s success. As a result, the U.S. Department of Education was able to comprehensively under- stand the degree and trends regarding adult illiteracy within the United States, and ultimately capture demographic and socioeconomic characteristics of those adults who were chal- lenged from a literacy standpoint. It was the hard work of highly trained personal interviewers that helped the Department of Education address this critical national question. Considering the time and effort required to complete these studies, it is no surprise that they are attempted only about once a decade!

Source: National Assessment of Adult Literacy, http://nces.ed.gov/NAAL/.

The Challenge of Assessing Adult Literacy

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RESEARCH SNAPSHOT

However, the longer the interview, no matter what the form, the more the respondent should be compensated for their time and participation. Researchers should also be clear about how long partic- ipation should take in the opening dialogue requesting participation. Online surveys should include a completion meter that shows the progress a respondent has made toward completing the task.

Completeness of Questionnaire The social interaction between a well-trained interviewer and a respondent in a personal interview increases the likelihood that the respondent will answer all the items on the questionnaire. The respondent who grows bored with a telephone interview may terminate the interview at his or her discretion simply by hanging up the phone. Self-administration of a mail questionnaire requires even more effort by the respondent. Rather than write lengthy responses, the respondent may sim- ply skip some of the questions. Item nonresponse—failure to provide an answer to a question—is least likely to occur when an experienced interviewer asks questions directly.

Props and Visual Aids Interviewing respondents face-to-face allows the investigator to show them new product samples, details of health care options, mock-ups of proposed advertising, or other visual aids. When Lego Group wanted to introduce new train model sets for its famous building bricks, the company tar- geted adults who build complex models with its product. The company invited adults who were swapping ideas at the Lego website to visit the New York office, where they viewed ideas and provided their opinions. The respondents wound up rejecting all the company’s ideas, but they suggested something different: the Santa Fe Super Chief set, which sold out within two weeks, after being promoted only by enthusiastic word of mouth.3 This research could not have been done in a telephone interview, mail survey, or web questionnaire.

Research that uses visual aids has become increasingly popular with researchers who investi- gate film concepts, advertising problems, and moviegoers’ awareness of performers. Research for movies often begins by showing respondents videotapes of the prospective cast. After the movie has been produced, film clips are shown and interviews conducted to evaluate the movie’s appeal, and especially to identify which scenes to emphasize in advertisements.

High Participation Although some people are reluctant to participate in a survey, the presence of an interviewer gen- erally increases the percentage of people willing to complete the interview. Respondents typically are required to do no reading or writing—all they have to do is talk. Many people enjoy sharing information and insights with friendly and sympathetic interviewers. People are often more hesitant to tell a person “no” face-to-face than they are over the phone or through some impersonal contact.

Disadvantages of Personal Interviews Personal interviews also have some disadvantages. Respondents are not anonymous and as a result may be reluctant to provide confidential information to another person. Suppose a survey asked top executives, “Do you see any major internal instabilities or threats (people, money, material, and so on) to the achievement of your organization’s objectives?” Many managers may be reluctant to answer this sensitive question honestly in a personal interview in which their identities are known.

Interviewer Influence Some evidence suggests that demographic characteristics of the interviewer influence respondents’ answers and may introduce bias. For example, one research study revealed that male interviewers produced larger amounts of interviewer variance than female interviewers in a survey in which 85 percent of the respondents were female. Older interviewers who interviewed older respondents produced more variance than other age combinations, whereas younger interviewers who inter- viewed younger respondents produced the least variance.

item nonresponse

Failure of a respondent to provide an answer to a survey question.

CHAPTER 10 Survey Research: Communicating with Respondents • 209

Differential interviewer techniques may be a source of bias. The rephrasing of a question, the interviewer’s tone of voice, and the interviewer’s appearance may influence the respondent’s answer. Consider the interviewer who has conducted 100 personal interviews. During the next one, he or she may lose concentration and either selectively perceive or anticipate the respon- dent’s answer. The interpretation of the response may differ somewhat from what the respondent intended. Typically, the public thinks of the person who does marketing research as a dedicated scientist. Unfortunately, some interviewers do not fit that ideal. Considerable interviewer vari- ability exists. Cheating is possible; interviewers may cut corners to save time and energy, faking parts of their reports by dummying up part, or all, of the questionnaire. Control over interview- ers is important to ensure that difficult, embarrassing, or time-consuming questions are handled properly.

Lack of Anonymity of Respondent Because a respondent in a personal interview is not anonymous and may be reluctant to provide confidential information to another person, researchers often spend considerable time and effort to phrase sensitive questions to avoid social desirability bias. For example, the interviewer may show the respondent a card that lists possible answers and ask the respondent to read a category number rather than be required to verbalize sensitive answers.

Cost Personal interviews are expensive, generally substantially more costly than mail, telephone, or Internet surveys. The geographic proximity of respondents, the length and complexity of the ques- tionnaire, and the number of people who are nonrespondents because they could not be contacted (not-at-homes) will all influence the cost of the personal interview.

Door-to-Door Interviews and Shopping Mall Intercepts Personal interviews may be conducted at the respondents’ homes or offices or in many other places. Increasingly, personal interviews are being conducted in shopping malls. Mall intercept interviews allow many interviews to be conducted quickly. Often, respondents are intercepted in public areas of shopping malls and then asked to come to a more private research facility within the mall to taste new food items or to view advertisements. The locale for the interview generally influences the participation rate, and thus the degree to which the sample represents the general population.

Door-to-Door Interviews The presence of an interviewer at the door generally increases the likelihood that a person will be willing to complete an interview. Because door-to-door interviews increase the participation rate, they may provide a more representative sample of the population than mail questionnaires. For example, response rates to mail surveys are substantially lower among Hispanics, whether the ques- tionnaire is printed in English or Spanish.4 People who do not have landline telephones, who have unlisted telephone numbers, or who are otherwise difficult to contact may be better reached using door-to-door interviews. However, door-to-door interviews may underrepresent some groups and overrepresent others based on the geographic areas covered.

Door-to-door interviews may exclude individuals who live in multiple-dwelling units with security systems, such as high-rise apartment dwellers, or executives who are too busy to grant personal interviews during business hours. Other people, for security reasons, simply will not open the door when a stranger knocks. As seen in the Research Snapshot “Being Good Neighbors Means Learning about Them First,” elderly adults, or people in retirement dwellings, may also be excluded. Telephoning an individual in one of these subgroups to make an appointment may make the total sample more representative. However, obtaining a representative sample of this security- conscious subgroup based on a listing in the telephone directory may be difficult. For these reasons, door-to-door interviews are becoming a thing of the past.

door-to-door interviews

Personal interviews conducted at respondents’ doorsteps in an effort to increase the

participation rate in the survey.

210 • PART THREE Research Methods for Collecting Primary Data

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Callbacks When a person selected to be in the sample cannot be contacted on the first visit, a systematic procedure is normally initiated to call back at another time. Callbacks, or attempts to recontact individuals selected for the sample, are the major means of reducing nonresponse error. Calling back a sampling unit is more expensive than interviewing the person the first time around, because subjects who initially were not at home generally are more widely dispersed geographically than the original sample units. Callbacks in door-to-door interviews are important because not- at- home individuals (for example, working parents) may systematically vary from those who are at home (nonworking parents, retired people, and the like).

Mall Intercept Interviews Personal interviews conducted in shopping malls are referred to as mall intercept interviews, or shopping center sampling. Interviewers typically intercept shoppers at a central point within the mall or at an entrance. The main reason mall intercept interviews are conducted is because their costs are lower. No travel is required to the respondent’s home; instead, the respondent comes to the interviewer, and many interviews can be conducted quickly in this way.

A major problem with mall intercept interviews is that individuals usually are in a hurry to shop, so the incidence of refusal is high—often 50 percent or more. Yet it is standard practice for many commercial research companies, who conduct more personal interviews in shopping malls than door-to-door.

In a mall interview, the researcher must recognize that he or she should not be looking for a representative sample of the total population. Each mall has its own target market’s charac- teristics, and there is likely to be a larger bias than with careful household probability sampling. However, personal interviews in shopping malls are appropriate when the target group is a

callbacks

Attempts to recontact individuals selected for a sample who were not available initially.

mall intercept interviews

Personal interviews conducted in a shopping mall.

The use of door-to-door surveys has always represented a chal- lenge for business researchers. They require well-trained survey administrators and can cost a significant amount of money. In addition to the costs, ensuring that you have the correct popu- lation of interest complicates the process further.

These challenges are not lost on communities as well. Com- munities often have limited resources and few ways to capture the needs of its citizens. Conducting door-to-door surveys is often the only way to meet and capture the attitudes and beliefs of specific populations in a particular city or town.

In 2004–2005 St. Louis Community College at Meramec recognized an opportunity to engage their students in a service learning project and capture information from an often missed demographic in our society—the elderly. They established a door- to-door survey of older adults to support an initiative entitled Older Adults—Honoring and Caring for Our Elders. Elderly resi- dents, in particular those that live in nursing homes and assisted living facilities, may have no phones and are likely to have some challenges in filling out mail questionnaires without some assis- tance. Two students, with the support of community organizers,

mapped the neighbor- hoods and divided the city into zones to ensure that the area was covered successfully. Students conducted door-to-door surveys in the area, capturing 680 surveys from older adults.

Their hard work led to the inclusion of elderly adult needs as part of the Good Neighbor Initiative, which included pro- grams for literacy, hunger, homelessness, and health. Without going from house to house, it may not have been possible for the community to capture the specific needs of this important population in their city. In the end, students had an enlightened learning experience, and gained an understanding of the elderly adults in their own neighborhoods. Good neighbors indeed!

Source: Halsband, D., Welch, G., & Fuller, M., “Community Survey Leads to Learning from and Caring for Our Elders” (paper presented at the annual national conference for the Community College National Center for Community Engagement, May 2008). Reprinted by permission of the authors.

Being Good Neighbors Means Learning about Them First

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special market segment such as the parents of children of bike-riding age. If the respondent indicates that he or she has a child of this age, the parent can then be brought into a rented space and shown several bikes. The mall intercept interview also allows the researcher to show large, heavy, or immobile visual materials, such as a television commercial. A mall interviewer can give an individual a product to take home to use and obtain a commitment that the respondent will cooperate when recontacted later by telephone. Mall intercept interviews are also valuable when activities such as cooking and tasting of food must be closely coordinated and timed to follow each other. They may also be appropriate when a consumer durable product must be demon- strated. For example, a researcher examining perceptions of 3D television can do so relatively easily in a mall, while the effort and expense required to set up and properly display these TVs rules out in-home testing.

Global Considerations Willingness to participate in a personal interview varies dramatically around the world. For example, in many Middle Eastern countries women would never consent to be interviewed by a man. And in many countries the idea of discussing grooming behavior and personal-care products with a stranger would be highly offensive. Few people would consent to be interviewed on such topics.

The norms about appropriate business conduct also influence a manager’s willingness to pro- vide information to interviewers. For example, conducting business-to-business interviews in Japan during business hours is difficult because managers, strongly loyal to their firm, believe that they have an absolute responsibility to oversee their employees while on the job.

Telephone Interviews For several decades, landline telephone interviews were the mainstay of commercial survey research. The quality of data obtained by telephone is potentially comparable to the quality of data collected face-to-face. Respondents are more willing to provide detailed and reliable information on a variety of personal topics over the phone while in the privacy of their own homes than when answering questions face-to-face.

In-home phone surveys are still considered capable of providing fairly representative samples of the U.S. population. However, the “no-call” legislation dating back to the middle of the first decade of the twenty-first century has limited this capability somewhat. Business researchers can- not solicit information via phone numbers listed on the do-not-call registry. Thus, to the extent that consumers who place their numbers on these lists share something in common, such as a greater desire for privacy or a higher level of education, a representative sample of the general population cannot be obtained. Business researchers can obtain the do-not-call lists of phone numbers from the FTC for $62 per area code. The entire registry can be obtained for $17,050. This information can be obtained from the FTC do-not-call website shown in the screenshot. Although this may seem expensive, the FTC levies fines on the order of $10,000 per violation (per call) so obtaining the registry is a wise investment for those wishing to contact consum- ers via telephone. AT&T faced fines of over three-quarters of a million dollars for making 78 unwanted calls to 29 consumers listed on the do-not-call list. So, the Feds do take violations very seriously!

Likewise, the Canadian government has instituted a nearly identical do-not-call program. The Canadian Radio-Television and Telecommunications Commission imposes fines up to $11,000 per call for calls made to people on the Canadian do-not-call list. Other countries in Europe and elsewhere are also considering such legislation. Combined with the rapidly increasing percentage of the population that do not have landline phones, these privacy laws make telephone interviews less capable of obtaining representative samples than they once were. Often, however, a landline phone call is still the researcher’s best option.

‘‘A man’s feet should be planted in his country, but his eyes should survey the world.’’—GEORGE SANTAYANA

telephone interviews

Personal interviews conducted by telephone, the mainstay of

commercial survey research.

212 • PART THREE Research Methods for Collecting Primary Data

Mobile Phone Interviews Mobile phone interviews differ from landline phones most obviously because they are directed toward a cell phone number. However, there are other less obvious distinctions.

■ In the U.S., no telemarketing can be directed toward mobile phone numbers by law. The primary reason for enacting this law was that respondents would often have to pay to receive the call. Respondents would have to “opt in” before their phone number would be made available for such calls.

■ The recipient of a mobile phone call is even more likely to be distracted than with a call to someone’s home or office. The respondent may be driving a car, on the subway, or walking down a noisy street. Factors such as this not only make a survey potentially dangerous, but also are not conducive to a high-quality interview.

■ The area codes for mobile phones are not necessarily tied to geography. For instance, a per- son who moves from Louisiana to Illinois may choose to keep his or her old phone number. Therefore, a researcher may be unable to determine whether or not a respondent fits into the desired geographic sampling population simply by taking note of the area code.

■ The phones have varying abilities for automated responses and differing keypads. Some requests, such as “hit pound sign,” may be more difficult to do on some keypads than on others.

Phone Interview Characteristics Phone interviews in general have several distinctive characteristics that set them apart from other survey techniques. These characteristics present significant advantages and disadvantages for the researcher.

Speed One advantage of telephone interviewing is the speed of data collection. While data collection with mail or personal interviews can take several weeks, hundreds of telephone interviews can be conducted literally overnight. When the interviewer enters the respondents’ answers directly into a computer program, the data processing speeds up even more.

Cost As the cost of personal interviews continues to increase, telephone interviews are becoming com- paratively inexpensive. The cost of telephone interviews is estimated to be at least 25 percent of the cost of door-to-door personal interviews. Travel time and costs are eliminated. However, the typical Internet survey is less expensive than a telephone survey.

Absence of Face-to-Face Contact Telephone interviews are more impersonal than face-to-face interviews. This has both advan- tages and disadvantages. Respondents may answer embarrassing or confidential questions more willingly in a telephone interview than in a personal interview. However, mail and Internet surveys, although not perfect, are better media for gathering extremely sensitive information because they seem more anonymous. Some evidence suggests that people provide infor- mation on income and other financial matters only reluctantly, even in telephone interviews.

The federal government provides information for both consumers and businesses through this website.

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CHAPTER 10 Survey Research: Communicating with Respondents • 213

Such questions may be personally threatening for a variety of reasons, and high refusal rates for this type of question occur with each form of survey research.

Although telephone calls may be less threatening because the interviewer is not physically present, the absence of face-to-face contact can also be a liability. For example, the respondent cannot see that the interviewer is still writing down the previous comment and may continue to elaborate on an answer. If the respondent pauses to think about an answer, the interviewer may not realize it and may go on to the next question. Hence, there is a greater tendency for inter- viewers to record no answers and incomplete answers in telephone interviews than in personal interviews.

Cooperation One trend is very clear. In the last few decades, telephone response rates have fallen. Analysis of response rates for the long-running Survey of Consumer Attitudes conducted by the University of Michigan found that response rates fell from a high of 72 percent to 67 percent during the period from 1979 to 1996 and then even faster after 1996, dropping to 60 percent.5 Lenny Murphy of data collection firm Dialtek says he has observed a decline in survey response rates from a typical range of 30 to 40 percent in the past down to below 20 percent.6 Fewer calls are answered because more households are using caller ID and answering machines to screen their calls, and many individu- als do not pick up the phone when the display reads “out of area’’ or when an unfamiliar survey organization’s name and number appear on the display. Also, more phone lines are dedicated to fax machines and computers. However, the University of Michigan study found that the rate of refusal actually grew faster in the more recent period than the rate of not answering researchers’ calls.

Further complicating the situation is the use of mobile phone services. Regulations by the Federal Communications Commission make it illegal for researchers to use automated dialing equipment to call mobile phones. Even if researchers dial the calls by hand, they may not contact anyone who would have to pay for the call—that is, most cell phone users. U.S. households are rapidly moving away from their landlines. Recent figures show that during the last six months of 2010, nearly three out of ten American homes (29.7 percent) had only mobile phone service.7 This is an increase of over 3 percent from just the first six months of 2010. Not only are these numbers growing rapidly, but they include a sizable segment of young adults which are critically important to business researchers. As a result, the ability of telephone surveys to reach a representative sample is decreasing.

Other countries may not adopt laws restricting calls to mobile phones. In addition, consumers in other countries are more open to responding to research delivered by voice or by text messaging. Thus, the mobile phone may be a better interview tool outside of the United States than inside.

Another way to encourage participation is to send households an invitation to participate in a survey. The invitation can describe the purpose and importance of the survey and the likely dura- tion of the survey. The invitation can also encourage subjects to be available and reassure them that the caller will not try to sell anything. In a recent study comparing response rates, the rates were highest among households that received an advance letter, somewhat lower when the notice came on a postcard, and lowest when no notice was sent.8

Incentives to Repond Respondents should receive some incentive to respond. Research addresses different types of incentives. For telephone interviews, test-marketing involving different types of survey introduc- tions suggests that not all introductions are equally effective. A financial incentive or some signifi- cant chance to win a desirable prize will produce a higher telephone response rate than a simple assurance that the research is not a sales pitch, a more detailed description of the survey, or an assurance of confidentiality.9

Representative Samples Practical difficulties complicate obtaining representative samples based on listings in the telephone book. While well over 95 percent of households in the United States have telephones, close to 30 percent have only wireless phones as noted above. People without phones are more likely to be

214 • PART THREE Research Methods for Collecting Primary Data

poor, aged, rural, or living in the South. Unlisted phone numbers and numbers too new to be printed in the directory are a greater problem. People have unlisted phone numbers for two reasons:

■ They have recently moved ■ They prefer to have unlisted numbers for privacy

Individuals whose phone numbers are unlisted because of a recent move differ slightly from those with published numbers. The unlisted group tends to be younger, more urban, and less likely to own a single-family dwelling. Households that maintain unlisted phone numbers by choice tend to have higher incomes. And, as previously mentioned, a number of low-income households are unlisted by circumstance.

The problem of unlisted phone numbers can be partially resolved through the use of random digit dialing. Random digit dialing eliminates the counting of names in a list (for example, calling every fiftieth name in a column) and subjectively determining whether a directory listing is a busi- ness, institution, or legitimate household. In the simplest form of random digit dialing, telephone exchanges (prefixes) for the geographic areas in the sample are obtained. Using a table of random numbers, the last four digits of the telephone number are selected. Telephone directories can be ignored entirely or used in combination with the assignment of one or several random digits. Random digit dialing also helps overcome the problem due to new listings and recent changes in numbers. Unfortunately, the refusal rate in commercial random digit dialing studies is higher than the refusal rate for telephone surveys that use only listed telephone numbers.

Callbacks One way researchers can try to improve telephone survey response rate is to make multiple attempts. An unanswered call, a busy signal, or a respondent who is not at home requires a callback. Researchers should try callbacks at different times and on different days to maximize the chance of reaching the survey target. Telephone callbacks are obviously much easier to make than callbacks in personal interviews.

Limited Duration Respondents who run out of patience with the interview can merely hang up. To encour- age participation, interviews should be relatively short. Refusal to cooperate with interviews is directly related to interview length. A major study of survey research found that interviews of 5 minutes or less had a refusal rate of 21 percent; interviews of between 6 and 12 minutes had a 41 percent refusal rate; and interviews of 13 minutes or more had a 47 percent refusal rate. In unusual cases, a few highly interested respondents will put up with longer interviews. A good rule of thumb is to keep telephone interviews under 15 minutes long. In general, 30 minutes is the maximum amount of time most respondents will spend unless they are highly interested in the survey subject.

Lack of Visual Medium Because visual aids cannot be used in telephone interviews, this method is not appropriate for packaging research, copy testing of television and print advertising, and concept tests that require visual materials. Likewise, certain attitude scales and measuring instruments, such as the semantic differential (described in Chapter 14), require the respondent to see a graphic scale, so they are difficult to use over the phone.

Central Location Interviewing Research agencies or interviewing services typically conduct all telephone interviews from a cen- tral location. Such central location interviewing allows firms to hire a staff of professional inter- viewers and to supervise and control the quality of interviewing more effectively. When telephone interviews are centralized and computerized, an agency or business can benefit from additional cost economies.

random digit dialing

Use of telephone exchanges and a table of random numbers to contact respondents with unlisted phone numbers.

central location interviewing

Telephone interviews conducted from a central location allowing firms to hire a staff of professional interviewers and to supervise and control the quality of interviewing more effectively.

CHAPTER 10 Survey Research: Communicating with Respondents • 215

216

Computer-Assisted Telephone Interviewing Computer technology allows responses to telephone interviews to be entered directly into the computer in a process known as computer-assisted telephone interviewing (CATI). Telephone interviewers are seated at computer terminals, where monitors display the questionnaires, one question at a time, along with precoded possible responses to each question. The interviewer reads each question as it appears on the screen. When the respondent answers, the interviewer enters the response directly into the computer, and it is automatically stored in the computer’s memory. The computer then displays the next question on the screen. Computer-assisted telephone inter- viewing requires that answers to the questionnaire be highly structured. If a respondent gives an unacceptable answer (that is, one not precoded and programmed), the computer will reject it (See Research Snapshot “Automated Phone Surveys of Teens”).

Computer-assisted telephone interviewing systems include telephone management systems that select phone numbers, dial the numbers automatically, and perform other labor-saving functions. These systems can automatically control sample selection by randomly generating names or fulfilling a sample quota. A computer can generate an automatic callback schedule. A typical call management system might schedule recontact attempts to recall no answers after two hours and busy numbers after 10 minutes and allow the interviewer to enter a more favorable time slot (day and hour) when a respondent indicates that he or she is too busy to be interviewed. Software also provides researchers daily status reports on the number of completed interviews relative to quotas. CATI interviews can also be conducted by a prerecorded voice with the respondent answering by punching buttons on the phone.

Computerized Voice-Activated Telephone Interview Current technology combines computerized telephone dialing and voice-activated computer mes- sages to allow researchers to conduct telephone interviews without human interviewers. However, researchers have found that computerized voice-activated telephone interviewing works best with very short, simple questionnaires. One system includes a voice-synthesized module controlled by a microprocessor. With it the sponsor is able to register a caller’s single response such as “true/false,” “yes/no,” “like/dislike,” or “for/against.” This type of system has been used by television and radio stations to register callers’ responses to certain issues. Computerized systems typically begin with an

computer-assisted telephone interviewing

(CATI)

Technology that allows answers to telephone

interviews to be entered directly into a computer for

processing.

Automatic telephone surveys are a good way to reach all mem- bers of the family, not just the head of the household. What if you wanted to ask questions about holiday shopping, what’s for din- ner, or what kind of vacation the family would like? A short tele- phone survey may be the answer. One advantage is that no “real person” has to hear the answers to potentially sensitive questions.

Computer-assisted telephone interviewing (CATI) and com- puterized self-interviewing, in which the subjects listen to pre- recorded questions and then respond by entering answers with the telephone’s keypad, have been used to ask the “teen” in the house about smoking. The researchers predicted that the young people would be more likely to say they smoke in the self- administered survey than in response to a live interviewer, because pressing keys on the keypad would feel more confidential.

The interviewers were right. In the self- administered survey, the teens were more likely to say they had smoked in the past

30 days or, if they had not smoked, to lack a firm commit- ment not to smoke in the future. Many of them indicated a parent was present while they answered the questions, and when they did, their responses were less likely to indicate smoking desire or susceptibility. This pat- tern suggests that they might be underreporting their smoking behavior. These findings encourage researchers to be attentive to confidentiality when working with teenage subjects.

Sources: “Survey: Consumers Say ‘Yes’ to Holiday Shopping,” Stores 83, no. 12 (December 2001), 18; also based on Moskowitz, Joel M., “Assessment of Cigarette Smoking and Smoking Susceptibility among Youth: Telephone Computer-Assisted Self-Interviews versus Computer-Assisted Telephone Interviews,” Public Opinion Quarterly 68 (Winter 2004), 565–587.

Automated Phone Surveys of Teens

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announcement that the respondent is listening to a recorded message. If respondents do not answer the first two questions, the computer disconnects and goes to the next call. With this process, the entire data collection process can be automated because a recorded voice is used to both ask the questions and record answers.

Global Considerations Different cultures often have different norms about proper telephone etiquette. For example, business-to-business researchers have learned that Latin American businesspeople will not open up to strangers on the telephone. So, researchers in Latin America usually find personal interviews (face-to-face) more suitable than telephone surveys. In Japan, respondents consider it ill-mannered if telephone interviews last more than 20 minutes.

Self-Administered Questionnaires Many surveys do not require an interviewer’s presence. Researchers distribute questionnaires to consumers through the mail and in many other ways (see Exhibit 10.1). They insert question- naires in packages and magazines. They may place questionnaires at points of purchase or in high-traffic locations in stores or malls. Business researchers may fax or text questionnaires to individuals. Questionnaires can be printed on paper, but they may also be posted on the Internet or sent via e-mail. No matter how the self-administered questionnaires are distributed, they are different from interviews because the respondent takes responsibility for reading and answering the questions.

self-administered questionnaires

Surveys in which the respondent takes the responsibility for reading and answering the questions.

mail survey

A self-administered questionnaire sent to respondents through the mail.

EXHIBIT 10.1

Self-Administered Questionnaires Can Be Either Printed or Electronic

Self-Administered Questionnaires

Mail In-person drop-off

Paper Questionnaires Electronic Questionnaires

Inserts Fax Internet website

E-mail Interactive kiosk

Mobile phones

Self-administered questionnaires present a challenge to the researcher because they rely on the clarity of the written word rather than on the skills of the interviewer. The nature of self- administered questionnaires is best illustrated by explaining mail questionnaires.

Mail Questionnaires A mail survey is a self-administered questionnaire sent to respondents through the mail. This paper-and-pencil method has long been the mainstay of business researchers and has several advan- tages and disadvantages.

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CHAPTER 10 Survey Research: Communicating with Respondents • 217

Geographic Flexibility Mail questionnaires can reach a geographically dispersed sample simultaneously because interviewers are not required. Respondents (such as farmers) who are located in isolated areas or those (such as executives) who are otherwise difficult to reach can easily be contacted by mail. For example, a phar- maceutical firm may find that doctors are not available for personal or telephone interviews. However, a mail survey can reach both rural and urban doctors who practice in widely dispersed geographic areas.

Cost Mail questionnaires are relatively inexpensive compared with personal interviews, though they are not cheap. Most include follow-up mailings, which require additional postage and printing costs. And it usually isn’t cost-effective to try to cut costs on printing—questionnaires photocopied on low-grade paper have a greater likelihood of being thrown in the wastebasket than those prepared with more expensive, high-quality printing. The low response rates typically associated with mail surveys also contributes to the cost.

Respondent Convenience Mail surveys and other self-administered questionnaires can be filled out when the respondents have time, so respondents are more likely to take time to think about their replies. Many hard- to-reach respondents place a high value on convenience and thus are best contacted by mail. In some situations, particularly in business-to-business research, mail questionnaires allow respondents to collect facts, such as employment statistics, that they may not be able to recall without check- ing. Being able to check information by verifying records or, in household surveys, by consulting with other family members should provide more valid, factual information than either personal or telephone interviews would allow. For example, a human resource manager may find a mail survey regarding different retirement investment plans better allows the employee to share the informa- tion with their spouse before answering. A catalog retailer may use mail surveys to estimate sales volume for catalog items by sending a mock catalog as part of the questionnaire. Respondents would be asked to indicate how likely they would be to order selected items.

Anonymity of Respondent In the cover letter that accompanies a mail or self-administered questionnaire, researchers almost always state that the respondents’ answers will be confidential. Respondents are more likely to pro- vide sensitive or embarrassing information when they can remain anonymous. For example, a mail survey may be better than an in-person interview regarding a respondent’s debt, income, political views, sexual orientation, or other more personal information.

Anonymity can also reduce social desirability bias. People are more likely to agree with con- troversial issues, such as extreme political candidates, when completing self-administered question- naires than when speaking to interviewers on the phone or at their doorsteps.

Absence of Interviewer Although the absence of an interviewer can induce respondents to reveal sensitive or socially unde- sirable information, this lack of personal contact can also be a disadvantage. Once the respondent receives the questionnaire, the questioning process is beyond the researcher’s control. Although the printed stimulus is the same, each respondent will attach a different personal meaning to each ques- tion. Selective perception operates in research as well as in advertising. The respondent does not have the opportunity to question the interviewer. Problems that might be clarified in a personal or telephone interview can remain misunderstandings in a mail survey. There is no interviewer to probe for additional information or clarification of an answer, and the recorded answers must be assumed to be complete.

With mail surveys, respondents also have the opportunity to read the entire questionnaire before they answer individual questions. Often the text of a later question will provide information that affects responses to earlier questions.

218 • PART THREE Research Methods for Collecting Primary Data

Standardized Questions Mail questionnaires typically are highly standardized, and the questions are quite structured. Questions and instructions must be clear-cut and straightforward. Ambiguous questions only cre- ate additional error. Interviewing allows for feedback from the interviewer regarding the respon- dent’s comprehension of the questionnaire. An interviewer who notices that the early respondents are having some difficulty understanding a question can report this fact to the research analyst so that revisions can be made. With a mail survey, however, once the questionnaires are mailed, it is difficult to change the format or the questions.

Time Is Money If time is a factor in management’s interest in the research results, or if attitudes are rapidly chang- ing (for example, political polling prior to elections), mail surveys may not be the best communi- cation medium. A minimum of two or three weeks is necessary for receiving the majority of the responses. Follow-up mailings, which usually are sent when the returns stop trickling in, require an additional two or three weeks. The time between the first mailing and the cut-off date (when questionnaires will no longer be accepted) normally is six to eight weeks. In a regional or local study, personal interviews can be conducted more quickly. However, conducting a national study by mail might be substantially faster than conducting personal interviews across the nation.

Length of Mail Questionnaire Mail questionnaires vary considerably in length, ranging from extremely short postcard question- naires to multipage booklets that require respondents to fill in thousands of answers. A general rule of thumb is that a mail questionnaire should not exceed six pages in length. When a question- naire requires a respondent to expend a great deal of effort, an incentive is generally required to induce the respondent to return the questionnaire. The following sections discuss several ways to increase response rates even when questionnaires are longer than average.

Response Rates The major limitations of mail questionnaires relate to response problems. Virtually all question- naires that arrive via bulk mail are likely to get thrown away. Questionnaires that are boring, unclear, or too complex are even more likely to get thrown in the wastebasket. A poorly designed mail questionnaire may be returned by less than 5 percent of those sampled (that is, a 5 percent response rate). The basic calculation for obtaining a response rate is to count the number of ques- tionnaires returned or completed, then divide the total by the number of eligible people who were contacted or requested to participate in the survey. Typically, the number in the denominator is adjusted for faulty addresses and similar problems that reduce the number of eligible participants.

A researcher’s major fear with low response rate is that respondents who complete the ques- tionnaire may not be typical of all people in the sample. Individuals with a special interest in the topic are more likely to respond to a mail survey than those who are indifferent. Furthermore, a researcher has no assurance that the intended subject is the person who fills out the question- naire. The wrong person answering the questions may be a problem when surveying corporate executives, physicians, and other professionals, who may pass questionnaires on to subordinates to complete. While this issue is not unique to snail mail surveys (electronic surveys may suffer from the same issue), it is likely more pronounced with mail questionnaires.

Evidence suggests that cooperation and response rates rise as home value increases. Mail survey respondents tend to be better educated than nonrespondents. If they return the questionnaire at all, poorly educated respondents who cannot read and write well may skip open-ended questions to which they are required to write out their answers. Despite a researcher’s best attempts, rarely will a mail survey have a 50 percent or greater response rate. However, the use of follow-up mailings and other techniques may increase the response rate to an acceptable percentage. The lower the response rate, the greater the concern that the resulting sample will not adequately represent the population.

response rate

The number of questionnaires returned or completed divided by the number of eligible people who were asked to participate in the survey.

CHAPTER 10 Survey Research: Communicating with Respondents • 219

Increasing Response Rates for Mail Surveys Nonresponse error is always a potential problem in business research, and is especially an issue with mail surveys. Individuals who are interested in the general subject of the survey are more likely to respond than those with less interest or little experience. Thus, people who hold extreme positions on an issue are more likely to respond than individuals who are largely indifferent to the topic. To minimize this bias, business researchers have developed a number of techniques to increase the response rate to mail surveys. For example, almost all surveys include postage-paid return enve- lopes. Using a stamped return envelope instead of a business reply envelope increases response rates even more.10 Designing and formatting attractive questionnaires and wording questions so that they are easy to understand also help ensure a good response rate. However, special efforts may be required even with a sound questionnaire. Several of these methods are discussed in the following subsections.

Cover Letter A cover letter that accompanies a questionnaire or is printed on the first page of the question- naire booklet is an important means of inducing a reader to complete and return the question- naire. Exhibit 10.2 illustrates a cover letter and some of the points considered by a research professional to be important in gaining respondents’ attention and cooperation. The first para- graph of the letter explains why the study is important. The basic appeal alludes to the social usefulness of responding. Two other frequently used appeals are asking for help (“Will you do us a favor?”) and the egotistical appeal (“Your opinions are important!”). Most cover letters prom- ise confidentiality, invite the recipient to use an enclosed postage-paid reply envelope, describe any incentive or reward for participation, explain that answering the questionnaire will not be difficult and will take only a short time, and describe how the person was scientifically selected for participation.

A personalized letter addressed to a specific individual shows the respondent that he or she is important. Including an individually typed cover letter on letterhead rather than a printed form is an important element in increasing the response rate in mail surveys.11

Money Helps The respondent’s motivation for returning a questionnaire may be increased by offering monetary incentives or premiums. Although pens, lottery tickets, and a variety of premiums have been used, monetary incentives appear to be the most effective and least biasing incentive. Money attracts attention and creates a sense of obligation. Perhaps for this reason, monetary incentives work for all income categories. Often, cover letters try to boost response rates with messages such as, “We know that the attached dollar cannot compensate you for your time but accept it as a token of our appreciation. Please buy yourself a cup of coffee or a soft drink as you complete the survey.” Response rates increase dramatically when the monetary incentive is to be sent to a charity of the respondent’s choice rather than directly to the respondent.

Interesting Questions The topic of the research—and thus the point of the questions—cannot be manipulated without changing the definition of the research problem. However, certain interesting questions can be added to the questionnaire, perhaps at the beginning, to stimulate respondents’ interest and to induce cooperation. By including questions that are of little concern to the researchers but that the respondents want to answer, the researchers may give respondents who are indifferent to the major questions a reason for responding.

Follow-ups Most mail surveys generate responses in a pattern like that shown in Exhibit 10.3, which graphs the cumulative response rates for two mail surveys. The response rates are relatively high for the first two weeks (as indicated by the steepness of each curve), then the rates gradually taper off.

cover letter

Letter that accompanies a questionnaire to induce the

reader to complete and return the questionnaire.

220 • PART THREE Research Methods for Collecting Primary Data

EXHIBIT 10.2

A Cover Letter Requesting Participation in a Survey

Market Research Leaders

Component: → 111 Eustice Square, Terroir, IL 39800-7600

Respondent’s Address

→ Mr. Griff Mitchell 821 Shrewsbury Ave Hector Chase, LA 70809

Dear Mr. Mitchell:

Request/Time Involved

→ We’d like your input as part of a study of family media habits. This study is not involved in any attempt to sell anything. Rather, the results will help provide better media options for families. The survey typically takes about 12 minutes to complete.

Selection Method

→ You were selected based on a random sample of home owners living in the 70809 zip code.

Reason to Respond

→ We need your opinion about many important issues involving the way modern families interact with various media including print, television, radio, and Internet sources. Companies need input to create the most appealing and useful options for consumers; and local, state, and federal agencies need input to know what types of regulations, if any, are most appropriate. Without the opinions of people like you, many of these key issues will likely not be resolved.

Confidentiality/ IRB Approval

→ The information you provide, just as with all the information collected within the scope of this project, will be entirely confidential. It will only be used for the purpose of this research and no individuals will be identified within the data. Additionally, this survey and the request for you to participate has been reviewed and approved by the Institutional Review Board of MKTR, which monitors all research conducted and assures that procedures are consistent with ethical guidelines for federally funded research (although this particular project is not funded federally). If you have any questions, the MKTR IRB can be contacted at (888) 555-8888.

Incentive → Your response will improve the media choices that your family faces. In addition, a $10 check that you can cash at your personal bank is included as a token of our appreciation.

Willingness to Answer Questions

→ Additionally, you can direct any questions about this project directly to me. The contact information is available at the top of this letter. Additionally, we will be happy to provide you a summary of the results. Simply complete the enclosed self-addressed postcard and drop it in the mail or include it with the completed questionnaire. A self-addressed, postage paid reply envelope is included for your return.

Thanks → Again, thank you so much for your time and for sharing your opinions.

Signature →

After responses from the first wave of mailings begin to trickle in, most studies use a follow-up letter or postcard reminder, which request that the questionnaire be returned because a 100 percent return rate is important. A follow-up may include a duplicate questionnaire or may merely be a reminder to return the original questionnaire. Multiple contacts almost always increase response rates. The more attempts made to reach people, the greater the chances of their responding.12

Both of the studies in Exhibit 10.3 used follow-ups. Notice how the cumulative response rates picked up around week 4.

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CHAPTER 10 Survey Research: Communicating with Respondents • 221

Advance Notification Advance notification, by either letter or telephone, that a questionnaire will be arriving has been successful in increasing response rates in some situations. Nielsen has used this technique to ensure a high cooperation rate in filling out diaries of television watching. Advance notices that go out closer to the questionnaire mailing time produce better results than those sent too far in advance. The optimal lead time for advance notification appears to be about three days before the mail survey is to arrive.

Survey Sponsorship Auspices bias may result from the sponsorship of a survey. One business-to-business researcher wished to conduct a survey of its wholesalers to learn their stocking policies and their attitudes concerning competing manufacturers. A mail questionnaire sent on the corporate letterhead very likely would have received a much lower response rate than the questionnaire actually sent, which used the letterhead of a commercial marketing research firm. Sponsorship by well-known and prestigious organizations such as universities or government agencies may also significantly

influence response rates. A mail survey sent to members of a consumer panel will receive an exceptionally high response rate because panel members have already agreed to cooperate with surveys.

Other Techniques Numerous other devices have been used for increasing response rates. For example, the type of postage (commemorative versus regular stamp), envelope size, color of the questionnaire paper, and many other factors have been varied in efforts to increase response rates. Each has had at least limited success in certain situations; unfortunately, under other conditions each has failed to increase response rates significantly. The researcher should consider his or her

EXHIBIT 10.3

Plots of Actual Response Patterns for Two Commercial Surveys

.30

.25

.20

.15

.10

.05

0 1 2 3 4 5 6 7 8

Survey of research firms

Survey of purchasing departments

Weeks after Mailing

Cu m

ul at

iv e

Re sp

on se

P ro

po rti

us tin

S ul

liv an

/G et

ty Im

ag es

Mail surveys can reach a geographically dispersed sample and are relatively inexpensive. One disadvantage is the length of time involved in getting responses back. Response rates themselves also offer a challenge to surveyors.

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222 • PART THREE Research Methods for Collecting Primary Data

particular situation. For example, the researcher who is investigating consumers faces one situation; the researcher who is surveying corporate executives faces quite another.

Keying Mail Questionnaires with Codes A researcher planning a follow-up letter or postcard should not disturb respondents who already have returned the questionnaire. The expense of mailing questionnaires to those who already have responded is usually avoidable. One device for eliminating those who have already responded from the follow-up mailing list is to mark the questionnaires so that they may be keyed to identify members of the sampling frame who are nonrespondents. Blind keying of questionnaires on a return envelope (systematically varying the job number or room number of the marketing research department, for example) or a visible code number on the questionnaire has been used for this purpose. Visible keying is indicated with statements such as, “The sole purpose of the number on the last page is to avoid sending a second questionnaire to people who complete and return the first one.” Ethical researchers key questionnaires only to increase response rates, thereby preserving respondents’ anonymity.

Global Considerations Researchers conducting surveys in more than one country must recognize that postal services and cultural circumstances differ around the world. Some of the issues to consider are the reliability of mail delivery, literacy rates, and trust that researchers can and will provide confidentiality. In some cases, hand delivery of surveys or door-to-door interviewing may be necessary. In other cases, consumers (especially women or children) might be discouraged from talking to a door- to-door interviewer who is not a family member, so mailed questionnaires would be superior to interviews.

Self-Administered Questionnaires Using Other Forms of Distribution Many forms of self-administered, printed questionnaires are very similar to mail questionnaires. Airlines frequently pass out questionnaires to passengers during flights. Restaurants, hotels, and other service establishments print short questionnaires on cards so that customers can evaluate the service. Tennis Magazine, Advertising Age, Wired, and many other publications have used inserted questionnaires to survey current readers inexpensively, and often the results provide material for a magazine article.

Many manufacturers use their warranty or owner registration cards to collect demographic information and data about where and why products were purchased. Using owner registration cards is an extremely economical technique for tracing trends in consumer habits. Again, problems may arise because people who fill out these self-administered questionnaires differ from those who do not.

Extremely long questionnaires may be dropped off by an interviewer and then picked up later. The drop-off method sacrifices some cost savings because it requires traveling to each respondent’s location. However, drop-off does enhance response rate compared to a mail survey and can be an efficient method of implementing a study using cluster sampling.

Fax Surveys With fax surveys, potential survey respondents receive and/or return questionnaires via fax machines.13 A questionnaire inserted in a magazine may instruct the respondent to clip out the questionnaire and fax it to a certain phone number. In a mail survey, a prepaid-postage envelope

drop-off method

A survey method that requires the interviewer to travel to the respondent’s location to drop off questionnaires that will be picked up later.

CHAPTER 10 Survey Research: Communicating with Respondents • 223

places little burden on the respondent. A disadvantage of the fax survey is that only respondents with fax machines who are willing to exert the extra effort will return questionnaires. Again, people with extreme opinions will be more likely to respond.

To address this disadvantage, marketers may use faxing as one of several options for replying to a survey. The journal American Family Physician carried a reader survey that gave respondents the option of either returning the reply by fax or visiting the journal’s website to answer the same questions online.14 For busy physicians who likely have access to office equipment, this approach would improve the response rate.

Fax machines can also be used to distribute questionnaires. These fax surveys reduce the sender’s printing and postage costs and can be delivered and returned faster than traditional mail surveys. Questionnaires distributed via fax can deal with timely issues. Although relatively few households have fax machines, when the sample consists of organizations that are likely to have fax machines, the sample coverage may be adequate.

E-Mail Surveys Questionnaires can be distributed via e-mail, but researchers must remember that some individuals cannot be reached this way. Certain projects do lend themselves to e-mail surveys, such as internal surveys of employees or satisfaction surveys of retail buyers who regularly deal with an organiza- tion via e-mail. The benefits of incorporating a questionnaire in an e-mail include the speed of distribution, lower distribution and processing costs, faster turnaround time, more flexibility, and less handling of paper questionnaires. The speed of e-mail distribution and the quick response time can be major advantages for surveys dealing with time-sensitive issues.

Some researchers have argued that many respondents feel they can be more candid in e-mail than in person or on the telephone, for the same reasons they are open with other self-administered questionnaires. Yet in many organizations employees know that their e-mails are not secure and “eavesdropping” by a supervisor could possibly occur. Further, maintaining respondents’ anonymity is difficult, because a reply to an e-mail message typically includes the sender’s address. Researchers designing e-mail surveys should assure respondents that their answers will indeed be confidential.

Not all e-mail systems have the same capacity or settings. For example, some organizations may limit employees’ ability to open e-mail attachments. The differences in the capabilities and/or type of respondents’ computers (PC versus Mac) and e-mail software limit the types of questions and the layout of the e-mail questionnaire. For example, the display settings for computer screens vary widely, and wraparound of lines may put the questions and the answer choices into strange and difficult-to-read patterns.15 Novice e-mail users find it difficult to mark answers in brackets on an e-mail questionnaire and/or to attach a completed questionnaire as a file. These issues are not likely distributed evenly throughout the population. For instance, elderly respondents are less likely to use e-mail and more likely to struggle with e-mail surveys. Thus, they may be systematically eliminated from the sample. For this reason, some researchers give respondents the option to print out the questionnaire, complete it in writing, and return it via regular mail. Unless the research is an internal organizational survey, this alternative, of course, requires the respondent to pay postage.

In general, the guidelines for printed mail surveys apply to e-mail surveys. However, some differences exist, because the cover letter and the questionnaire appear in a single e-mail mes- sage. A potential respondent who is not immediately motivated to respond, especially one who considers an unsolicited e-mail survey to be spam, can quickly hit the delete button to remove the e-mail. This response suggests that e-mail cover letters should be brief and the questionnaires relatively short. The cover letter should explain how the company got the recipient’s name and should include a valid return e-mail address in the “from” box and reveal who is conducting the survey. Also, if the e-mail lists more than one address in the “to” or “cc” field, all recipients will see the entire list of names. This lack of anonymity has the potential to cause response bias and nonresponse error. When possible, the e-mail should be addressed to a single person. Alternatively, the blind carbon copy (bcc) field can be used if the same message must be sent to an entire sample.

E-mail has another important role in survey research. E-mail letters can be used as cover letters asking respondents to participate in an Internet survey. Such e-mails typically provide a password and a link to a unique website location that requires a password for access.

fax survey

A survey that uses fax machines as a way for

respondents to receive and return questionnaires.

e-mail surveys

Surveys distributed through electronic mail.

224 • PART THREE Research Methods for Collecting Primary Data

Internet Surveys An Internet survey is a self-administered questionnaire posted on a website. The use of Internet surveys has grown exponentially over the past decade as easy to use web survey software has become widely available. The Qualtrics software and survey included with this text are an excel- lent example of Internet surveying software. Like every other type of survey, Internet surveys have both advantages and disadvantages.

Speed and Cost-Effectiveness Internet surveys allow researchers to reach a large audience (possibly a global one), personalize individual messages, and secure confidential answers quickly and cost-effectively. These computer- to-computer self-administered questionnaires eliminate the costs of paper, postage, and data entry, as well as other administrative costs. Once an Internet questionnaire has been developed, the incremental cost of reaching additional respondents is minimal. So, samples can be larger than with interviews or other types of self-administered questionnaires. Even with large samples, surveys that used to take months can be conducted in a week or less.

Visual Appeal and Interactivity Surveys conducted on the Internet can be interactive. The researcher can use more sophisticated lines of questioning based on the respondents’ prior answers. Many of these interactive surveys utilize color, sound, and animation, which may help to increase respondents’ cooperation and willingness to spend time answering the questionnaires. The Internet is an excellent medium for the presentation of visual materials, such as photographs or drawings of product prototypes, advertisements, and movie trailers. Innovative measuring instruments that take advantage of the ability to adjust backgrounds, fonts, color, and other features have been designed and applied with considerable success.

Respondent Participation and Cooperation Participation in some Internet surveys occurs because computer users intentionally navigate to a particular website where questions are displayed. For example, a survey of more than 10,000 visi- tors to the Ticketmaster website helped Ticketmaster better understand its customer purchase pat- terns and evaluate visitor satisfaction with the site. In some cases, individuals expect to encounter a survey at a website; in others, it is totally unexpected. In some instances, the visitor cannot ven- ture beyond the survey page without providing information for the organization’s “registration” questionnaire. When the computer user does not expect a survey on a website and participation is voluntary, response rates are low. And, as with other questionnaires that rely on voluntary self- selection, participants tend to be more interested in or involved with the subject of the research than the average person.

For many other Internet surveys, respondents are initially contacted via e-mail. Often they are members of consumer panels who have previously indicated their willingness to cooperate. When panel members receive an e-mail invitation to participate, they are given logon instructions and a password. This security feature prevents access by individuals who are not part of the scientifi- cally selected sample. Assigning a unique password code also allows the researchers to track the responses of each respondent, thereby identifying any respondent who makes an effort to answer the questionnaire more than once.

Panel members also need an incentive to respond. A study of German consumers showed that nothing beat financial incentives. In other words, the best way to get responses was to simply pay consumers for participating in surveys.16

Ideally, the welcome screen contains the name of the research company and information about how to contact the organization if the respondent has a problem or concern. A typical statement might be, “If you have any concerns or questions about this survey or if you experience any techni- cal difficulties, please contact [name of research organization].”

welcome screen

The first web page in an Internet survey, which introduces the survey and requests that the respondent enter a password or PIN.

Internet survey

A self-administered questionnaire posted on a website.

CHAPTER 10 Survey Research: Communicating with Respondents • 225

226

Representative Samples The population to be studied, the purpose of the research, and the sampling methods determine the quality of Internet samples, which varies substantially. If the sample consists merely of those who visit a web page and voluntarily fill out a questionnaire, then it is not likely to be representa- tive of the entire population of interest due to self-selection error. However, if the purpose of the research is to evaluate how visitors feel about a website, randomly selecting every 100th visitor may accomplish the study’s purpose. Scientifically drawn samples from a consumer panel, or randomly selected samples such as those used in the “Mixed Mode Data Collection: The Case of Web and Telephone Surveys” Research Snapshot can also provide samples representative of the population.

Of course, a disadvantage, albeit ever decreasing, of Internet surveys is that some individuals in the general population cannot access the Internet. Even among people with Internet access, not all of them have the same level of technology. Many people with low-speed Internet connec- tions (low bandwidth) cannot quickly download high-resolution graphic files. Many lack powerful computers or software that is compatible with advanced features programmed into many Internet questionnaires. Some individuals have minimal computer skills. They may not know how to navi- gate through and provide answers to an Internet questionnaire. For example, the advanced audio- and video-streaming technology of RealPlayer or Windows Media Player software can be used to incorporate a television commercial and questions about its effectiveness into an Internet survey. However, some respondents might find downloading the file too slow or even impossible, others might not have the RealPlayer or Windows Media Player software, and still others might not know how to use the streaming media software to view the commercial.

For the foreseeable future, Internet surveys sampling the general public should be designed with the recognition that problems may arise for the reasons just described. Thus, photographs, animation, video streams, or other technological features created on the researcher’s/web designer’s powerful computer may have to be simplified or eliminated so that all respondents can interact at the same level of technological sophistication.

Do web surveys adequately capture the population’s attitudes and perceptions? While the advantages in time and cost efficien- cies of Internet-based surveys are apparent, questions about the effectiveness of this approach to data collection remain. A major criticism of web surveys is the low response rate—typically from 10 to 20 percent. While low response rate is not necessarily an issue in itself, it does present the possibility of nonresponse error. This gives rise to the question, “Would the information provided by sample members that did not respond to the web survey differ from that provided by web survey respondents?”

Recent research investigated this issue in the business-to- business context by conducting telephone surveys with non- respondents to web surveys. Termed “sequential mixed-mode data collection,” web-based customer satisfaction studies were initially carried out in three industries. After multiple invitations, the web surveys were closed and telephone surveys of nonrespon- dents launched. The information obtained from the telephone respondents was then compared to that from the Internet survey.

Response rates to the web surveys ranged from 11.6 to 15.1 percent across the three industries. The telephone response rates ranged from 31.5 to 37.9 percent. However,

comparison of the mean responses to 90 performance and outcome measures revealed only six significant differences. Further, the web surveys actually generated greater variances across the research items.

While certainly not conclusive, it appears that web- based surveys are not only fast and cost-effective, but provide the same information as far more costly telephone surveys. Business managers will be happy to know that the investment in telephone surveys may not be necessary.

Source: Attaway, Jill S., Mitch Griffin, and Michael R. Williams, “Exploring Data Quality and Measurement in Mixed Mode Data Collection,” presented at The Academy of Marketing Science 15th Biennial World Marketing Congress, Reims, France (July 20, 2011).

Mixed Mode Data Collection: The Case of Web and Telephone Surveys

RESEARCH SNAPSHOT

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

10.0

Item Item Item Item Item Item Item 1 2 3 4 5 6 7

Item Item Item Item Item Item Item 8 9 10 11 12 13 14

Web Survey Telephone Survey

Because Internet surveys can be accessed anytime (24/7) from anywhere, they can reach cer- tain hard-to-reach, busy respondents such as doctors, who would be almost impossible to reach via the telephone.

Accurate Real-Time Data Capture The computer-to-computer nature of Internet surveys means that each respondent’s answers are entered directly into the researcher’s computer as soon as the questionnaire is submitted. In addi- tion, the questionnaire software may be programmed to reject improper data entry. For example, on a paper questionnaire a respondent might incorrectly check two responses even though the instruc- tions call for a single answer. In an Internet survey, this mistake can be interactively corrected as the survey is taking place. Thus, the data capture is more accurate than when humans are involved.

Real-time data capture allows for real-time data analysis. A researcher can review up-to-the- minute sample size counts and tabulation data from an Internet survey in real time.

Callbacks When the sample for an Internet survey is drawn from a consumer panel, those who have not completed the survey questionnaire can be easily recontacted. Computer software can simply automatically send e-mail reminders to panel members who did not visit the welcome page. Com- puter software can also identify the passwords of respondents who completed only a portion of the questionnaire and send those people customized messages. Sometimes such e-mails offer additional incentives to those individuals who terminated the questionnaire with only a few additional ques- tions to answer, so that they are motivated to comply with the request to finish the questionnaire.

Personalized and Flexible Questioning Internet surveys are programmed in much the same way as computer-assisted telephone inter- views. That is, the software that is used allows questioning to branch off into two or more differ- ent lines depending on a respondent’s answer to a filtered question. The difference is that there is no interviewer. The respondent interacts directly with software on a website. In other words, the computer program asks questions in a sequence determined by the respondent’s previous answers. These methods avoid labor costs associated with data collection and processing of paper-and- pencil questionnaires.

This ability to sequence questions based on previous responses is a major advantage of com- puter-assisted surveys. The computer can be programmed to skip from question 6 to question 9 if the answer to question 6 is no. Furthermore, responses to previous questions can lead to ques- tions that can be personalized for individual respondents (for example, “When you cannot buy your favorite beer, Bud Light, what brand of beer do you prefer?”). Often the respondent’s name appears in questions to personalize the questionnaire. Fewer and more relevant questions speed up the response process and increase the respondent’s involvement with the survey.

A related advantage of using an Internet survey is that it can prompt respondents when they skip over a question. In a test comparing telephone and Internet versions of the same survey, the rate of item nonresponse was less for the Internet version, which issued a prompt for each item that was left blank.17 This was likely not a simple matter of motivation, because the rate of respon- dents who actually took the web version was less than for the telephone version, even though the researchers offered a larger incentive to those who were asked to go online. (An earlier telephone screening had verified that everyone who was asked to participate had a computer.)

The ability to customize questions and the low cost per recipient also help researchers keep surveys short, an important consideration for boosting responses.18 Jakob Nielsen, a consultant on Internet usability with the Nielsen Norman Group, emphasizes that “quick and painless” surveys generate the highest response and urges researchers to keep surveys as short as possible. He suggests that if the research objectives call for a long survey, the questions can be divided among several questionnaires, with each version sent to a different group of respondents.

Designers of Internet questionnaires can be creative and flexible in the presentation of ques- tions by using a variety of dialog boxes, or windows that prompt the respondent to enter informa- tion. Chapter 15 discusses electronic questionnaire design and layout further.

dialog boxes

Windows that open on a computer screen to prompt the user to enter information.

CHAPTER 10 Survey Research: Communicating with Respondents • 227

Respondent Anonymity Respondents are more likely to provide sensitive or embarrassing information when they can remain anonymous. The anonymity of the Internet encourages respondents to provide honest answers to sensitive questions.

Response Rates The methods for improving response rates for an Internet survey are similar to those for other kinds of survey research. A personalized invitation is important. In many cases, the invitation is delivered via e-mail. The respondents may not recognize the sender’s address, so the message’s subject line is also critical.19 The subject line should refer to a topic likely to interest the audience, and legal as well as ethical standards dictate that it may not be deceptive. Thus, the line might be worded in a way similar to the following: “Please give your opinion on [subject matter of interest].” Researchers should avoid gimmicks like dollar signs and the word free, either of which is likely to alert the spam filters installed on most computers.

As mentioned earlier, with a password system or Internet Protocol address (IP address) track- ing, people who have not participated in a survey in a predetermined period of time can be sent a friendly e-mail reminder asking them to participate before the study ends. This type of follow-up, along with preliminary notification, interesting early questions, and variations of most other techniques for increasing response rates to mail questionnaires, is recommended for Inter- net surveys.

Unlike mail surveys, Internet surveys do not offer the opportunity to send a physical incen- tive, such as a dollar bill, to the respondent. Incentives to respond to a survey must be in the form of a promise of a future reward—for example, “As a token of appreciation for complet- ing this survey, the sponsor of the survey will make a contribution to a national charity. You can vote for your preferred charity at the end of the survey.” Although some researchers have had success with promising incentives, academic research about Internet surveys is sparse but growing, currently providing few definitive answers about the most effective ways to increase response rates.

Security Concerns Many organizations worry that hackers or competitors may access websites to discover new prod- uct concepts, new advertising campaigns, and other top-secret ideas. Respondents may worry whether personal information will remain private. So may the organizations sponsoring the research. Recently, McDonald’s conducted quality-control research in England and Scotland, auto- mating the transmittal of data with a system in which consultants used handheld devices and sent the numbers to headquarters as e-mail messages. The system saved hours of work, but the company worried that confidential information could be compromised. McDonald’s therefore purchased software that encrypted the data and allowed the handhelds to be remotely wiped clean of data if they were lost or stolen.20

As in the experience of McDonald’s, no system can be 100 percent secure, but risks can be minimized. Many research service suppliers specializing in Internet surveying have developed password-protected systems that are very secure. One important feature of these systems restricts access and prevents individuals from filling out a questionnaire over and over again.

Kiosk Interactive Surveys A computer with a touch screen may be installed in a kiosk at a trade show, at a professional conference, in an airport, or in any other high-traffic location to administer an interactive survey. Because the respondent chooses to interact with an on-site computer, self-selection often is a problem with this type of survey. Computer-literate individuals are most likely to complete these interactive questionnaires. At temporary locations such as conventions, these surveys often require

228 • PART THREE Research Methods for Collecting Primary Data

a fieldworker to be at the location to explain how to use the computer system and answer any potential questions.

Survey Research That Mixes Modes For many surveys, research objectives dictate the use of some combination of telephone, mail, e-mail, Internet, and personal interview. For example, the researcher may conduct a short tele- phone screening interview to determine whether respondents are eligible for recontact in a more extensive personal interview. Such a mixed-mode survey combines the advantages of the tele- phone survey (such as fast screening) and those of the personal interview. A mixed-mode survey can employ any combination of two or more survey methods. Conducting a research study in two or more waves, however, creates the possibility that some respondents will no longer cooperate or will be unavailable in the second wave of the survey.

Several variations of survey research use cable television channels. For example, a telephone interviewer calls a cable subscriber and asks him or her to tune in to a particular channel at a cer- tain time. An appointment is made to interview the respondent shortly after the program or visual material is displayed. NBC uses this type of mixed-mode survey to test the concepts for many proposed new programs.

Text-Message Surveys As discussed in the chapter vignette, surveys are being sent via text messages. These may use SMS (short message service) or MMS (multimedia message service). This technique is perhaps the newest survey approach. It has all the advantages of mobile phone surveys in terms of reach and it also shares the disadvantages in terms of reaching respondents who have not opted in via a mobile phone. However, text-message surveys are catching on in other countries and are ideal for surveys involving only a few very short questions. Additionally, MMS messages can include graphic displays or even short videos. This technology is likely to see more applications in the near future.

Selecting the Appropriate Survey Research Design Earlier discussions of research design and problem definition emphasized that many research tasks may lead to similar decision-making information. There is no best form of survey; each has advan- tages and disadvantages. A researcher who must ask highly confidential questions may use a mail survey, thus sacrificing speed of data collection to avoid interviewer bias. If a researcher must have considerable control over question phrasing, central location telephone interviewing may be appropriate.

To determine the appropriate technique, the researcher must ask several questions: Is the assis- tance of an interviewer necessary? Are respondents highly interested in the issues being investi- gated? Will cooperation be easily attained? How quickly is the information needed? Will the study require a long and complex questionnaire? What information about the population is available (i.e., mailing address, telephone number, e-mail address)? How large is the budget? The criteria—cost, speed, anonymity, and so forth—may differ for each project.

Exhibit 10.4 summarizes the major advantages and disadvantages of typical door-to-door, mall intercept, telephone, mail, and Internet surveys. It emphasizes the typical types of surveys. For example, a creative researcher might be able to design highly versatile and flexible mail question- naires, but most researchers use standardized questions. An elaborate mail survey may be far more expensive than a short personal interview, but generally this is not the case.

‘‘Practice is the best of all instructors.’’—PUBLIUS SYRUS, CIRCA 42 bc

mixed-mode survey

Study that employs any combination of survey methods.

CHAPTER 10 Survey Research: Communicating with Respondents • 229

EXHIBIT 10.4

Advantages and Disadvantages of Typical Survey Methods

Door-to-Door Personal Interview

Mall Intercept Personal Interview

Telephone Interview Mail Survey Internet Survey

Speed of data collection

Moderate to fast Fast Very fast Slow; researcher has no control over return of questionnaire

Instantaneous; 24/7

Geographic flexibility

Limited to moderate

Confined, possible urban bias

High High High (worldwide)

Respondent cooperation

Excellent Moderate to low Good Moderate; poorly designed questionnaire will have low response rate

Varies depending on website; high from consumer panels

Versatility of questioning

Quite versatile Extremely versatile Moderate Not versatile; requires highly standardized format

Extremely versatile

Questionnaire length

Long Moderate to long Moderate Varies depending on incentive

Moderate; length customized based on answers

Item nonresponse rate

Low Medium Medium High Software can assure none

Possibility for respondent misunderstanding

Low Low Average High; no interviewer present for clarification

High

Degree of interviewer influence on answers

High High Moderate None; interviewer absent

None

Supervision of interviewers

Moderate Moderate to high High, especially with central- location interviewing

Not applicable Not applicable

Anonymity of respondent

Low Low Moderate High Respondent can be either anonymous or known

Ease of callback or follow-up

Difficult Difficult Easy Easy, but takes time

Difficult, unless e-mail address is known

Cost Highest Moderate to high Low to moderate Lowest Low

Special features Visual materials may be shown or demonstrated; extended probing possible

Taste tests, viewing of TV commercials possible

Fieldwork and supervision of data collection are simplified; quite adaptable to computer technology

Respondent may answer questions at own convenience; has time to reflect on answers

Streaming media software allows use of graphics and animation

Note: The emphasis is on typical surveys. For example, an elaborate mail survey may be far more expensive than a short personal interview, but this generally is not the case.

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230 • PART THREE Research Methods for Collecting Primary Data

Pretesting A researcher who is surveying 3,000 consumers does not want to find out after the questionnaires have been completed or returned that most respondents misunderstood a particular question, skipped a series of questions, or misinterpreted the instructions for filling out the questionnaire. To avoid problems such as these, screening procedures, or pretests, are often used. Pretesting involves a trial run with a group of respondents to iron out fundamental problems in the instructions or design of a questionnaire. The researcher looks for such obstacles as the point at which respondent fatigue sets in, whether there are any particular places in the questionnaire where respondents tend to terminate, or if some specific questions are skipped. Despite its substantial value, this stage of research is unfortunately sometimes eliminated because of costs or time pressures.

Broadly speaking, three basic ways to pretest exist. The first two involve screening the ques- tionnaire with other research professionals, and the third—the one most often called pretesting—is a trial run with a group of respondents. When screening the questionnaire with other research professionals, the investigator asks them to look for such problems as difficulties with question wording, leading questions, and bias due to question order. An alternative type of screening might involve a client or the research manager who ordered the research. Often, managers ask researchers to collect information, but when they see the questionnaire, they find that it does not really meet their needs. Only by checking with the individual who has requested the questionnaire does the researcher know for sure that the information needed will be provided. Once the researcher has decided on the final questionnaire, data should be collected with a small number of respondents (usually 100 or less) to determine whether the questionnaire needs refinement.

Ethical Issues in Survey Research Many ethical issues apply to survey research, such as respondents’ right to privacy, the use of deception, respondents’ right to be informed about the purpose of the research, the need for con- fidentiality, the need for honesty in collecting data, and the need for objectivity in reporting data. You may wish to reexamine Chapter 5’s coverage of these issues now that various survey research techniques have been discussed.21

pretesting

Screening procedure that involves a trial run with a group of respondents to iron out fundamental problems in the survey design.

SUMMARY

1. Summarize ways researchers gather information through interviews. Interviews can be categorized based on the medium used to communicate with respondents. Interviews can be con- ducted door-to-door, in shopping malls, or on the telephone. Traditionally, interviews have been recorded using paper and pencil, but survey researchers are increasingly using computers. Personal interviews are a flexible method that allows researchers to use visual aids and various kinds of props. However, the presence of an interviewer may influence subjects’ responses.

2. Compare the advantages and disadvantages of conducting door-to-door, mall intercept, and telephone interviews. Door-to-door personal interviews can get high response rates, but they are more costly to administer than other types of surveys. When a sample need not represent the entire country, mall intercept interviews may reduce costs. Telephone interviewing has the advan- tage of providing data fast and at a lower cost per interview. However, not all households have tele- phones, and not all telephone numbers are listed in directories. This causes problems in obtaining a representative sample, so researchers often use random digit dialing. Absence of face-to-face contact and inability to use visual materials also limit telephone interviewing. Computer-assisted telephone interviewing from central locations can improve the efficiency of certain kinds of telephone surveys.

3. Evaluate the advantages and disadvantages of distributing questionnaires through the mail, the Internet, and by other means. Traditionally, self-administered questionnaires have been distributed by mail, but self-administered questionnaires also may be dropped off to individual respondents, distributed from central locations, or administered via computer. Mail

CHAPTER 10 Survey Research: Communicating with Respondents • 231

questionnaires generally are less expensive than telephone or personal interviews, but they also introduce a much larger chance of nonresponse error. Several methods can be used to encourage higher response rates. Mail questionnaires must be more structured than other types of surveys and cannot be changed if problems are discovered in the course of data collection. The Internet and other interactive media provide convenient ways for organizations to conduct surveys. Internet surveys are quick and cost-effective, but not everyone has Internet access. Because the surveys are computerized and interactive, questionnaires can be personalized and data can be captured in real time. Some privacy and security concerns exist, but the future of Internet surveys looks promising.

4. Discuss the importance of pretesting questionnaires. Pretesting a questionnaire on a small sample of respondents is a useful way to discover problems while they can still be corrected. Pre- tests may involve screening the questionnaire with other research professionals or conducting a trial run with a set of respondents.

5. Describe ethical issues that arise in survey research. Researchers must protect the public from misrepresentation and exploitation. This obligation includes honesty about the purpose of a research project and protection of subjects’ right to refuse to participate or to answer particular questions. Researchers also should protect the confidentiality of participants and record responses honestly.

KEY TERMS AND CONCEPTS callbacks, 211 central location interviewing, 215 computer-assisted telephone

interviewing (CATI), 216 cover letter, 220 dialog boxes, 227 door-to-door interviews, 210 drop-off method, 223

e-mail surveys, 224 fax survey, 224 Internet survey, 225 item nonresponse, 209 mail survey, 217 mall intercept interviews, 211 mixed-mode survey, 229 personal interview, 207

pretesting, 231 random digit dialing, 215 response rate, 219 self-administered questionnaires, 217 telephone interviews, 212 welcome screen, 225

1. What type of communication medium would you use to con- duct the following surveys? Why? a. Survey of the buying motives of industrial engineers b. Survey of the satisfaction levels of hourly support staff c. Survey of television commercial advertising awareness d. Survey of top corporate executives

2. A publisher offers college professors one of four best-selling mass-market books as an incentive for filling out a 10-page mail questionnaire about a new textbook. What advantages and dis- advantages does this incentive have?

3. “Individuals are less willing to cooperate with surveys today than they were 50 years ago.” Comment on this statement.

4. What do you think should be the maximum length of a self- administered e-mail questionnaire?

5. Do most surveys use a single communication mode (for example, the telephone), as most textbooks suggest?

6. A survey researcher reports that “205 usable questionnaires out of 942 questionnaires delivered in our mail survey converts to a 21.7 percent response rate.” What are the subtle implications of this statement?

7. Evaluate the following survey designs: a. A researcher suggests mailing a small safe (a metal file box

with a built-in lock) without the lock combination to respondents, with a note explaining that respondents will be called in a few days for a telephone interview. During the telephone interview, the respondent is given the combina- tion and the safe may be opened.

b. A shopping mall that wishes to evaluate its image places packets including a questionnaire, cover letter, and stamped return envelope in the mall where customers can pick them up if they wish.

c. An e-mail message is sent to individuals who own computers, asking them to complete a questionnaire on a website. Respondents answer the questions and then have the opportunity to play a slot-machine game on the website. Each respondent is guaranteed a monetary incentive but has the option to increase it by playing the slot-machine game.

d. A mall intercept interviewing service is located in a regional shopping center. The facility contains a small room for television and movie presentations. Shoppers are used as sampling units. However, mall intercept interviewers recruit additional subjects for television commercial experiments by offering them several complimentary tickets for special sneak previews. Individuals contacted at the mall are allowed to bring up to five guests. In some cases the complimentary tickets are offered through ads in a local newspaper.

e. Time magazine opts to conduct a mail survey rather than a telephone survey for a study to determine the demo- graphic characteristics and purchasing behavior of its subscribers.

8. What type of research studies lend themselves to the use of e-mail for survey research? What are the advantages and disadvantages of using e-mail?

QUESTIONS FOR REVIEW AND CRITICAL THINKING

232 Part 3: Research Methods for Collecting Primary Data

RESEARCH ACTIVITIES 1. ‘NET Visit this website: http://www.zoomerang.com. What

unique service does this company offer? Then visit this site: http://www.qualtrics.com. How does this service differ from Zoomerang? Create a short survey and e-mail it to 10 of your

friends without any advance notice. At the end of the survey, ask them if they would have responded had they not noticed the survey came from you. What is the response rate? What would it have been if the respondent did not know you?

National Do Not Call Registry Citizens’ annoyance with phone calls from sales- people prompted Congress to pass a law setting up a National Do Not Call Registry. The registry was

soon flooded with requests to have phone numbers removed from telemarketers’ lists. By law, salespeople may not call numbers listed on this registry. The law makes exceptions for charities and research- ers. However, a recent poll suggests that even though phone calls from researchers may be legal, they are not always well received.22

In late 2005, Harris Interactive conducted an Internet survey in which almost 2,000 adults answered questions about the National Do Not Call Registry. About three-quarters of the respondents said they had signed up for the registry, and a majority (61 percent) said they had since received “far less” contact from telemarketers. In addi- tion, 70 percent said that since registering, they had been contacted

by someone “who was doing a poll or survey” and wanted them to participate. But apparently respondents weren’t sure whether this practice was acceptable. Only one-fourth (24 percent) of respondents said they knew that researchers “are allowed to call,” and over half (63 percent) weren’t sure about researchers’ rights under the law.

Questions 1. Was an online survey the best medium for a poll on this subject?

What were some pros and cons of conducting this poll online? 2. How might the results have differed if this poll had been con-

ducted by telephone? 3. As a researcher, how would you address people’s doubts about

whether pollsters may contact households listed on the Do Not Call Registry?

CASE 10.1

9. ETHICS Comment on the ethics of the following situations: a. A researcher plans to use invisible ink to code questionnaires

to identify respondents in a distributor survey. b. A political action committee conducts a survey about its

cause. At the end of the questionnaire, it includes a request for a donation.

c. A telephone interviewer calls at 1:00 p.m. on Sunday and asks the person who answers the phone to take part in an interview.

d. An industrial manufacturing firm wishes to survey its own distributors. It invents the name “Mountain States Market- ing Research” and sends out a mail questionnaire under this name.

e. A questionnaire is printed on the back of a warranty card included inside the package of a food processor. The ques- tionnaire includes a number of questions about shopping behavior, demographics, and customer lifestyles. At the bottom of the warranty card is a short note in small print that says, “Thank you for completing this questionnaire. Your answers will be used for further studies and to help us serve you better in the future. You will also benefit by receiving important mailings and special offers from a number of organizations whose products and services relate directly to the activities, interests, and hobbies in which you enjoy participating on a regular basis. Please indicate if there is some reason you would prefer not to receive this information.”

10. ETHICS How might the business research industry take action to ensure that the public believes that telephone surveys and door- to-door interviews are legitimate activities and that firms that

misrepresent and deceive the public using research as a sales ploy are not true researchers?

11. Why is the mobile phone likely to be an ineffective way of reaching potential respondents in America?

12. The American Testing Institute (also known as the U.S. Test- ing Authority) mails respondents what it calls a “television” survey. A questionnaire is sent to respondents, who are asked to complete it and mail it back along with a check for $14.80. In return for answering eight questions on viewing habits, the institute promises to send respondents one of 20 prizes ranging in value from $200 to $2,000—among which are video record- ers, diamond watches, color televisions, and two nights of hotel accommodations at a land development resort community. The institute lists the odds of winning as one in 150,000 on all prizes except the hotel stay, for which the odds are 149,981 out of 150,000. During a three-month period, the institute sends out 200,000 questionnaires. What are the ethical issues in this situation?

13. ‘NET Go to the Pew Internet and American Life page at http:// www.pewinternet.org. Several reports based on survey research will be listed. Select one of the reports. What were the research objectives? What were the first three questions on the survey?

14. ‘NET Go to the NPD Group website (http://www.npd.com) and click on the Products & Services link. What types of custom and syndicated survey research services does the company offer?

15. ‘NET Go to the CASRO (Council of American Survey Research Organizations) home page (http://www.casro.org). Locate the organization’s mission under the About tab. What are the key aspects of this research organization’s mission?

Chapter 10: Survey Research: Communicating with Respondents 233

Royal Bee Electric Fishing Reel Royal Barton started thinking about an electric fish- ing reel when his father had a stroke and lost the use of an arm. To see that happen to his dad, who

had taught him the joys of fishing and hunting, made Barton real- ize what a chunk a physical handicap could take out of a sports enthusiast’s life. Being able to cast and retrieve a lure and experience the thrill of a big bass trying to take your rig away from you were among the joys of life that would be denied Barton’s father forever.

Barton was determined to do something about it, if not for his father, then at least for others who had suffered a similar fate. So, after tremendous personal expense and years of research and development, Barton perfected what is sure to be the standard bearer for all future freshwater electric reels. Forget those saltwater jobs, which Barton refers to as “winches.” He has developed something that is small, compact, and has incredible applications.

He calls it the Royal Bee. The first word is obviously his first name. The second word refers to the low buzzing sound the reel makes when in use.

The Royal Bee system looks simple enough and probably is if you understand the mechanical workings of a reel. A system of gears ties into the spool, and a motor in the back drives the gears attached to the triggering system.

All gearing of the electrical system can be disengaged so that you can cast normally. But pushing the button for “retrieve” engages two gears. After the gears are engaged, the trigger travels far enough to touch the switch that tightens the drive belt, and there is no slipping. You cannot hit the switch until the gears are properly engaged. This means that you cast manually, just as you would normally fish, then you reengage the reel for the levelwind to work. And you can do all that with one hand!

The system works on a 6-volt battery that you can attach to your belt or hang around your neck if you are wading. If you have a boat with a 6-volt battery, the reel can actually work off of the battery. There is a small connector that plugs into the reel, so you could eas- ily use more than one reel with the battery. For instance, if you have two or three outfits equipped with different lures, you just switch the connector from reel to reel as you use it. A reel with the Royal Bee system can be used in a conventional manner. You do not have to use it as an electric reel unless you choose to do so.

Barton believes the Royal Bee may not be just for handicapped fishermen. Ken Cook, one of the leading professional anglers in the country, is sold on the Royal Bee. After he suffered a broken arm, he

had to withdraw from some tournaments because fishing with one hand was difficult. By the time his arm healed, he was hooked on the Royal Bee because it increased bass catching efficiency. As Cook explains, “The electric reel has increased my efficiency in two ways. One is in flipping, where I use it all the time. The other is for fishing topwater, when I have to make a long cast. When I’m flipping, the electric reel gives me instant control over slack line. I can keep both hands on the rod. I never have to remove them to take up slack. I flip, engage the reel, and then all I have to do is push the lever with my thumb to take up slack instantly.”

Cook’s reel (a Ryobi 4000) is one of several that can be con- verted to the electric retrieve. For flipping, Cook loads his reel with 20-pound test line. He uses a similar reel with lighter line when fish- ing a surface lure. “What you can do with the electric reel is elimi- nate unproductive reeling time,” Cook says.

A few extra seconds may not mean much if you are out on a neighborhood pond just fishing on the weekend. But it can mean a lot if you are in tournament competition, where one extra cast might keep you from going home with $50,000 tucked in your pocket. “Look at it this way,” Cook explains. “Let’s suppose we’re in clear water and it’s necessary to make a long cast to the cover we want to fish with a topwater lure. There’s a whole lot of unproduc- tive water between us and the cover. With the electric reel, I make my long cast and fish the cover. Then, when I’m ready to reel in, I just press the retrieve lever so the battery engages the necessary gears, and I’ve got my lure back ready to make another cast while you’re still cranking.”

When Royal Barton retired from his veterinary supply business, he began enjoying his favorite pastimes: hunting, fishing, and devel- oping the Royal Bee system. He realized he needed help in market- ing his product, so he sought professional assistance to learn how to reach the broadest possible market for the Royal Bee system.

Questions 1. What business research problem does Royal Barton face? What

are his information needs? Outline some survey research objec- tives for a research project on the Royal Bee system.

2. What type of survey—personal interview, telephone interview, or mail survey—should be selected?

3. What sources of survey error are most likely to occur in a study of this type?

4. What means should be used to obtain a high response rate?

CASE 10.2

234 Part 3: Research Methods for Collecting Primary Data

235

Observation Methods

After studying this chapter, you should be able to

1. Discuss the role of observation as a business research method

2. Describe the use of direct observation and contrived observation

3. Identify ethical issues in observation studies

4. Explain the observation of physical objects and message content

5. Describe major types of mechanical observation

6. Summarize techniques for measuring physiological reactions

C H A P T E R

M ike and Marilyn, Brian and Mary Kay, and Mitch and Jill were certainly enjoying their dinner at Seasons Restaurant on the seventh floor of the Four Seasons Hotel in Chicago.

The restaurant is “an extravagant affair” with mahogany pan- eled walls, beautiful brocade armchairs, and fresh flowers everywhere. They had started by splitting a bottle of cham- pagne before each ordering the eight-course degustation— eight separate dishes, each with a matching wine. The three couples were enjoying the service, the food, the wine, and the company of their friends, a bit unaware of the other diners. However, they were being closely observed without even real- izing it. A mystery shopper (or mystery diner in this case) was sitting near them, observing their dining experience, as well as the service being provided.

Mystery shoppers (or mystery diners or mystery employees) can help inspect and evaluate a variety of activities, including customer service, company operations, employee integrity, store merchandising, and product quality. Mystery shopping origi- nated as a technique used by private investigators to identify and prevent employee theft—primarily at banks and retail stores. By posing as workers, mystery employees could become part of the organization and observe the operation and employee behavior, including identifying opportunities for theft and workers that

LEARNING OUTCOMES

Chapter Vignette:

Mystery Diner at Seasons Restaurant

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Introduction Mystery shoppers are just one observational approach to collecting research data. While survey data can provide some insight into future or past behavior, one can hardly argue with the power of data representing actual behavior. This chapter introduces the various techniques involved in observational methods of data gathering in business research.

Observation in Business Research In business research, observation is a systematic process of recording behavioral patterns of people, objects, and occurrences as they happen. No questioning or communicating with people is needed. Researchers who use observation as a method of data collection either witness and record infor- mation while watching events take place or take advantage of some tracking system such as check- out scanners or Internet activity records. These tracking systems can observe and provide data such as whether or not a specific consumer purchased more products on discount or at regular price or how long an employee takes to complete a specific task.

Observation becomes a tool for scientific inquiry when it meets several conditions:

■ The observation serves a formulated research purpose. ■ The observation is planned systematically. ■ The observation is recorded systematically and related to general propositions, rather than

simply reflecting a set of interesting curiosities. ■ The observation is subjected to checks or controls on validity and reliability.2

What Can Be Observed? Observational studies gather a wide variety of information about behavior. Exhibit 11.1 lists seven kinds of observable phenomena: physical actions, such as shopping patterns (in-store or via a web interface) or television viewing; verbal behavior, such as sales conversations or the exchange between a worker and supervisor; expressive behavior, such as tone of voice, facial expressions, or a coach stomping his foot; spatial relations and locations, such as traffic patterns; temporal patterns, such as amount of time spent shopping, driving, or making a business decision; physical objects, such as the amount of newspapers recycled or number of beer cans in the trash; and verbal and pictorial records, such as the content of advertisements or the number of minorities pictured in a company brochure. (Investigation of secondary data also uses observation, but that subject was described in Chapter 8 and is not extensively discussed in this chapter.)

While the observation method may be used to describe a wide variety of behavior, cognitive phenomena such as attitudes, motivations, and preferences cannot be observed. As a result, obser- vation research cannot provide an explanation of why a behavior occurred or what actions were intended. Another limitation is that the observation period generally is short. Observing behavior

observation

The systematic process of recording the behavioral

patterns of people, objects, and occurrences as they are

witnessed.

‘‘Where observation is concerned, chance favors only the prepared mind.’’—LOUIS PASTEUR

might be stealing. The term “Mystery Shopping” was coined in the 1940s by WilMark, the first research firm to apply the concept beyond integrity applications. Since then, we have seen widespread application of mystery shopping. Today, over 400 companies belong to the Mystery Shopping Providers Associa- tion (MSPA) located in countries around the world.

Mystery shoppers can prove advantageous in many ways. A mystery shopper allows an organization to view their operation through a trained customer’s (or employee’s) eyes.

Questioning employees is certainly unlikely to reveal employee theft. Similarly, a survey of the wait staff at Seasons Restau- rant would probably indicate that they are attentive to din- ers, keep the water glasses filled, and are consistently polite. Observing these behaviors, however, can provide a far more accurate picture and more detailed information. Mystery shop- pers can report how long they waited, how they were treated, and even note that some diners are “not the typical Seasons crowd.”

236 • PART THREE Research Methods for Collecting Primary Data

237

patterns that occur over a period of several days or weeks generally is too costly or even impossible. Nonetheless, observation can provide some very interesting insights as described in the Research Snapshot “This Trend Brought to You by DDB SignBank”.

The Nature of Observation Studies Business researchers can observe people, objects, events, or other phenomena using either human observers or machines designed for specific observation tasks. Human observation best suits a situ- ation or behavior that is not easily predictable in advance of the research. Mechanical observation, as performed by supermarket scanners or traffic counters, can very accurately record situations or types of behavior that are routine, repetitive, or programmatic.

Human or mechanical observation is generally unobtrusive, meaning no communication with a respondent takes place. For example, rather than asking an employee how long it takes to handle an insurance claim, a researcher might observe and record the time it takes for different steps in this process. Or, rather than ask a consumer how long they spend shopping for produce, a researcher can watch shoppers in a supermarket and note the time each spends in the produce area. As noted in the opening vignette, the unobtrusive or nonreactive nature of the observation method often generates data without a subject’s knowledge. A situation in which an observer’s presence is known to the subject involves visible observation. A situation in which a subject is unaware that observation is taking place is hidden observation. Hidden, unobtrusive observation minimizes respondent error. Asking subjects to participate in the research is not required when they are unaware that they are being observed.

EXHIBIT 11.1

What Can Be ObservedPhenomenon Example

Physical action A worker’s movement during an assembly process

Verbal behavior Statements made by airline travelers while waiting in line

Expressive behavior Facial expressions, tones of voices, and forms of body language

Spatial relations and locations Proximity of middle managers’ offices to the president’s office

Temporal patterns Length of time it takes to execute a stock purchase order

Physical objects Percent of recycled materials compared to trash

Verbal and pictorial records Number of illustrations appearing in a training booklet

SURVEY

THIS! Observation can be a very useful form of data collection. After reading this chapter you should be able to under- stand and identify the strengths and weaknesses of observation relative to other forms of data collection.

Considering these strengths and weaknesses, review the online survey. Are there areas where you feel observation would be a better method of gathering information than what the online survey will provide? Identify at least one area or issue that you believe could either be better addressed by observation than a survey, or where observation could be used to enhance the information provided by the survey. Design and describe an observation approach to provide this information.

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visible observation

Observation in which the observer's presence is known to the subject.

hidden observation

Observation in which the subject is unaware that observation is taking place.

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238

The major advantage of observation studies over surveys, which obtain self-reported data from respondents, is that the data are free from distortions, inaccuracies, or other response biases due to memory error, social desirability bias, and so on. The data are recorded when the actual behavior takes place.

Observation of Human Behavior Whereas surveys emphasize verbal responses, observation studies emphasize and allow for the systematic recording of nonverbal behavior. Toy manufacturers such as Fisher Price use the obser- vation technique because children often cannot express their reactions to products. By observing children at play with a proposed toy, doll, or game, business researchers may be able to identify the elements of a potentially successful product. Toy researchers might observe play to answer the following questions:

■ How long does the child’s attention stay with the product? ■ How exactly does the child play with the toy? ■ Are the child’s peers equally interested in the toy?

Behavioral scientists have recognized that nonverbal behavior can be a communication process by which meanings are exchanged among individuals. Head nods, smiles, raised eyebrows, and other facial expressions or body movements have been recognized as communication symbols.

Extending the practice of observation beyond what can clearly be done scientifically, such as counting the number of tomato soup cans in a pantry or measuring the time spent watching television, some researchers have tried to catalog behaviors that may signal the beginning of important trends. This practice, called trend spotting, is controversial because the observations are subjective and unsystematic. In spite of the criticism, mar- keters are increasingly turning to trend spotters, so researchers have an incentive to develop this method’s capabilities.

Starting in its office in Copenhagen, Denmark, giant ad agency DDB Worldwide has created a service called DDB SignBank, which invites all of DDB’s staff throughout the world, plus other targeted groups such as members of youth organiza- tions, to submit their observations to managers appointed as SignBankers. Staff members are directed to identify consumer behaviors, rather than comments gathered from other research methods, that might signal a new trend in the society or cul- ture. The SignBankers classify the observations and enter them into a corporate database. The database is updated each day, and account teams at the agency can search it for signs related to their clients’ advertising objectives.

The idea behind SignBank, developed by sociologist Eva Steensig, is that the size of the database (which contained thirty thousand signs at a recent count) will allow patterns to emerge in the sheer number of observations. The data may be most useful as a source of ideas to test more rigorously. Anthon

Berg, a Scandinavian brand of chocolate, used SignBank data to identify new occasions for which to promote chocolate and new uses for chocolate in health and beauty treatments.

Recently, SignBank has observed a shift in consumers from “herds” to “swarms.” Herds are a single body of peo- ple that share a common view and choose a joint direction led by an opinion leader. Swarms are a group of individuals with differing opinions that go in a multitude of directions. DDB claims that the information available on the Internet has empowered consumers and they don’t “believe just anything anymore.” As a result, traditional institutions and media sources, such as churches, banks, newspapers, and brands, are losing power. Individuals are sharing information directly and being strengthened by becoming a member of the swarm.

Sources: Based on Creamer, Matthew, “DDB Collects ’Signs’ to Identify Trends,” Advertising Age (December 5, 2005), http://adage.com/article/ news/ddb-collects-signs-identify-trends/105464/, accessed August 9, 2011; Pfanner, Eric, “On Advertising: Do I Spot a Trend?” International Her- ald Tribune (January 1, 2006), www.iht.com; “From Herds to Swarms,” Marketing Tribune 7 (April 7, 2008), 30–31, http://www.ddbamsterdam.nl/ public/en/SignBank/SignBank.

This Trend Brought to You by DDB SignBank

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RESEARCH SNAPSHOT

Observation of nonverbal communication may hold considerable promise for the business researcher. For example, a hypothesis about customer-salesperson interactions is that the salesper- son signals status based on the importance of each transaction. In low-importance transactions, in which potential customers are plentiful and easily replaced (say, a shoe store), the salesperson may show definite nonverbal signs of higher status than the customer. When customers are scarce, as in big-ticket purchase situations, the opposite should be true. For example, real estate sales agents may display nonverbal indicators of deference. One way to test this hypothesis would be with an observation study using the nonverbal communication measures shown in Exhibit 11.2.

EXHIBIT 11.2

Observing and Interpreting Nonverbal Communication

Behavior Description Example

Facial expressions

Expressions of emotion such as surprise (eyes wide open, mouth rounded and slightly open, brow furrowed)

A consumer reacts to the price quoted by a salesperson.

Body language Posture, placement of arms and legs A consumer crosses arms as salesperson speaks, possibly indicating a lack of trust.

Eye activity Eye contact, staring, looking away, dilated pupils. In U.S. culture, not making eye contact is indicative of a deteriorating relationship. Dilated pupils can indicate emotion or degree of honesty.

A consumer avoids making eye contact with a salesperson knowing that he or she will not make a purchase.

Personal space Physical distance between individuals; in the United States, people like to be about eight feet apart to have a discussion.

A consumer may back away from a salesperson who is viewed to be violating one's personal space.

Gestures Responses to certain events with specific body reactions or gestures

A consumer who wins something (maybe at the casino or a sports contest) lifts arms, stands tall, and sticks out chest.

Manners Accepted protocol for given situations A salesperson may shake a customer’s hand, but should not touch a customer otherwise.

Of course, researchers would not ignore verbal behavior. In fact, in certain observation studies, verbal expression is very important.

Complementary Evidence The results of observation studies may extend the results of other forms of research by providing complementary evidence concerning individuals’ “true” feelings. Focus group interviews often are conducted behind two-way mirrors from which executives observe as well as listen to what is occurring. This additional source allows for interpretation of nonverbal behavior such as facial expressions or head nods to supplement information from interviews.

For example, in one focus group session concerning women’s use of hand lotion, researchers observed that all the women’s hands were above the table while they were casually waiting for the session to begin. Seconds after the women were told that the topic was to be hand lotion, all their hands were placed out of sight. This observation, combined with the group discussion, revealed the

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CHAPTER 11 Observation Methods • 239

women’s anger, guilt, and shame about the condition of their hands. Although they felt they were expected to have soft, pretty hands, their housework required them to wash dishes, clean floors, and do other chores that were hard on their hands. Note, however, that without the discussion provided by the participants the researcher would only have been able to note the action of placing their hands under the table, not the explanation for this behavior.

Direct Observation Direct observation can produce detailed records of what people actually do during an event. The observer plays a passive role, making no attempt to control or manipulate a situation, instead merely recording what occurs. Many types of data can be obtained more accurately through direct obser- vation than by questioning. For example, recording traffic counts or observing the direction of customer movement within a supermarket can help managers design store layouts that maximize the exposure of departments that sell impulse goods. A manufacturer can determine the number of facings, shelf locations, display maintenance, and other characteristics that improve store condi- tions. If directly questioned in a survey, most shoppers would be unable to accurately portray the time they spent in each department. The observation method, in contrast, could determine this without difficulty.

With the direct observation method, the data consist of records of events made as they occur. An observation form often helps keep researchers’ observations consistent and ensures that they record all relevant information. A respondent is not required to recall—perhaps inaccurately—an event after it has occurred; instead, the observation is instantaneous.

In many cases, direct observation is the most straightforward form of data collection—or the only form possible. A produce manager for Auchan (a France-based hypermarket firm) may peri- odically gather competitive price information from Carrefour (also a France-based hypermarket firm) stores within competing areas. Both Carrefour and Auchan can monitor each other’s pro- motions by observing promotions posted on the competitor’s website (see http://www.auchan.fr and http://www.carrefour.fr, for example). In other situations, observation is the most economical technique. In a common type of observation study, a shopping center manager may observe the license plate (tag) numbers on cars in its parking lot. These data, along with automobile registration information, provide an inexpensive means of determining where customers live.

Certain data may be obtained more quickly or easily using direct observation than by other methods—gender, race, and other respondent characteristics can simply be observed. Researchers investigating a diet product may use observation when selecting respondents in a shopping mall. Overweight people may be prescreened by observing pedestrians, thus eliminating a number of screening interviews. Behaviors occurring in public places can also be easily observed.

In a quality-of-life survey, researchers asked respondents a series of questions that were compiled into an index of well-being. But interviewers also used direct observation because the researchers wanted to investigate the effect of weather conditions on people’s answers. The researchers quickly and easily observed and recorded outside weather conditions on the day of the interviews, as well as the temperature and humidity in the building in which the interviews were conducted.3

Recording the decision time necessary to make a choice between two alternatives is a rel- atively simple, unobtrusive task easily accomplished through direct observation. Observing the choice time as a measure of the strength of the preference between alternatives is called response latency. This measure is based on the hypothesis that the longer a decision maker takes to choose between two alternatives, the closer the two alternatives are in terms of preference. In contrast, making a quick decision presumably indicates a considerable psychological distance between alternatives—that is, the choice is obvious. It is simple for a computer to record decision times, so the response latency measure has gained popularity now that computer-assisted data collection methods are common.

Errors Associated with Direct Observation Although direct observation involves no interaction with the subject, the method is not error-free; the observer may record events subjectively. The same visual cues that may influence the interplay between interviewer and respondent (e.g., the subject’s age or sex) may come into play in some

direct observation

A straightforward attempt to observe and record

what naturally occurs; the investigator does not create an

artificial situation.

response latency

The amount of time it takes to make a choice between two

alternatives; used as a measure of the strength of preference.

240 • PART THREE Research Methods for Collecting Primary Data

direct observation settings, such as when the observer subjectively attributes a particular economic status or educational background to a subject. A distortion of measurement resulting from the cognitive behavior or actions of the witnessing observer is called observer bias. For example, in a research project using observers to evaluate whether sales clerks are rude or courteous, fieldworkers may be required to rely on their own interpretations of people or situations during the observa- tion process.

Also, accuracy may suffer if the observer does not record every detail that describes the persons, objects, and events in a given situation. Generally, the observer should record as much detail as possible. However, the pace of events, the observer’s memory and writing speed, and other factors will limit the amount of detail that can be recorded.

Interpretation of observation data is another potential source of error. Facial expressions and other nonverbal communication may have several meanings. Does a smile always mean happiness? Does the fact that someone is standing or seated next to the president of a company necessarily indicate the person’s status?

Scientifically Contrived Observation Most observation takes place in a natural setting, but sometimes the investigator intervenes to create an artificial environment in order to test a hypothesis. This approach is called contrived observation. Contrived observation can increase the frequency of occurrence of certain behavior patterns, such as employee responses to complaints. An airline passenger complaining about a meal or service from the flight attendant may actually be a researcher recording that person’s reactions. If situations were not contrived, the research time spent waiting and observing would expand con- siderably. This is one of the reasons for the growing popularity of mystery shoppers introduced in the opening vignette. They can effectively create a situation (such as a customer complaint) that might be very time consuming to observe if it were to occur naturally.

Combining Direct Observation and Interviewing Some research studies combine visible observation with personal interviews. During or after detailed observations, individuals are asked to explain their actions.4 For example, direct observa- tion of women applying hand and body lotion identified two kinds of users. Some women slapped on the lotion, rubbing it briskly into their skin. Others caressed their skin as they applied the lotion. When the women were questioned about their behavior, the researchers discovered that women who slapped the lotion on were using the lotion as a remedy for dry skin. Those who caressed their skin were more interested in making their skin smell nice and feel soft.

Ethical Issues in the Observation of Humans Observation methods introduce a number of ethical issues. Hidden observation raises the issue of the respondent’s right to privacy. Suppose a research firm is approached by a company interested in acquiring information about how women put on their bras. The researcher considers approaching spas in several key cities about placing small cameras inconspicuously to observe women getting dressed. Obviously, this is an illegal and unethical approach. However, what if other women are hired to observe and record this activity? While to some extent the dressing room is an area where women often do dress where others can observe them, women do not expect to have their dress- ing behavior recorded. Therefore, unless a way can be found to have some women consent to such observation, this observational approach is unethical.

Some people might see contrived observation as entrapment. To entrap means to deceive or trick into difficulty, which clearly is an abusive action. The problem is one of balancing values. If the researcher obtains permission to observe someone, the subject may not act naturally. So, at times there is a strong temptation to observe without obtaining consent. In other times, such as monitoring mall traffic, obtaining consent just to observe people walking through the mall would be difficult.

‘‘What we see depends mainly on what we look for.’’—SIR JOHN LUBBOCK

contrived observation

Observation in which the investigator creates an artificial environment in order to test a hypothesis.

observer bias

A distortion of measurement resulting from the cognitive behavior or actions of a witnessing observer.

CHAPTER 11 Observation Methods • 241

So, when should researchers feel comfortable collecting observational data? While exceptions exist to every rule, here are three questions that can help address this question:

1. Is the behavior being observed commonly performed in public where it is expected that others can observe the behavior?

2. Is the behavior performed in a setting in which the anonymity—meaning there is no way to identify individuals— of the person being observed is assured?

3. Has the person agreed to be observed?

If the answers to the first two questions are yes, then there is not likely a violation of privacy in collecting observational research data. If the answer to the third question is yes, then gathering the data also is likely to be ethical.

Observation of Physical Objects Physical phenomena may be the subject of observation study. Physical-trace evidence is a visible mark of some past event or occurrence. For example, the wear on library books indirectly indicates which books are actually read (handled most) when checked out. A classic example of physical-trace evidence in a nonprofit setting was erosion on the floor tiles around the hatching-chick exhibit at Chicago’s Museum of Science and Industry. These tiles had to be replaced every six weeks; tiles in other parts of the museum did not need to be replaced for years. The selective erosion of tiles, indexed by the replacement rate, was a measure of the relative popularity of exhibits.

Clearly, a creative business researcher has many options for determining the solution to a problem. The story about Charles Coolidge Parlin, generally recognized as one of the founders of commercial business research, counting garbage cans at the turn of the twentieth century illustrates another study of physical traces.

Parlin designed an observation study to persuade Campbell’s Soup Company to advertise in the Saturday Evening Post. Campbell’s was reluctant to advertise because it believed that the Post was read primarily by working people who would prefer to make soup from scratch, peeling the potatoes and scraping the carrots, rather than paying 10 cents for a can of soup. To demon- strate that rich people weren’t the target market, Parlin selected a sample of Philadelphia garbage routes. Garbage from each specific area of the city that was selected was dumped on the floor of a local National Guard Armory. Parlin had the number of Campbell’s soup cans in each pile counted. The results indicated that the garbage from the rich people’s homes didn’t contain many cans of Campbell’s soup. Although they may not have made soup from scratch themselves, their housekeepers may have. The garbage piles from the blue-collar area showed a larger number of Campbell’s soup cans. This observation study was enough evidence for Campbell’s. They adver- tised in the Saturday Evening Post.5

The method used in this study has since been used in a scientific project at the University of Arizona in which aspiring archaeologists have sifted through garbage for over 30 years. They exam- ine soggy cigarette butts, empty milk cartons, and half-eaten Big Macs in an effort to understand modern life.

What is most interesting about the “Garbage Project” is that observations can be compared with the results of surveys about food consumption—and garbage does not lie. This type of obser- vation can correct for overreporting consumption of healthful items and underreporting of, say, cigarette or alcohol consumption.

‘‘What would you rather believe? What I say, or what you saw with your own eyes?’’—GROUCHO MARX

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Even if fashion companies could learn a lot about the types of problems consumers typically have when purchasing and wearing clothes, would observation through two-way mirrors be appropriate?

242 • PART THREE Research Methods for Collecting Primary Data

Another application of observing physical objects is to count and record physical inventories through retail or wholesale audits. This method allows researchers to investigate brand sales on regional and national levels, market shares, seasonal purchasing patterns, and so on. Business research suppliers offer audit data at both the retail and the wholesale levels.

An observer can record physical- trace data to discover information a respondent could not recall accurately. For example, measuring the number of ounces of liquid bleach used during a test provides precise physical-trace evi- dence without relying on the respon- dent’s memory. The accuracy of respondents’ memories is not a problem for the firm that conducts a pantry audit. The pantry audit requires an inventory of the brands, quantities, and package sizes in a consumer’s home rather than responses from individuals. The problem of untruthfulness or some other form of response bias is avoided. For example, the pantry audit prevents the possible problem of respondents erroneously claiming to have purchased prestige brands. However, gain- ing permission to physically check consumers’ pantries is not easy, and the fieldwork is expensive. In addition, the brand in the pantry may not reflect the brand purchased most often if consumers substituted it because they had a coupon, the usual brand was out of stock, or some other reason.

Content Analysis Besides observing people and physical objects, researchers may use content analysis, which obtains data by observing and analyzing the contents or messages of advertisements, newspaper articles, television programs, letters, and the like. This method involves systematic analysis as well as obser- vation to identify the specific information content and other characteristics of the messages. Con- tent analysis studies the message itself and involves the design of a systematic observation and recording procedure for quantitative description of the manifest content of communication. This technique measures the extent of emphasis or omission of a given analytical category. For example, content analysis of advertisements might evaluate their use of words, themes, characters, or space and time relationships. Another topic of content analysis is the frequency with which women, African-Americans, or ethnic minorities appear in mass media.

Content analysis might be used to investigate questions such as whether some advertisers use certain themes, appeals, claims, or deceptive practices more than others or whether recent consumer-oriented actions by the Federal Trade Commission have influenced the contents of advertising. A cable television programmer might do a content analysis of network programming to evaluate its competition. Every year researchers analyze the Super Bowl telecast to see how much of the visual material is live-action play and how much is replay, or how many shots focus on the cheerleaders and how many on spectators. Content analysis also can explore the information content of television commercials directed at children, the company images portrayed in ads, and numerous other aspects of advertising.

Study of the content of communications is more sophisticated than simply counting the items; it requires a system of analysis to secure relevant data. After one employee role-playing session involving leaders and subordinates, researchers analyzed videotapes to identify categories of ver- bal behaviors (e.g., positive reward statements, positive comparison statements, and self-evaluation requests). Trained coders, using a set of specific instructions, then recorded and coded the leaders’ behavior into specific verbal categories.

content analysis

The systematic observation and quantitative description of the manifest content of communication.

Picking through the garbage on the side of the road can reveal behaviors of fast-food customers.

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CHAPTER 11 Observation Methods • 243

Mechanical Observation In many situations, the primary—and sometimes the only—means of observation is mechanical rather than human. Video cameras, traffic counters, and other machines help observe and record behavior. Some unusual observation studies have used motion-picture cameras and time-lapse photography. An early application of this observation technique photographed train passengers and determined their levels of comfort by observing how they sat and moved in their seats. Another time-lapse study filmed traffic flows in an urban square and resulted in a redesign of the streets. Similar techniques may help managers determine how to better organize and arrange items in a warehouse or improve the design of store layouts to enhance traffic flow. Mechanical devices can also be utilized to observe employees and their actions when they can not be observed in person as illustrated in the Research Snapshot “ATTI, Inc. Shadows the Fleet”.

Television Monitoring Perhaps the best-known research project involving mechanical observation and computerized data collection is Nielsen’s television monitoring system for estimating national television audiences. Nielsen Media Research uses a consumer panel and a monitoring device called a peoplemeter to obtain ratings for television programs nationwide.6 The Nielsen peoplemeter gathers data on what each television in a household is playing and who is watching it at the time. Researchers attach

electronic boxes to television sets and remote con- trols to capture information on program choices and the length of viewing time. Nielsen matches the sig- nals captured through these devices with its database of network broadcast and cable program schedules so that it can identify the specific programs being viewed.

When a television in the panel household is turned on, a red light on the peoplemeter periodi- cally flashes to remind viewers to indicate who is watching. The viewer then uses a remote control to record who in the household is currently watching. One button on the control is assigned to each mem- ber of the household and a separate visitor button is used for potential guests. The household member presses his or her button to indicate the sex and age of the person who is watching. Knowing who in the family is watching allows executives to match televi- sion programs with demographic profiles.

Each night, Nielsen’s computers automatically retrieve the data stored in the peoplemeter’s record- ing box. In this way, Nielsen gathers daily estimates of when televisions are in use, which channels are used, and who is viewing each program. The panel includes more than five thousand households, selected to be representative of the U.S. population. For local pro- gramming, Nielsen uses additional panels equipped with recording devices but not peoplemeters to record viewer demographics. (Nielsen uses surveys to record demographic data for local programming.)

Critics of the peoplemeter argue that subjects in Nielsen’s panel grow bored over time and do not always record when they begin or stop watching television. Arbitron, best known for measuring radio audiences, has attempted to answer this objection

television monitoring

Computerized mechanical observation used to obtain

television ratings.

Believe it or not, this company can “observe” what radio station you are listening to.

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with its own measuring system, which it calls the Portable People Meter.7 The Portable People Meter, which occupies about four cubic inches and weighs less than three ounces, reads inaudible codes embedded in audio signals to identify their source. Study participants wear or carry the meter throughout the day, and it automatically picks up codes embedded in whatever radio and television signals they encounter. At the end of the day, the participant inserts the meter into a “base station,” which extracts the data collected, sends it to a household hub, and recharges the battery. The house- hold hub then sends the data to Arbitron’s computer over phone lines. To encourage cooperation, the meter has a motion sensor connected to a green light signaling that the meter senses it is being carried. Each participant is awarded points for the amount of time the meter is on. Total points are displayed in the base station and used to determine the size of the incentive paid to each participant. Arbitron’s meter simplifies the participants’ role and collects data on exposure to radio and television programming outside the home. How- ever, the device records only signals that the radio or television system embeds using Arbitron’s equipment.

Other devices gather data about the viewing of advertisements. The TiVo digital television recorder col- lects detailed viewing data, such as what commercials people skip by using fast- forward. The PreTesting Group sets up contrived observational studies in which viewers equipped with a remote con- trol are invited to watch any of three prerecorded channels playing different programs and advertisements, including the client’s ads to be tested.8 The system records the precise points at which the viewer changes the channel. By combin- ing the results from many participants, ©

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Traffic cameras that monitor speeding on major highways are becoming commonplace in Europe, Australia, and even in some parts of the United States. Would car companies learn anything from the observed behavior?

First incorporated in 1998, Advanced Tracking Technologies, Inc. (ATTI) has developed sophisticated monitoring and track- ing devices for fleet vehicles. While managers cannot directly observe their driver’s actions, ATTI’s Shadow Tracker Vision II modular vehicle tracking system provides detailed information about what is happening on the road. This electronic device can pinpoint the location of any equipped vehicle through its Global Positioning System (GPS), as well as record detailed trip information, including start and end times, distance traveled, average and top speed, idle time, off-hour usage, and the operator’s driving habits.

ATTI’s multiple product offerings provides managers with the answers to important questions:

■ Do my drivers report more hours than they should? ■ Do my drivers stop too long at their stops? ■ Are my drivers speeding? ■ Do my drivers wear their seat belts? ■ Are my drivers using our vehicles durirng off-hour periods?

■ Are we getting accurate mileage readings?

■ Have fault codes/ malfunction indicators gone unreported?

Not only is this observation technique useful to manage driv- ers, but it can assist fleet managers with many of their responsi- bilities. The service requirements for the vehicle can be carefully monitored, dispatchers know which driver is closest to the next service call, and the company can easily download data for route analysis, mileage reports, fuel tax reports, and state line crossings for tax purposes. An important issue for any manager is monitor- ing employees who are out in the field. The ATTI Shadow Tracker System is like having a manager riding with every driver.

Source: Advanced Tracking Technologies, Inc. (www.advantrack.com) accessed August 22, 2011.

ATTI, Inc. Shadows the Fleet

RESEARCH SNAPSHOT

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the company arrives at a Cumulative Zapping Score, that is, the percentage of viewers who had exited the client’s advertisement by each point in the ad. So that viewing behavior will be more natural, subjects are told they are evaluating the programming, not the ads.

Monitoring Website Traffic Computer technology makes gathering detailed data about online behavior easy and inexpensive. The greater challenges are to identify which measures are meaningful and to interpret the data cor- rectly. For instance, most organizations record the number of hits at their websites—mouse clicks on a single page of a website. If the visitor clicks on many links, that page receives multiple hits. Similarly, they can track page views, or single, discrete clicks to load individual pages of a website. Page views more conservatively indicate how many users visit each individual page on the website and may also be used to track the path or sequence of pages that each visitor follows.

Click-Through Rates A click-through rate (CTR) is the percentage of people who are exposed to an advertisement who actually click on the corresponding hyperlink which takes them to the company’s website. Count- ing hits or page views can suggest the amount of interest or attention a website is receiving, but these measures are flawed. First, hits do not differentiate between a lot of activity by a few visitors and a little activity by many visitors. In addition, the researcher lacks information about the mean- ing behind the numbers. If a user clicks on a site many times, is the person finding a lot of useful or enjoyable material, or is the user trying unsuccessfully to find something by looking in several places? Additionally, some hits are likely made by mistake. The consumers may have had no intention of clicking through the ad or may not have known what they were doing when they clicked on the ad.

A more refined count is the number of unique visitors to a website. This measurement counts the initial access to the site but not multiple hits on the site by the same visitor during the same day or week. Operators of websites can collect the data by attaching small files, called cookies, to the computers of visitors to their sites and then tracking those cookies to see whether the same visitors return. Some research companies, notably Jupiter Research and Nielsen, specialize in monitor- ing this type of Internet activity. A typical approach is to install a special tracking program on the personal computers of a sample of Internet users who agree to participate in the research effort. Nielsen has its software installed in thirty thousand computers in homes and workplaces. Internet monitoring enables these companies to identify the popularity of websites. In recent years, accurate measurement of unique visitors has become more difficult, because over half of computer users have deleted cookies and many users block cookies to make themselves anonymous.9

As online advertising has become commonplace, business research has refined methods for measuring the effectiveness of the advertisements. The companies that place these ads can keep count of the CTR. Applying the CTR to the amount spent on the advertisement gives the adver- tiser a cost per click. These measures have been hailed as a practical way to evaluate advertising effectiveness. However, marketers have to consider that getting consumers to click on an ad is rarely the ad’s objective. Companies are more often advertising to meet short- or long-term sales goals.

Google has benefited from CTR research indicating that the highest click-through rates tend to occur on pages displaying search results. (Not surprisingly, someone who searches for the term kayaks is more likely to be interested in an advertisement offering a good deal on kayaks.) The company showed Vanguard, for example, that its banner ads cost the financial firm less than 50 cents per click and generated a 14 percent click-through rate. That CTR is far above typical response rates for direct- mail advertising, but it does not indicate whether online clicks are as valuable in terms of sales.10

Scanner-Based Research Lasers performing optical character recognition and bar code technology like the Universal Product Code (UPC) have accelerated the use of mechanical observation in business research. Chapter 8 noted that a number of syndicated services offer secondary data about product category movement generated from retail stores using scanner technology.

click-through rate

Proportion of people who are exposed to an Internet ad who

actually click on its hyperlink to enter the website;

click-through rates are generally very low.

246 • PART THREE Research Methods for Collecting Primary Data

This technology allows researchers to investigate questions that are demographically or pro- motionally specific. Scanner research has investigated the different ways consumers respond to price promotions and the effects of those differences on a promotion’s profitability. One of the pri- mary means of implementing this type of research is through the establishment of a scanner-based consumer panel to replace consumer purchase diaries. In a typical scanner panel, each household is assigned a bar-coded card, like a frequent-shopper card, which members present to the clerk at the register. The household’s code number is coupled with the purchase information recorded by the scanner. In addition, as with other consumer panels, background information about the household obtained through answers to a battery of demographic and psychographic survey questions can also be coupled with the household code number.

Aggregate data, such as actual store sales as measured by scanners, are available to clients and industry groups. Data may also be aggregated by product category. To interpret the aggregated data, researchers can combine them with secondary research and panel demographics. For instance, data from Information Resources Inc. (IRI) have indicated a downward trend in sales of hair-coloring products. Demographic data suggest that an important reason is the aging of the population; many consumers who dye their hair reach an age at which they no longer wish to cover their gray hair. A smaller segment of the population is at an age where consumers typically begin using hair coloring.11

Data from scanner research parallel data provided by a standard mail diary panel, with some important improvements:

1. The data measure observed (actual) purchase behavior rather than reported behavior (recorded later in a diary).

2. Substituting mechanical for human record-keeping improves accuracy. 3. Measures are unobtrusive, eliminating interviewing and the possibility of social desirability or

other bias on the part of respondents. 4. More extensive purchase data can be collected, because all UPC categories are measured. In a

mail diary, respondents could not possibly reliably record all items they purchased. Because all UPC-coded items are measured in the panel, users can investigate many product categories to determine loyalty, switching rates, and so on for their own brands as well as for other compa- nies’ products and locate product categories for possible market entry.

5. The data collected from computerized checkout scanners can be combined with data about the timing of advertising, price changes, displays, and special sales promotions. Researchers can scrutinize them with powerful analytical software provided by the scanner data providers.

Scanner data can show a researcher week-by-week how a product is doing, even in a single store, and track sales in response to changes of sales personnel, local advertising, or price promotions. Also, several organizations have developed scanner panels, such as Information Resources Inc.’s Behavior Scan system, and expanded them into electronic test-market systems.

Measuring Physiological Reactions Researchers have used a number of mechanical devices to evaluate physical and physiological reactions to advertising copy, packaging, and other stimuli. Researchers use such means when they believe consumers are unaware of their own reactions to stimuli such as advertising or that consumers will not provide honest responses. Recent research approaches use devices to monitor and measure brain activity as described in the Research Snapshot “Neuroco Peers into the Con- sumer’s Brain”. Four major categories of mechanical devices are used to measure physiological reactions: (1) eye-tracking monitors, (2) pupilometers, (3) psychogalvanometers, and (4) voice- pitch analyzers.

A magazine or newspaper advertiser may wish to grab readers’ attention with a visual scene and then direct it to a package or coupon. Or a television advertiser may wish to identify which selling points to emphasize. Eye-tracking equipment records how the subject reads a print ad or views a television commercial and how much time is spent looking at various parts of the stimu- lus. In physiological terms, the gaze movement of a viewer’s eye is measured with an eye-tracking monitor, which measures unconscious eye movements. Originally developed to measure astronauts’

eye-tracking monitor

A mechanical device used to observe eye movements; some eye monitors use infrared light beams to measure unconscious eye movements.

scanner-based consumer panel

A type of consumer panel in which participants' purchasing habits are recorded with a laser scanner rather than a purchase diary.

CHAPTER 11 Observation Methods • 247

248

eye fatigue, modern eye-tracking systems need not keep a viewer’s head in a stationary position. The devices track eye movements with invisible infrared light beams that lock onto a subject’s eyes. The light reflects off the eye, and eye-movement data are recorded while another tiny video camera monitors which magazine page is being perused. The data are analyzed by computer to determine which components in an ad (or other stimuli) were seen and which were overlooked. Eye-tracking monitors have recently been used to measure the way subjects view e-mail and web messages. OgilvyOne has used this technology to learn that people often skip over more than half of the words in e-mail advertising, especially words on the right side of the message. Interestingly, consumers generally ignore the word free.12

Other physiological observation techniques are based on a common principle: that adrenaline is released when the body is aroused. This hormone causes the heart to enlarge and to beat harder and faster. These changes increase the flow of blood to the fingers and toes. The blood vessels dilate, and perspiration increases, affecting the skin’s electrical conductivity. Other physical changes following the release of adrenaline include dilation of the pupils, more frequent brain wave activ- ity, higher skin temperature, and faster breathing. Methods that measure these and other changes associated with arousal can apply to a variety of business questions, such as subjects’ reactions to advertising messages or product concepts.

A pupilometer observes and records changes in the diameter of a subject’s pupils. A subject is instructed to look at a screen on which an advertisement or other stimulus is projected. When the brightness and distance of the stimulus from the subject’s eyes are held constant, changes in pupil size may be interpreted as changes in cognitive activity that result from the stimulus, rather than from eye dilation and constriction in response to light intensity, distance from the object, or other physiological reactions to the conditions of observation. This method of research is based on the assumption that increased pupil size reflects positive attitudes toward and interest in advertisements.

A psychogalvanometer measures galvanic skin response (GSR), a measure of involun- tary changes in the electrical resistance of the skin. This device is based on the assumption that

pupilometer

A mechanical device used to observe and record changes

in the diameter of a subject's pupils.

psychogalvanometer

A device that measures galvanic skin response, a

measure of involuntary changes in the electrical

resistance of the skin.

When Hewlett-Packard was developing advertisements for its digital photography products, the firm wanted to ensure its ad images would evoke the desired response. For guidance, the company turned to Neuroco (now NeuroFocus, a Nielson Com- pany) and its high-tech research method, known as neuromar- keting. Neuroco researchers showed subjects a pair of photos of the same woman, and about half of them preferred each picture. Then Neuroco measured the electrical activity in the brains of subjects looking at the same images, and the analysis showed a definite preference for one of the pictures in which the woman’s smile was a little warmer.

Neuroco’s approach uses a technology called quantified electroencephalography (QEEG). Subjects wear light and porta- ble EEG equipment that records brain activity; software presents the data in computer maps that display activity levels in areas of the brain. Researchers can then evaluate whether the person is attentive and whether brain activity signifies emotional involve- ment or analytical thinking. QEEG is more flexible than the better-known use of functional magnetic resonance imaging (fMRI), which has provided many advances in brain research but requires all subjects to lie still in a large, noisy machine. With QEEG, the measuring equipment can travel with subjects as they walk around a store or watch advertisements.

Consider a young woman demonstrat- ing a Neuroco study by shopping with electrodes discreetly attached to her head. Neuroco chief scientist David Lewis observes a computer screen showing a map of her brain waves in red and green, with the colors signaling levels of alpha-wave activity. The zigzag pattern tells Lewis that this shopper is alert but not engaged in making purchase decisions. As the woman walks into a store’s shoe department, however, the pattern changes when she picks up a pair of stiletto heels. An explosion of brain activity occurs, then the woman heads for the cash register, decision made.

As this example illustrates, observation can provide tremen- dous insight to businesses. Advances in observation technology are literally providing a view of what is happening inside the brain.

Source: Based on Mucha, Thomas, “This Is Your Brain on Advertising,” Business 2.0 (August 2005), http://money.cnn.com/magazines/business2/ business2_archive/2005/08/01/8269671/index.htm, accessed August 9, 2011; and Laybourne, Peter, and David Lewis, “Neuromarketing: The Future of Consumer Research?” Admap (May 2005), 28–30. See also www.neurofocus.com.

Neuroco Peers into the Consumer’s Brain

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physiological changes, such as increased perspiration, accompany emotional reac- tions to advertisements, packages, and slogans. Excitement increases the body’s perspiration rate, which increases the electrical resistance of the skin. The test is an indicator of emotional arousal or tension.

Voice-pitch analysis is a relatively new physiological measurement tech- nique that gauges emotional reactions as reflected in physiological changes in a person’s voice. Abnormal frequencies in the voice caused by changes in the autonomic nervous system are measured with sophisticated, audio-adapted com- puter equipment. Computerized analysis compares the respondent’s voice pitch during warm-up conversations (normal range) with verbal responses to questions about his or her evaluative reaction to television commercials or other stimuli. This technique, unlike other physiological devices, does not require the researcher to surround subjects with mazes of wires or equipment.

All of these devices assume that physiological reactions are associated with persuasiveness or predict some cognitive response. This assumption has not yet been clearly demonstrated. No strong theoretical evidence supports the argument that such a physiological change is a valid measure of future sales, attitude change, or emotional response. Another major problem with physiological research is the calibration, or sensitivity, of measuring devices. Identifying arousal is one thing, but precisely measuring levels of arousal is another. In addition, most of these devices are expensive. However, as a prominent researcher points out, physiological measurement is coincidental: “Physi- ological measurement isn’t an exit interview. It’s not dependent on what was remembered later on. It’s a live blood, sweat, and tears, moment-by-moment response, synchronous with the stimulus.”13

Each of these mechanical devices has another limitation: the subjects are usually placed in arti- ficial settings, such as watching television in a laboratory rather than at home, and the participants know they are being observed.

voice-pitch analysis

A physiological measurement technique that records abnormal frequencies in the voice that are supposed to reflect emotional reactions to various stimuli.

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Physiological responses to advertising can be recorded with a device like this one.

SUMMARY

1. Discuss the role of observation as a business research method. Observation is a powerful tool for the business researcher. Scientific observation is the systematic process of recording the behavioral patterns of people, objects, and occurrences as they are witnessed. Questioning or otherwise communicating with subjects does not occur. A wide variety of information about the behavior of people and objects can be observed. Seven kinds of phenomena are observable: physi- cal actions, verbal behavior, expressive behavior, spatial relations and locations, temporal patterns, physical objects, and verbal and pictorial records. Thus, both verbal and nonverbal behavior may be observed.

2. Describe the use of direct observation and contrived observation. Human observation, whether direct or contrived, is commonly used when the situation or behavior to be recorded is not easily predictable in advance of the research. It may be unobtrusive, and many types of data can be obtained more accurately through direct observation than by questioning respondents. Direct observation involves watching and recording what naturally occurs, without creating an artificial situation. For some data, observation is the most direct or the only method of collection.

CHAPTER 11 Observation Methods • 249

For example, researchers can measure response latency, the time it takes individuals to choose between alternatives. Observation can also be contrived by creating the situations to be observed, such as with a mystery shopper or a research laboratory. This can reduce the time and expense of obtaining reactions to certain circumstances.

3. Identify ethical issues in observation studies. Contrived observation, hidden observation, and other observation research designs have the potential to involve deception. For this rea- son, these methods often raise ethical concerns about subjects’ right to privacy and right to be informed. We mentioned three questions to help determine the ethicality of observation: (1) Is the behavior being observed commonly performed in public where others can observe it? (2) Is anonymity of the subject assured? (3) Has the subject agreed to be observed? If the answers to 1 and 2 are “yes,” or if the answer to 3 is “yes,” the observation is likely ethical.

4. Explain the observation of physical objects and message content. Physical-trace evidence serves as a visible record of past events. Researchers may examine whatever evidence provides such a record, including inventory levels, the contents of garbage cans, or the items in a consum- er’s pantry. Content analysis obtains data by observing and analyzing the contents of the messages in written or spoken communications.

5. Describe major types of mechanical observation. Mechanical observation uses a variety of devices to record behavior directly. It may be an efficient and accurate choice when the situation or behavior to be recorded is routine, repetitive, or programmatic. National television audience ratings are based on mechanical observation (for example, Nielsen’s peoplemeters) and comput- erized data collection. website traffic may be measured electronically. Scanner-based research provides product category sales data recorded by laser scanners in retail stores. Many syndicated services offer secondary data collected through scanner systems.

6. Summarize techniques for measuring physiological reactions. Physiological reactions, such as arousal or eye movement patterns, may be observed using a number of mechanical devices. Eye-tracking monitors identify the direction of a person’s gaze, and a pupilometer observes and records changes in the diameter of the pupils of subjects’ eyes, based on the assumption that a larger pupil signifies a positive attitude. A psychogalvanometer measures galvanic skin response as a signal of a person’s emotional reactions. Voice-pitch analysis measures changes in a person’s voice and associates the changes with emotional response.

KEY TERMS AND CONCEPTS click-through rate, 246 content analysis, 243 contrived observation, 241 direct observation, 240 eye-tracking monitor, 247

hidden observation, 237 observation, 236 observer bias, 241 psychogalvanometer, 248 pupilometer, 248

response latency, 240 scanner-based consumer panel, 247 television monitoring, 244 visible observation, 237 voice-pitch analysis, 249

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Yogi Berra, former New York Yankee catcher, said, “You can

observe a lot just by watching.” How does this fit in with the definition of scientific observation?

2. What are the advantages and disadvantages of observation studies relative to surveys?

3. Under what conditions are observation studies most appropriate? 4. ETHICS The chapter showed a photograph of a traffic monitor-

ing camera. Do you think the use of these cameras to issue speeding tickets is ethical? What types of behavior might cam- eras like these capture that would help automobile designers produce products that better match our needs as drivers?

5. A multinational fast-food corporation plans to locate a restaurant in La Paz, Bolivia. Secondary data for this city are sketchy and outdated. How might you determine the best location using observation?

6. Discuss how an observation study might be combined with a personal interview.

7. ‘NET Click-through rates for advertisements placed in websites are usually very, very low (less than 1 percent). What types of error might exist in using click-through rate data as a measure of an ad’s success?

250 Part 3: Research Methods for Collecting Primary Data

RESEARCH ACTIVITIES 1. ‘NET William Rathje, a distinguished professor (emeritus) at the

University of Arizona, Department of Anthropology, became well-known for the “Garbage Project.” The project involves observational research. Use http://www.ask.com to find infor- mation about the Garbage Project at the University of Arizona. What is the name of the book that describes some of the key

findings of the Garbage Project? How do you think it involves observational research?

2. ‘NET The Internet is filled with webcams. For example, Pebble Beach Golf Club has several webcams (http://www .pebblebeach.com). How could a researcher use webcams like these to collect behavioral data?

Mazda and Syzygy When Mazda Motor Europe set out to improve its website, the company wanted details about how consumers were using the site and whether find-

ing information was easy. Mazda hired a research firm called Syzygy to answer those questions with observational research.14 Syzygy's methods include the use of an eye-tracking device that uses infrared light rays to record what areas of a computer screen a user is viewing. For instance, the device measured the pro- cess computer users followed in order to look for a local dealer or arrange a test drive. Whenever a process seemed confusing or difficult, the company looked for ways to make the website easier to navigate.

To conduct this observational study, Syzygy arranged for 16 sub- jects in Germany and the United Kingdom to be observed as they used the website. The subjects in Germany were observed with the

eye-tracking equipment. As the equipment measured each subject’s gaze, software recorded the location on the screen and graphed the data. Syzygy’s results included three-dimensional contour maps high- lighting the “peak” areas where most of the computer users’ attention was directed.

Questions 1. What could Mazda learn from eye-tracking software that would

be difficult to learn from other observational methods? 2. What are the shortcomings of this method? 3. Along with the eye-tracking research, what other research

methods could help Mazda assess the usability of its website? Summarize your advice for how Mazda could use complemen- tary methods to obtain a complete understanding of its website’s usability.

CASE 11.1

8. Outline a research design using observation for each of the fol- lowing situations: a. A bank wishes to collect data on the number of cus-

tomer services and the frequency of customer use of these services.

b. A state government wishes to determine the driving public's use of seat belts.

c. A researcher wishes to know how many women have been featured on Time covers over the years.

d. A human resource manager wants to know what sala- ries their key competitors are offering for some common positions.

e. A fast-food restaurant manger wishes to determine if they serve their customers as quickly as their competitors.

f. A magazine publisher wishes to determine exactly what people look at and what they pass over while reading one of its magazines.

g. An overnight package delivery service wishes to observe delivery workers beginning at the moment when they stop the truck, continuing through the delivery of the package, and ending when they return to the truck.

9. What is a scanner-based consumer panel? 10. What are the major types of mechanical observation? 11. ETHICS Comment on the ethics of the following situations:

a. During the course of telephone calls to investors, a stock- broker records respondents’ voices when they are answering sensitive investment questions and then conducts a voice- pitch analysis. The respondents do not know that their voices are being recorded.

b. A researcher plans to invite consumers to be test users in a simulated kitchen located in a shopping mall and then to videotape their reactions to a new microwave dinner from behind a two-way mirror (one that an observer behind the mirror can see through but the person looking into the mir- ror sees only the reflection).

c. A researcher arranges to purchase the trash from the head- quarters of a major competitor. The purpose is to sift through discarded documents to determine the company’s strategic plans.

12. What is a psychogalvanometer?

Chapter 11: Observation Methods 251

Texas Instruments and E-Lab E-Lab, LLC is a business research and design firm in Chicago that specializes in observing people, identifying patterns in behavior, and developing an

understanding of why these patterns exist.15 The company then uses the knowledge that it gains as a framework in the product develop- ment process. Texas Instruments (TI) used E-Lab to investigate the mobility, connectivity, and communications needs of law enforce- ment officers, which led to ideas for a set of computing and com- munications products. As part of its product development research, TI’s Advanced Integrated Systems Department and E-Lab research- ers spent 320 hours shadowing police officers in three Texas police departments. Shadowing involves asking questions while observing. Researchers walked foot patrols, rode in patrol cars, and pedaled with bike patrols. They spent time with crowd control, narcotics, homicide, dispatch, and juvenile teams. They recorded their obser- vations and interviews on paper, digital camera, and video.

A number of interesting findings emerged from all this research. First, police officers are very social, so it was important that any product TI developed should enhance socialization rather than

detract from it. For example, an in-car computing and communica- tions device should be able to access a database that lists names and numbers of experts on the force so officers can call or e-mail the experts directly. Second, police officers are not driven by procedure. That told TI that the procedures for an investigation should reside in the device and that the device should prompt the officer at each step in the process. And third, officers rely on informal information about people and activities on their beats. This information may be kept on scraps of paper, on a spreadsheet back in the office, or in the police officer’s head. Business researchers concluded that any device that TI develops should have a place to compile and share informal information.

Questions

1. Identify the research design used by E-Lab. 2. Compare this research design with a survey research design.

What advantages, if any, did this research design have over a survey?

CASE 11.2

252 Part 3: Research Methods for Collecting Primary Data

253

12 C H A P T E R

After studying this chapter, you should be able to

1. Identify the independent variable, dependent variable, and construct a valid simple experiment to assess a cause and effect relationship

2. Understand and minimize systematic experimental error

3. Know ways of minimizing experimental demand characteristics

4. Avoid unethical experimental practices

5. Understand the advantages of a between-subjects experimental design

6. Weigh the trade-off between internal and external validity

7. Use manipulations to implement a completely randomized experimental design, a randomized-block design, and a factorial experimental design

LEARNING OUTCOMES

Experimental Research

T echnological advances have drastically changed the way we conduct banking and related financial services. ATMs, online banking, real-time stock trading, and other web services have created a

globally accessible 24-hour-a-day financial market. For most of us, how this happens is not that relevant. However, for information technology directors and financial managers, how this process occurs—and how to make it occur faster and cheaper—is vitally important. This vignette describes an experiment examining two protocols for transferring information over computer networks.

FIX (Financial Information eXchange) is the commonly used electronics communication protocol for global real-time information exchange for securities transactions and markets. With the annual volume of trade in trillions of dollars, financial service firms are constantly seeking ways to cost-effectively increase the speed of access to financial markets. SOAP (Simple Object Access Protocol) is a potential competitor for the FIX method of information exchange.

Researchers designed a laboratory experiment to compare the relative performance of FIX and SOAP in business com- puting scenarios.1 By creating identical conditions—transfer- ring the same information, using the same computers, over the same Ethernet connection—the performance of the two protocols was assessed. The researchers measured the time

Chapter Vignette:

Testing Web Protocols for Financial Markets

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it took for a round-trip message from the client to the server and back. The results indicate that FIX remains a faster proto- col for transferring financial market information. While most of us do not fully understand the difference between FIX and SOAP—and even fewer really care—it is not necessary for us to understand the purpose of the experiment. This experiment

answers an important question to the financial world, “How does the performance of these two approaches compare?” However, an understanding of the process of testing these two approaches is very important to business researchers. This chapter provides a basic understanding of experimental busi- ness research.

Introduction Most students are familiar with scientific experiments from studying physical sciences like physics and chemistry. The term experiment typically conjures up an image of a chemist surrounded by bubbling test tubes and Bunsen burners. Behavioral and physical scientists have used experimen- tation far longer than have business researchers. Nevertheless, both social scientists and physical scientists use experiments for much the same purpose—to assess cause and effect relationships.

Creating an Experiment As described in an earlier chapter, experiments are widely used in causal research designs. Experimen- tal research allows a researcher to control the research situation so that causal relationships among vari- ables may be evaluated. The experimenter manipulates one or more independent variables and holds constant all other possible independent variables while observing effects on dependent variable(s). Events may be controlled in an experiment to a degree that is simply not possible in a survey.

Independent variables are expected to determine the outcomes of interest. In an experiment, they are controlled by the researcher through manipulations. Dependent variables are the out- comes of interest to the researcher and the decision makers. A simple example would be thinking about how changes in price would influence sales. Price would be an independent variable and sales would be a dependent variable. In our opening vignette, the protocol used (FIX or SOAP) would be an experimental manipulation—the independent variable—and the speed of data trans- mission is the important dependent variable.

The researcher’s goal in conducting an experiment is to determine whether changing an exper- imental independent variable causes changes in the specified dependent variable. The assumption of the experiment described above is that the type of protocol used will affect the speed of finan- cial data transfer. In other words, changing from FIX to SOAP will increase or decrease the time required for data transfer. If all the other conditions are the same, then a causal inference is supported.

A famous experiment in the marketing field investigated the influence of brand name on consumers’ taste perceptions. An experimenter manipulated whether consumers preferred the taste of beer in labeled or unlabeled bottles. One week respondents were given a six-pack containing bottles labeled only with letters (A, B, C). The following week, respondents received another six- pack with brand labels (like Budweiser, Coors, Miller, and so forth). The experimenter measured reactions to the beers after each tasting. In every case, the beer itself was the same. So, every person involved in the experiment drank the very same beer. Therefore, the differences observed in taste, the key dependent variable, could only be attributable to the difference in labeling. When the con- sumers participating in the experiment expressed a preference for the branded beer, the conclusion is that brand name does influence consumers’ taste perceptions.

An Illustration: Can a Self-Efficacy Intervention Enhance Job Attitude? This chapter deals with business experiments, which can best be illustrated through examples like the opening vignette and the one which follows. We will refer back to these examples throughout the chapter.

254 • PART THREE Research Methods for Collecting Primary Data

255

Let’s take a look at an experiment investigating how self-efficacy might influence an employ- ee’s attitude toward their job.2 Self-efficacy is a person’s confidence and belief in their own abilities to accomplish the tasks at hand. While the subjects of this particular research are accountants, and the results are highly relevant for those involved in fields like human resource management, it has implications for anyone in a managerial role. The key issue centers on a manager’s ability to raise an employee’s confidence in their ability to perform their job and the favorable outcomes of this increased confidence.

Experimental Subjects This experiment involved actual employees of an accounting firm. Seventy-one first and second year auditors of one major accounting firm participated in the study. Participants in experimen- tal research are referred to as subjects rather than respondents. This is because the experimenter subjects them to some experimental treatment. In this experiment, 35 of the subjects were given positive feedback and encouragement from their supervisors as the experimental treatment. The other 36 subjects were not provided the positive feedback.

Independent Variables The experiment involved one relevant independent variable, whether or not the employee received the positive feedback intended to enhance their self-efficacy. Employees receiving the experimen- tal treatment participated in an interview and received three different pieces of written communi- cation from their supervisors providing encouragement and expressing confidence that they would be successful in their positions.

While not a true independent variable, the length of time each employee had worked with the firm was also important to the researchers. Could new employees react differently to the positive feedback than employees who had already been with the firm? In this case, length of time cannot be manipulated by the researchers, but it can still be considered in the experiment. Variables such as this (another example would be the sex of the experimental sub- ject) are referred to as blocking variables, which are discussed in more detail later in this chapter. Considering the independent vari- able (treatment or no treatment) and the blocking variable (new or current employee), four different experimental cells are possible. Exhibit 12.1 illustrates the four different experimental conditions for this experiment. An experimental condition refers to one of the possible levels of an experimental variable manipulation.

The screenshot here shows the edit view from the Qualtrics website interface. The survey asked respondents several questions about prospective careers. Notice that we’ve included questions about marketing and man- agement (near the bottom of the screenshot). However, each subject only responded to a single occupation (marketing, management, finance, or accounting). This actually repre- sents a very simple experimental design in which the type of occupation described may cause the subjects’ responses to these ques- tions. Take a look at the data and see if you can determine whether or not students’ beliefs about careers are altered by the type of occupation they were assigned to rate. Here, job type becomes the experimental manipulation. Do you know the treatment levels?

subjects

The sampling units for an experiment, usually human respondents who provide measures based on the experimental manipulation.

blocking variables

A categorical (less than interval) variable that is not manipulated like an experimental variable but is included in the statistical analysis of experiments.

experimental condition

One of the possible levels of an experimental variable manipulation.

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An experiment can capture whether or not mangers can increase self-efficacy and enhance employee attitudes toward their job.

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Experimental Treatment

Blocking Variable No Feedback Received Feedback

Employment

Newcomers (New Employees)

22 participants 22 participants

Insiders (Current Employees)

14 participants 13 participants

Subjects were divided into “newcomers” (the new employees) and “insiders” (current employees) and then randomly assigned to either the treatment condition or the control group. By analyzing differences between the groups, the researcher can see what effects occur due to the independent and blocking variables.

Experimental Outcome The key outcomes, or dependent variables, in this example are a subject’s job satisfaction, organi- zational commitment, professional commitment, intent to quit the organization, and intent to quit the profession. In addition, the researchers followed up later to see if the subjects had actually left the firm. For simplicity, we will only look at the effect on one dependent variable, job satisfac- tion. In this case, subjects were asked to respond to a rating scale asking how much they agreed with a series of statements regarding their satisfaction with their job. The possible scores ranged from 1 to 7, where a higher score means higher job satisfaction.

Exhibit 12.2 shows the average for each experimental condition. The results show that after receiving the positive feedback and encouragement, subjects that were already working for the firm reported an average job satisfaction score of 5.45, while the new employees that received the treatment reported an average score of 5.93. For those subjects that did not receive the treatment, the current employees’ average score was 4.77 and the new employee’s average was 5.80.

EXHIBIT 12.1

Experimental Conditions in Self-Efficacy Experiment

Thus, the conclusion at this point seems to be that the difference in job satisfaction is primarily between the current and new employees. The self-efficacy treatment does not seem to have much impact. Or does it?

Independent Variable Main Effects and Interaction The length of time that the employee works at the firm clearly appears to matter. But maybe the attempts to enhance self-efficacy shouldn’t be dismissed so quickly. The researcher must examine both the effects of each experimental variable considered alone and the effects due to combina- tions of variables. A main effect refers to the experimental difference in means between the differ- ent levels of any single experimental variable. In this case, there are potential main effects for the

main effect

The experimental difference in dependent variable means between the different levels

of any single experimental variable.

EXHIBIT 12.2

Job Satisfaction Means in Self-Efficacy Experiment

Experimental Treatment

Blocking Variable No Feedback Received Feedback

Employment

Newcomers (New Employees)

5.80 5.93 5.87

Insiders (Current Employees)

4.77 5.45 5.10

5.38 5.68

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256 • PART THREE Research Methods for Collecting Primary Data

self-efficacy treatment and for the length of time as an employee, but only the differences associated with employment length are significant (at a .05 level).

An interaction effect is due to a specific combination of independent variables. In this case, it’s possible that the combination of length of employment and the self-efficacy treatment creates effects that are not clearly represented in the main effects. Interaction results are often shown with a line graph as shown in Exhibit 12.3. Main effects are illustrated when the lines are at different heights as is the case here. Notice the line for new employees is higher than the line for current employees. When the lines have very different slopes, an interaction is likely present. In this case, the combination of length of employment and self-efficacy treatment is presenting an interaction leading to the following interpretation.

The worst situation is the current employees who do not receive positive feedback. Conversely, the best scenario regarding job satisfaction occurs when the treatment is given to new employees. It also appears that job satisfaction tends to decrease over time. The benefit of the self-efficacy treat- ment is greater for the employees that have been with the organization than for new employees. In other words, it appears that the self-efficacy treatment helps prevent the decline in job satisfaction.

interaction effect

Differences in dependent variable means due to a specific combination of independent variables.

EXHIBIT 12.3

Experimental Graph Showing Results within Each Condition

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3.0 No Feedback Received Feedback

New Employee Current Employee

Designing an Experiment to Minimize Experimental Error Experimental design is a major research topic. In fact, there are courses and books devoted only to that topic.3 Here, an introduction into experimental design is provided. A student should be able to design and implement basic experimental designs with this introduction. Fortunately, most experimental designs for business research are relatively simple.

Experimental designs involve no less than four important design elements. These issues include (1) manipulation of the independent variable(s); (2) selection and measurement of the dependent variable(s); (3) selection and assignment of experimental subjects; and (4) control over extraneous variables.4 Each element can be implemented in a way that helps minimize error.

Manipulation of the Independent Variable The thing that makes independent variables special in experimentation is that the researcher actually creates his or her values. This is how the researcher manipulates, and therefore controls, independent

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CHAPTER 12 Experimental Research • 257

variables. In the financial market pro- tocol experiment, the researchers decided to test different protocols. In the self-efficacy example, the research- ers chose to provide some employees with positive feedback and not give the same encouragement to others. Experimental independent variables are hypothesized to be causal influ- ences. Therefore, experiments are very appropriate in causal designs.

An experimental treatment is the term referring to the way an experi- mental variable is manipulated. For example, the opening vignette manipu- lated the protocol by choosing FIX and SOAP for their test. In the self-efficacy study, the researchers had a personal interview with the employees and then had the supervisors send three encour-

aging letters to manipulate self-efficacy. Similarly, a medical researcher may manipulate an experimental variable by treating some subjects with one drug and the other subjects with a separate drug. Experi- mental variables often involve treatments with more than two levels. For instance, prices of $1.29, $1.69, and $1.99 might represent treatments in a pricing experiment examining how price affects sales.

Experimental variables like these can not only be described as independent variables, but they also can be described as a categorical variable because they take on a value to represent some classifiable or qualitative aspect. The protocol, for example, is either FIX or SOAP. The employees either received the feedback or they did not. In other situations an independent variable may truly be a continuous variable. For example, the pricing experiment mentioned above could involve any price levels. The task for the researcher is to select appropriate levels of that variable as experimen- tal treatments. For example, consumers might not perceive a difference between $1.24 and $1.29, but likely will notice the difference between $1.29 and $1.69. Before conducting the experiment, the researcher decides on levels that would be relevant to study. The levels should be noticeably different and realistic.

Experimental and Control Groups In perhaps the simplest experiment, an independent variable is manipulated over two treatment levels resulting in two groups, an experimental group and a control group. An experimental group is one in which an experimental treatment is administered. A control group is one in which no experimental treatment is administered. In our self-efficacy example, the experimental group is comprised of the subjects that received the positive feedback. The control group did not receive the additional positive feedback designed to enhance self-efficacy. By holding conditions constant in the control group, the researcher controls for potential sources of error in the experiment. Job satisfaction (the dependent variable) in the two groups was compared at the end of the experiment to determine whether the encouragement (the independent variable) had any effect.

Several Experimental Treatment Levels An experiment with one experimental and one control group may not tell a manager everything he or she wishes to know. If an advertiser wished to understand the functional nature of the rela- tionship between advertising and sales at several treatment levels, additional experimental groups with annual advertising expenditures of $250,000, $500,000, $750,000, and $1 million might be studied. This experiment may still involve a control variable (keeping the advertising budget for a region at the current level of $100,000). By analyzing more groups each with a different treatment

experimental treatment

The term referring to the way an experimental variable is

manipulated.

‘‘You never know what is enough unless you know what is more than enough.’’—WILLIAM BLAkE

experimental group

A group of subjects to whom an experimental treatment is

administered.

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Business research involving retail stores often involves experiments that manipulate different elements of the physical environment.

‘‘We are never deceived; we deceive ourselves.’’—JOHANN WOLFGANG

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control group

A group of subjects to whom no experimental treatment is

administered.

258 • PART THREE Research Methods for Collecting Primary Data

259

level, a more precise result may be obtained than in a simple experimental group–control group experiment. This design, only manipulating the level of advertising, can produce only a main effect. An example of an experiment using multiple treatment levels is briefly discussed in the Research Snapshot “Talking While Driving: Are Cell Phone Conversations Different from Passenger Conversations?”.

More Than One Independent Variable An experiment can also be made more complicated by including the effect of another experimen- tal variable. Our extended example of the self-efficacy experiment would typify a still relatively simple two-variable experiment. Since there are two variables, each with two different levels, four experimental groups are obtained. Often, the term cell is used to refer to a treatment combination within an experiment. The number of cells involved in any experiment can be easily computed as follows:

K = (T 1 )(T

2 )…(T

m )

where K = the number of cells, T 1 = the number of treatment levels for experimental group num-

ber 1, T 2 = the number of treatment levels for experimental group number 2, and so forth through

the mth experimental group (T m ). In this case, since there are two variables each with two levels,

the computation is quite simple:

K = 2 × 2 = 4 cells

Including multiple variables allows a comparison of experimental treatments on the dependent variable. Since there are more than two experimental variables, this design involves both main effects and interactions.

Cell phones seem to be everywhere—in particular, the increased use of cell phones while driving has led to increased driver distractions, and the potential for accidents and even fatalities. This is largely believed to be related to the fact that while a person is engaged in a conversation on a cell phone, they are less likely to be fully aware of what is happening as they are driving down a road. But people engage in conversations with fellow passengers as they drive as well. Is it the fact that they are in a conversation that leads to more accidents, or is it the fact that they are engaged in a conversation on a cell phone? Researchers designed an ingenious experiment with a driving simulator to test this interesting question.

The design of the experiment involved students who were told that they would be evaluating the quality of a driving simu- lator developed by a manufacturer. The experimental design involved the assignment of the students into three groups: A group with no passenger as a control group, a group that had a passenger who asked them questions while they simu- lated driving (and who was supposed to be a representative of the manufacturer) and a group where the driver engaged in a

cell phone conversation with the manufacturer representative while they drove the simulator. Statistical tests revealed that none of the students were aware that the study was actually focused on their performance as they drove and engaged in a conversation, whether it was with a passenger or on a cell phone.

The results indicated that driving while engaged in a con- versation on a cell phone was indeed much more distracting and dangerous than having a conversation with a fellow pas- senger. One possible explanation is that fellow passengers are able to also anticipate changing driving conditions, such as a sudden stop of a vehicle or a changing traffic signal ahead, and can adjust their engagement in conversation accordingly. Clearly, conversation with others is an important part of driving a vehicle. However, perhaps it is best to hold that cell phone conversation until you have arrived at your destination.

Source: James Hunton & Jacob M. Rose, “Cellular Telephones and Driving Performance: The Effects of Attentional Demands on Motor Vehicle Crash Risk,” (2005), Risk Analysis, 25(4): 855–866.

Talking While Driving: Are Cell Phone Conversations Different from Passenger Conversations?

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Refers to a specific treatment combination associated with an experimental group.

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RESEARCH SNAPSHOT

Repeated Measures Experiments in which an individual subject is exposed to more than one level of an experimental treatment are referred to as repeated measures designs. Although this approach has advantages, including being more economical since the same subject provides more data than otherwise, it has several drawbacks that can limit its usefulness. We will discuss these experiments in more detail later.

Selection and Measurement of the Dependent Variable Selecting dependent variables is crucial in experimental design. Unless the dependent variables are relevant and truly represent an outcome of interest, the experiment will not be useful. Sometimes, the dependent variable is fairly obvious. In the protocol example, the speed of the data exchange is an important and logical dependent measure. Other dependent measures, such as the number of errors in the data transmission, may also be of relevance. In the self-efficacy study, the researchers did consider several dependent measures in addition to job satisfaction (organizational commit- ment, professional commitment, intent to quit the profession, intent to quit the organization, and actual turnover). In some situations, however, clearly defining the dependent variable is not so easy. If researchers are experimenting with different forms of advertising copy appeals, defining the dependent variable may be more difficult. For example, measures of advertising awareness, recall, changes in brand preference, or sales might be possible dependent variables.

Choosing the right dependent variable is part of the problem definition process. Like the prob- lem definition process in general, it sometimes is considered less carefully than it should be. The experimenter’s choice of a dependent variable determines what type of answer is given to assist managers in decision making.

Consider how difficult one might find selecting the right dependent variable in an advertis- ing experiment. While sales are almost certainly important, when should sales be measured? What about brand image or recognition? The amount of time needed for effects to become evident should be considered in choosing the dependent variable. Sales may be measured several months after the changes in advertising to determine if there were any carryover effects. Changes that are relatively permanent or longer lasting than changes generated only during the period of the exper- iment should be considered. Repeat purchase behavior may be important too, since the advertising may motivate some consumers to try a product once, but then never choose that product again. Consumers often try a “loser” once, but they do not buy a “loser” again and again.

The introduction of the original Crystal Pepsi illustrates the need to think beyond consum- ers’ initial reactions. When Crystal Pepsi, a clear cola, was introduced, the initial trial rate was high, but only a small percentage of customers made repeat purchases. The brand never achieved high repeat sales within a sufficiently large market segment. Brand awareness, trial purchase, and repeat purchase are all possible dependent variables in an experiment. The dependent variable therefore should be considered carefully. Thorough problem definition will help the researcher select the most important dependent variable or variables.

Selection and Assignment of Test Units Test units are the subjects or entities whose responses to the experimental treatment are measured or observed. Individual consumers, employees, organizational units, sales territories, market segments, or other entities may be the test units. People, whether as customers or employees, are the most common test units in most organizational behavior, human resources, and marketing experiments.

Sample Selection and Random Sampling Errors As in other forms of business research, random sampling errors and sample selection errors may occur in experimentation. For example, experiments sometimes go awry even when a geographic area is specially chosen for a particular investigation. A case in point was the experimental test- ing of a new lubricant for outboard boat motors by Dow Chemical Company. The lubricant was tested in Florida. Florida was chosen because researchers thought the hot, muggy climate would

repeated measures

Experiments in which an individual subject is exposed to more than one level of an

experimental treatment.

test units

The subjects or entities whose responses to the experimental

treatment are measured or observed.

260 • PART THREE Research Methods for Collecting Primary Data

provide the most demanding test. In Florida the lubricant was a success. However, the story was quite different when the product was sold in Michigan. Although the lubricant sold well and worked well during the summer, the follow- ing spring Dow discovered the oil had congealed, allow- ing the outboard motors, idle all winter, to rust. The rusting problem never came to light in Florida, where the motors were in year-round use. Thus, sample selection error occurs because of flaws in procedures used to assign experimental test units. Florida conditions made the experiment irrelevant in Michigan.

Systematic or nonsampling error may occur if the sam- pling units in an experimental cell are somehow different than the units in another cell, and this difference affects the dependent variable. For example, suppose some professors are interested in testing the effect of providing snacks dur- ing exams on student’s scores. The experimental variable is snacks, manipulated over three levels: (1) fruit, (2) cookies, and (3) chocolate. The test units in this case are individual students. When the professors conduct the experiment, for convenience, they decide to give all of the 8:00 a.m. classes chocolate for a snack, all of the 1:00 p.m. classes get fruit, and all of the 7:00 p.m. classes get cookies. While this type of procedure is often followed, if our tastes and digestive systems react differently to different foods at different times of the day, systematic error is introduced into the experiment. Furthermore, because the night classes contain students who are older on average, the professors may reach the conclusion that students perform better when they eat cookies, when it may really be due to the fact that students who are older perform better no matter what they are fed.

Randomization Randomization—the random assignment of subject and treatments to groups—is one device for equally distributing the effects of extraneous variables to all conditions. These nuisance variables, items that may affect the dependent measure but are not of primary interest, often cannot be eliminated. However, they will be controlled because they are likely to exist to the same degree in every experimental cell if subjects are randomly assigned. In our self-efficacy experiment, it is likely that some subjects are happier with their positions to start with, have greater or lesser ability, and so forth. By randomly assigning employees to the control and experimental group, all these factors should balance out. Thus, all cells would be expected to yield similar average scores on the dependent variables if it were not for the experimental treatment administered. In other words, the researcher would like to set up a situation where everything in every cell is the same except for the experimental treatment. Random assignment of subjects allows the researcher to make this assumption.

Matching Random assignment of subjects to the various experimental groups is the most common tech- nique used to prevent test units from differing from each other on key variables; it assumes that all characteristics of the subjects have been likewise randomized. Matching the respondents on the basis of pertinent background information is another technique for controlling systematic error by assigning subjects in a way that their characteristics are the same in each group. This is best thought of in terms of demographic characteristics. If a subject’s sex is expected to influence dependent variable responses, as in a taste test, then the researcher may make sure that there are equal num- bers of men and women in each experimental cell. In general, if a researcher believes that certain extraneous variables may affect the dependent variable, he or she can make sure that the subjects in each group are the same on these characteristics.

For example, in an experiment examining three different training programs designed to develop leadership skills, it might be important to match the subjects on the basis of sex. That way,

systematic or nonsampling error

Occurs if the sampling units in an experimental cell are somehow different than the units in another cell, and this difference affects the dependent variable.

randomization

The random assignment of subject and treatments to groups; it is one device for equally distributing the effects of extraneous variables to all conditions.

nuisance variables

Items that may affect the dependent measure but are not of primary interest.

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Although experiments are often administered in groups, if all groups are not the same, then systematic error is introduced.

CHAPTER 12 Experimental Research • 261

the same number of men and women will be exposed to Program A, Program B, and Program C. While matching can be a useful approach for a handful of key factors, the researcher can never be sure that sampling units are matched on all characteristics. Here, for example, perhaps the subjects in the leadership experiment need to be matched on education, intelligence, and work experi- ence in addition to sex. It is easy to see the increasing complexity of trying to match the subjects on these four factors. However, even if this is accomplished, the researcher can still not know about other factors, such as interest in leadership, family influences, and various personality issues. As a result, random assignment is a more common approach to balancing subject characteristics than matching.

Control Over Extraneous Variables The fourth decision about the basic elements of an experiment concerns control over extrane- ous variables. This is related to the various types of experimental error. In an earlier chapter, we classified total survey error into two basic categories: random sampling error and systematic error. The same dichotomy applies to all research designs, but the terms random (sampling) error and systematic error are more frequently used when discussing experiments.

Experimental Confounds We have already discussed how systematic error can occur when the extraneous variables or the conditions of administering the experiment are allowed to influence the dependent variables. When this occurs, the results will be confounded because the extraneous variables have not been controlled or eliminated. A confound means that there is an alternative explanation beyond the experimental variables for any observed differences in the dependent variable. Once a potential confound is identified, the validity of the experiment is severely questioned.

Recall from the opening vignette the experimental procedures involved in the protocol test. The same data was sent over the two protocols in the experiment. What if the FIX protocol was better suited to handling small files, while the SOAP protocol was better suited for large files? If only small files were tested, the experiment has a confound. The size of the file is confounding the explanation that the FIX protocol is faster for sending financial data. In fact, it may depend on the size of the data file which protocol is faster. If large data sets had been used, the results may have indicated that SOAP was the faster protocol.

In a simple experimental group–control group experiment, if subjects in the experimental group are always administered treatment in the morning and subjects in the control group always receive the treatment in the afternoon, a systematic error occurs. In such a situation, time of day represents a confound. In a training experiment the sources of constant error might be the per- sons who do the training (line or external specialists) or whether the training is conducted on the employees’ own time or on company time. These and other characteristics of the training may have an impact on the dependent variable and will have to be taken into account:

The effect of a constant error is to distort the results in a particular direction, so that an erroneous difference masks the true state of affairs. The effect of a random error is not to distort the results in any particular direction, but to obscure them. Constant error is like a distorting mirror in a fun house; it produces a pic- ture that is clear but incorrect. Random error is like a mirror that has become cloudy with age; it produces a picture that is essentially correct but unclear.5

Extraneous Variables Most business students realize that the marketing mix variables—price, product, promotion, and distribution—interact with uncontrollable forces in the market, such as economic variables, com- petitor activities, and consumer trends. Thus, many marketing experiments are subject to the effect of extraneous variables. Since extraneous variables can produce confounded results, they must be identified before the experiment if at all possible.

One issue with significant business and public policy implications is cigarette smoking. Does cigarette advertising cause young people to smoke? Although this is an often asked question, it is far from settled. One of the primary reasons for the inconclusiveness of this debate is the failure for

confound

A confound means that there is an alternative explanation

beyond the experimental variables for any observed

differences in the dependent variable.

extraneous variables

Variables that naturally exist in the environment that may have

some systematic effect on the dependent variable.

262 • PART THREE Research Methods for Collecting Primary Data

most of the research to control for extraneous variables.6 For instance, consider a study in which two groups of U.S. high school students are studied over the course of a year. One is exposed to foreign television media in which American cigarettes are more often shown in a flattering and glamorous light. In fact, the programming includes cigarette commercials. The other group is a control group in which their exposure to media is not controlled. At the end of the year, the exper- imental group reports a greater frequency and incidence of cigarette smoking. Did the increased media exposure involving cigarettes cause smoking behavior?

While the result seems plausible at first, the careful researcher may ask the following questions:

■ Was the demographic makeup of the two groups the same? While it is clear that the ages of the two groups are likely the same, it is well known that different ethnic groups have different smoking rates. Approximately 28 percent of all high school students report smoking, but the rate is higher among Hispanic teens, for example.7 Therefore, if one group contained more Hispanics, we might expect it to report different smoking rates than otherwise. Similarly, smoking varies with social class. Were the two groups comprised of individuals from compa- rable social classes?

■ How did the control group fill the time consumed by the experimental group in being exposed to the experimental treatment? Could it be that it somehow dissuaded them from smoking? Perhaps they were exposed to media with more antismoking messages?

■ Were the two groups of the same general achievement profiles? Those who rate high in the need for achievement may be less prone to smoke than are other students.

■ Although it is a difficult task to list all possible extraneous factors, some that even sound unusual can sometimes have an effect. For example, did the students have equally dispersed birthdays? Researchers have shown that smoking rates correspond to one’s birthday, meaning that different astrological groups have different smoking rates.8

Because an experimenter does not want extraneous variables to affect the results, he or she must eliminate or control such variables. It is always better to spend time thinking about how to control for possible extraneous variables before the experiment, since often there is nothing that can be done to salvage results if a confounding effect is identified after the experiment is conducted.

Demand Characteristics What Are Demand Characteristics? The term demand characteristic refers to an experimental design element that unintention- ally provides subjects with hints about the research hypothesis. Researchers cannot reveal the research hypotheses to subjects before the experiment or else they can create a confounding effect. Think about the self-efficacy experiment. If the subjects learned that they were being intentionally given positive feedback to enhance their confidence and attitudes toward their job, the researcher would never be sure if their responses to the dependent variable were really due to the differences in the experimental stimuli or due to the fact that the subjects were trying to provide a “correct” response. Once subjects know the hypotheses, there is little hope that they will respond naturally.

A confound may be created by knowledge of the experimental hypothesis. This particular type of confound is known as a demand effect. Demand characteristics make demand effects very likely.

Experimenter Bias and Demand Effects Demand characteristics are aspects of an experiment that demand (encourage) that the sub- jects respond in a particular way. Hence, they are a source of systematic error (see Exhibit 12.4). If participants recognize the experimenter’s expectation or demand, they are likely to act in a man- ner consistent with the experimental treatment. Even slight nonverbal cues may influence their reactions.

demand characteristic

Experimental design element or procedure that unintentionally provides subjects with hints about the research hypothesis.

demand effect

Occurs when demand characteristics actually affect the dependent variable.

CHAPTER 12 Experimental Research • 263

Prominent demand characteristics are often presented by the person administering experi- mental procedures. If an experimenter’s presence, actions, or comments influence the subjects’ behavior or sway the subjects to slant their answers to cooperate with the experimenter, the experiment has introduced experimenter bias. When subjects slant their answers to cooperate with the experimenter, they are exhibiting behaviors that might not represent their behavior in the marketplace. For example, if subjects in an advertising experiment understand that the experimenter is interested in whether they changed their attitudes in accord with a given adver- tisement, they may answer in the desired direction. Acting in this manner reflects a demand effect rather than a true experimental treatment effect.

Hawthorne Effect A famous management experiment illustrates a common demand characteristic. Researchers were attempting to study the effects on productivity of various working conditions, such as hours of work, rest periods, lighting, and methods of pay, at the Western Electric Hawthorne plant in Cicero,

Illinois. The researchers found that work- ers’ productivity increased whether the work hours were lengthened or shortened, whether lighting was very bright or very dim, and so on. The surprised investigators realized that the workers’ morale was higher because they were aware of being part of a special experi- mental group. This totally unintended effect is now known as the Hawthorne effect because researchers realize that people will perform differently when they know they are experi- mental subjects.9

If subjects in a laboratory experiment inter- act (i.e., are not relatively isolated), their conversa- tions may produce joint decisions rather than a desired individual decision. For this reason, social interaction generally is restricted in laboratory experiments.

EXHIBIT 12.4

By Smiling or Looking Solemn, Experimenters Can Modify Subject’s Behavior

Hawthorne effect

People will perform differently from normal when they know

they are experimental subjects.

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The experimenter unintentionally can create a demand effect by smiling, nodding, or frowning at the wrong time.

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Reducing Demand Characteristics Although it is practically impossible to eliminate demand characteristics from experiments, there are steps that can be taken to reduce them. Many of these steps make it difficult for subjects to know what the researcher is trying to find out. Some or all of these may be appropriate in a given experiment.

1. Use an experimental disguise. 2. Isolate experimental subjects. 3. Use a “blind” experimental administrator. 4. Administer only one experimental treatment level to each subject.

Experimental Disguise Subjects participating in the experiment can be told that the purpose of the experiment is some- what different than the actual purpose. Most often, they are simply told less than the complete “truth” about what is going to happen. In other cases, more deceit may be needed. For example, psychologists studying how much pain one person may be willing to inflict on another might use a ruse telling the subject that they are actually interested in the effect of pain on human performance. The researcher tells the subject to administer a series of questions to another person (who is actu- ally a research assistant) and to provide them with an increasingly strong electric shock each time an incorrect answer is given. In reality, the real dependent variable has something to do with how long the actual subject will continue to administer shocks before stopping.

A placebo is an experimental deception involving a false treatment. A placebo effect refers to the corresponding effect in a dependent variable that is due to the psychological impact that goes along with knowledge of the treatment. A placebo is particularly important when the experimen- tal variable involves physical consumption of some product. The placebo should not be different in any observable manner from the true treatment that is actually noticeable by the research subject. Assume a researcher is examining the ability of a special food additive to suppress appetite. The additive is a product that is supposed to be sprinkled on food before it is eaten. The experimental group would be given the actual product to test, while the control group would be given a placebo that looks exactly like the actual food additive but is actually an inert compound. Both groups are likely to show some difference in consumption compared to someone undergoing no effect. The difference in the actual experimental group and the placebo group would represent the true effect of the additive.

Placebo effects exist in marketing research. For example, when subjects are told that an energy drink is sold at a discount price, they believe it is significantly less effective than when it is sold at the regular, nondiscounted price.10 Later, we will return to the ethical issues involved in experi- mental deception.

Isolate Experimental Subjects Researchers should minimize the extent to which subjects are able to talk about the experimental procedures with each other. Although it may be unintentional, discussion among subjects may lead them to guess the experimental hypotheses. For instance, it could be that different subjects received different treatments, which the subjects could discover if they talked to one another. The experimen- tal integrity will be higher when each subject only knows enough to participate in the experiment.

Use a “Blind” Experimental Administrator When possible, the people actually administering the experiment may not be told the experimen- tal hypotheses. The advantage is that if they do not know what exactly is being studied, then they are less likely to give off clues that result in demand effects. Like the subjects, when there is some reason to expect that their knowledge may constitute a demand characteristic, it is best if adminis- trators know only enough to do their job.

placebo

A false experimental condition aimed at creating the impression of an effect.

placebo effect

The effect in a dependent variable associated with the psychological impact that goes along with knowledge of some treatment being administered.

CHAPTER 12 Experimental Research • 265

Administer Only One Experimental Condition per Subject When subjects know more than one experimental treatment condition, they are much more likely to guess the experimental hypothesis. So, even though there are cost advantages to administering multiple treatment levels to the same subject, it should be avoided when possible. For example, in the self- efficacy experiment, if the subjects were asked to complete a questionnaire regarding their self-confidence in doing their job and their job satisfaction, then again asked to respond to the same questions after the personal interview, and then again after each of the three letters giving them positive feedback, they are very likely to guess that they are intentionally being given the feedback to enhance their self-efficacy.

Establishing Control The major difference between experimental research and descriptive research is an experimenter’s ability to control variables by either holding conditions constant or manipulating the experimental variable. If the color of beer causes preference, a brewery experimenting with a new clear beer must determine the possible extraneous variables other than color that may affect an experiment’s results and attempt to eliminate or control those variables. Marketing theory tells us that brand image and packaging design are important factors in beer drinkers’ reactions. Therefore, the researcher may wish to control the influence of these variables. He or she may eliminate these two extraneous variables by packaging the test beers in plain brown packages without any brand identification.

When extraneous variables cannot be eliminated, experimenters may strive for constancy of conditions. This means that subjects in all experimental groups are exposed to identical conditions except for the differing experimental treatments. Random assignment and the principle of match- ing discussed earlier help make sure that constancy is achieved.

A supermarket experiment involving shelf space shows the care that must be taken to hold all factors constant. The experiment required that all factors other than shelf space be kept constant throughout the testing period. In all stores, the shelf level that had existed before the test began was to be maintained throughout the test period. Only the amount of shelf space (the treatment) was changed. One problem involved store personnel accidentally changing shelf level when stocking the test products. This deviation from the constancy of conditions was minimized by auditing each store four times a week. In this way, any change could be detected in a minimum amount of time. The experimenter personally stocked as many of the products as possible, and the cooperation of stock clerks also helped reduce treatment deviations.

If an experimental method requires that the same subjects be exposed to two or more experi- mental treatments, an error may occur due to the order of presentation. For instance, if subjects are examining the effects of different levels of graphical interface on video game enjoyment, and they are asked to view each of four different levels, the order in which they are presented may influence enjoyment. Subjects might prefer one level simply because it follows a very poor level. Counter- balancing attempts to eliminate the confounding effects of order of presentation by requiring that one-fourth of the subjects be exposed to treatment A first, one-fourth to treatment B first, one- fourth to treatment C first, and finally one-fourth to treatment D first. Likewise, the other levels are counterbalanced so that the order of presentation is rotated among subjects. It is easy to see where counterbalancing is particularly important for experiments such as taste tests, where the order of presentation may have significant effects on consumer preference.

Problems Controlling Extraneous Variables In many experiments it is not always possible to control every potential extraneous variable. For example, competitors may bring out a product during the course of a test-market. This form of competitive interference occurred in a Boston test-market for Anheuser-Busch’s import beer, Wurzburger Hofbrau. During the test, Miller Brewing Company introduced its own brand, Munich Oktoberfest, and sent eight salespeople out to blitz the Boston market. A competitor who learns of a test-market experiment may knowingly change its prices or increase advertising to confound the test results. This brings us to ethical issues in experimentation.

constancy of conditions

Means that subjects in all experimental groups are

exposed to identical conditions except for the differing

experimental treatments.

counterbalancing

Attempts to eliminate the confounding effects of order of

presentation by requiring that one-fourth of the subjects be exposed to treatment A first,

one-fourth to treatment B first, one-fourth to treatment C

first, and finally one-fourth to treatment D first.

266 • PART THREE Research Methods for Collecting Primary Data

Ethical Issues in Experimentation Ethical issues with business research were discussed in Chapter 5. There, the question of deception was raised. Although deception is necessary in most experiments, when subjects can be returned to their prior condition through debriefing, then the experiment is probably consistent with high moral standards. If subjects might be injured significantly or truly psychologically harmed, debrief- ing will not return them to their formal condition and the experiment should not be undertaken. Therefore, some additional commentary on debriefing is warranted.

Debriefing experimental subjects by communicating the purpose of the experiment and the researcher’s hypotheses is expected to counteract negative effects of deception, relieve stress, and provide an educational experience for the subject.

Proper debriefing allows the subject to save face by uncovering the truth for himself. The experimenter should begin by asking the subject if he has any questions or if he found any part of the experiment odd, confusing, or disturbing. This question provides a check on the subject’s suspiciousness and effectiveness of manipulations. The experimenter continues to provide the subject cues to the deception until the subject states that he believes there was more to the experiment than met the eye. At this time the purpose and procedure of the experiment [are] revealed.11

Additionally, there is the issue of test-markets and efforts extended toward interfering with a competitor’s test-market. The Research Snapshot “The Hidden in Hidden Valley Ranch” describes just such a situation. When a company puts a product out for public consumption, they should be aware that competitors may also now freely consume the product. When attempts to interfere with a test-market are aimed solely at invalidating test results or aimed at infringing on some copyright protection, those acts are ethically questionable.

Practical Experimental Design Issues Basic versus Factorial Experimental Designs In basic experimental designs a single independent variable is manipulated to observe its effect on a single dependent variable. Our example of the computer communication protocols falls into this category— one independent variable (the two protocols) was examined and one dependent measure (speed of the data transfer) was assessed. However, we know that most business situations are much more complex and multiple independent and dependent variables are possible. Our self-efficacy experiment illustrated this as both the treatment and the length of time as an employee were independent variables and mul- tiple dependent variables were examined. In a complex marketing experiment, multiple dependent variables such as sales, product usage, and preference are influenced by several factors. The simultaneous change in independent variables such as price and advertising may have a greater influence on sales than if either variable is changed alone. In job satisfaction studies, we know that no one thing totally determines job satisfac- tion. Salary, opportunities for advancement, the pleasantness of the workplace, interactions with colleagues, and many more factors all combine and interact to determine how satisfied employees are with their job. Factorial experimen- tal designs are more sophisticated than basic experimental designs and allow for an investigation of the interaction of two or more independent variables.

Laboratory Experiments A business experiment can be conducted in a natural setting (a field experiment) or in an artificial setting (a laboratory experiment). In social sciences, the actual laboratory may be a behavioral lab, which is somewhat

Facilities like this one can break down the food that companies sell and tell them exactly what it should taste like. Is this a good way to test the taste of new products?

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CHAPTER 12 Experimental Research • 267

ik e

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/C or

bi s

The naturally occurring noise that exists in the field can interfere with experimental manipulations.

like a focus group facility. However, it may simply be a room or classroom dedicated to collecting data, or it can even take place in one’s home.

In a laboratory experiment the researcher has more complete control over the research setting and extraneous variables. Our example of the financial protocol experiment illustrates the benefits of a laboratory setting. The researchers were able to control for many factors, such as the size of the data file, the models of the computers, the Internet connection, and so forth. This enhanced their confidence in establishing that the differences noted in speed were due to the different pro- tocols. However, the researchers were not able to determine how the protocols compared when used in the field, on various computers, with a variety of file sizes, and under differing “real-world” circumstances.

In testing the effectiveness of a television commercial, subjects can be recruited and brought to an advertising agency’s office, a research agency’s office, or perhaps a mobile unit designed for research pur- poses. They are exposed to a television commercial within the context of a program that includes com- petitors’ ads among the commercials shown. As compensation for their time, they are then allowed to purchase either the advertised product or one of several competing products in a simulated store envi- ronment. Trial purchase measures are thus obtained. A few weeks later, subjects are contacted again to measure their satisfaction and determine repeat purchasing intention. This laboratory experiment gives the consumer an opportunity to “buy” and “invest.” In a short time span, the marketer is able to collect information on decision making. In this example, many of the outside influences can be controlled.

Other laboratory experiments may be more controlled or artificial. For example, a tachisto- scope allows a researcher to experiment with the visual impact of advertising, packaging, and so on by controlling the amount of time a subject is exposed to a visual image. Each stimulus (for example, package design) is projected from a slide to the tachistoscope at varying exposure lengths (1/10 of a second, 2/10, 3/10, and so on). The tachistoscope simulates the split-second duration of a customer’s attention to a package in a mass display.

Field Experiments Field experiments are research projects involving experimental manipulations that are implemented in a natural environment. They can be useful in fine-tuning managerial strategies and tactical deci- sions. Our self-efficacy study is an example of a field experiment. Rather than bringing subjects into an artificial setting, trying to manipulate their self-efficacy, and then measuring their perceptions of job satisfaction, the researchers took their experiment to the field and used actual employees, who were provided feedback from their supervisors. In the marketing discipline, test-markets are good examples of field experiments. Betty Crocker’s Squeezit (a 10 percent fruit juice drink in a squeeze bottle) was so successful in a test-market that production could not keep up with demand. As a

result, the product’s national introduction was postponed until production capacity could be increased.

McDonald’s conducted a field experiment testing the Triple Ripple, a three-flavor ice cream product. The product was dropped because the experiment revealed distribution problems reduced product quality and lim- ited customer acceptance. In the distribution system the product would freeze, defrost, and refreeze. Solving the problem would have required each McDonald’s city to have a local ice cream plant with special equipment to roll the three flavors into one. While a laboratory experi- ment might have shown tremendous interest, a natu- ralistic setting for the experiment helped McDonald’s executives realize the product was impractical.

Experiments vary in their degree of artificiality and control. Exhibit 12.5 shows that as experiments increase in naturalism, they begin to approach a pure field experi- ment. As they become more artificial, they approach a pure laboratory experiment.

tachistoscope

Device that controls the amount of time a subject is exposed to a visual image.

field experiments

Research projects involving experimental manipulations

that are implemented in a natural environment.

laboratory experiment

The researcher has more complete control over

the research setting and extraneous variables.

268 • PART THREE Research Methods for Collecting Primary Data

269

In field experiments, a researcher manipulates experimental variables but cannot possibly control all the extraneous variables. An example is NBC’s research on new television programs. Viewers who subscribe to a cable television service are asked to watch a cable preview on their home television sets at a certain time on a certain cable channel. While the program is being aired, telephone calls from the viewers’ friends cannot be controlled. In contrast, an advertising professor may test some advertising effect by showing subjects advertising in a classroom set- ting. Here, there are no phone calls and little to distract the subject. Which produces a better experiment?

Generally, subjects know when they are participating in a laboratory experiment. Performance of certain tasks, responses to questions, or some other form of active involvement is characteristic of laboratory experiments. In field experiments, such as test-markets or the self-efficacy experi- ment, subjects do not even know they are taking part in an experiment. Ethically, consent should be sought before having someone participate in an experiment. However, with field experiments the consent is implied since subjects are not asked to do anything departing from their normal behavior to participate in the experiment. All precautions with respect to safety and confidentiality should be maintained.

Field experiments involving new products or promotions are often conducted in a retail store. These are known as controlled store tests. The products are put into stores in a number of small cities or into selected supermarket chains. Product deliveries are made not through the traditional warehouse but by the research agency, so product information remains confidential. The Research Snapshot “The Hidden in Hidden Valley Ranch” describes such a test. While they can be less

A few years ago, Hidden Valley Ranch (HVR) conducted a field market experiment to examine how effective three new flavors of salad dressings would be in the marketplace. Thus, there were three levels of the experimental variable, each represent- ing a different flavor. Tests like this can be costly. HVR had to produce small batches of each flavor, get them bottled, and ship them to their sales representatives, who then had to stock the dressings in the participating retail stores. All of this is very expensive.

The first day of the test was consumed with sales reps plac- ing the products in the salad dressing sections of retail stores. The second day, each rep went back to each store to record the number of sales for each flavor. By the third day, all of the bottles of all flavors had sold! Amazing! Was every flavor a huge suc- cess? Actually, one of HVR’s competitors had noticed the test and

sent its sales reps around beginning on the second day to buy every bottle of the new HVR dressings in every store it had been placed in. Thus, HVR was unable to produce any valid sales data (the dependent variable) and the competitor was able to break down the dressing in its labs and determine the recipe.

This illustrates one risk that comes along with field tests. Once a product is available for sale, there are no secrets. Also, you risk espionage of this type that can render the experiment invalid.

The Hidden in Hidden Valley Ranch

EXHIBIT 12.5

The Artificiality of Laboratory versus Field Experiments

Artificial environmental setting

Natural environmental setting

Field experiments

Laboratory experiments

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expensive than a full-blown market test, they also have drawbacks because of the relatively small sample of stores and the limitations on the type of outlet where the product is tested. Thus, their results may not generalize to all consumers in a population.

Within-Subjects and Between-Subjects Designs A basic question faced by the researchers involves how many treatments a subject should receive. For economical reasons, the researcher may wish to apply multiple treatments to the same sub- ject. Thus, multiple observations on the dependent variable can be obtained from a single subject. Such a design is called a within-subjects design. Within-subjects designs involve repeated measures because with each treatment the same subject is measured.

In contrast, the researcher could decide that each person will receive only one treatment com- bination. This is referred to as a between-subjects design. Each dependent variable is measured only once for every subject. Exhibit 12.6 illustrates this point.

Between-subjects designs are usually advantageous even though they are typically more costly. The validity of between-subjects designs is usually higher since applying only one treatment com- bination to each subject greatly reduces the possibility of demand characteristics. In addition, as we will see later, statistical analysis of between-subjects designs are simpler than within-subjects designs. This also means the results are easier to report and explain to management.

EXHIBIT 12.6

Within- and Between- Subjects Designs

within-subjects design

Involves repeated measures because with each treatment

the same subject is measured.

between-subjects design

Each subject receives only one treatment combination.

Within-Subjects: Same subject is measured after being exposed

to each treatment.

Between-Subjects: Each subject is measured only once after being

exposed to one treatment.

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Issues of Experimental Validity An experiment’s quality is judged by two types of validity. These are known as internal and exter- nal validity.

Internal Validity Internal validity exists to the extent that an experimental variable is truly responsible for any vari- ance in the dependent variable. In other words, does the experimental manipulation truly cause changes in the specific outcome of interest? If the observed results were influenced or confounded by extraneous factors, the researcher will have problems making valid conclusions about the rela- tionship between the experimental treatment and the dependent variable.

Thus, a lab experiment enhances internal validity because it maximizes control of outside forces. If we wish to know whether certain music causes increased productivity among workers, we may set up a task in a room with different music piped in (our experimental manipulation), but with the temperature, lighting, density, other sounds, and any other factors all controlled, which would be difficult or impossible to control outside of a lab environment. If the only thing that varies from subject to subject is the music, then we can safely say that any differences in perfor- mance must be attributable to human reactions to the music. Our opening example of the protocol experiment focused on maximizing internal validity. By testing the protocols in a lab setting, the researchers were able to control extraneous variables such as differences in computing hardware, network issues, and so forth.

Manipulation Checks Internal validity depends in large part on successful manipulations. Manipulations should be carried out in a way that the independent variable differs over meaningful levels. If the levels are too close together, the experiment may lack the power necessary to observe differences in the dependent vari- able. In a pricing experiment, it may be that manipulating the price of an automobile over two levels, $24,600 and $24,800, would not be successful in creating truly different price categories. Respon- dents might not perceive the differences or experience any reaction to such a slight deviation.

The validity of manipulations can often be determined with a manipulation check. If a drug is administered in different dosages that should affect blood sugar levels, the researcher could actually measure blood sugar level after administering the drug to make sure that the dosages were different enough to produce a change in blood sugar. In business research, the manipulation check is often conducted by asking a survey question or two. In the pricing example above, subjects may be asked a question about how low they believe the price of the car to be. A valid manipulation would pro- duce substantially different average responses to that question in a “high” and “low” price group. In our self-efficacy example, the researchers were interested in the impact increased self-efficacy (the independent variables) had on job satisfaction (the dependent variable). The experimental manipu- lation was the positive feedback the subjects were given. The manipulation check was a series of questions assessing the subject’s self-efficacy. Did the positive feedback actually increase self- efficacy? If it did, then the researchers could examine the other relationships of interest. However, if self-efficacy did not increase, then the researchers would have to reconsider their manipulation and find another way to enhance self-efficacy to carry out the study. Manipulation checks should always be administered after dependent variables in self-response format experiments. This keeps the manipulation check item from becoming a troublesome demand characteristic.

Extraneous variables can jeopardize internal validity. The six major ones are history, maturation, testing, instrumentation, selection, and mortality.

History A history effect occurs when some change other than the experimental treatment occurs dur- ing the course of an experiment that affects the dependent variable. A common history effect occurs when competitors change their marketing strategies during a test marketing experiment.

manipulation check

A validity test of an experimental manipulation to make sure that the manipulation does produce differences in the independent variable.

history effect

Occurs when some change other than the experimental treatment occurs during the course of an experiment that affects the dependent variable.

internal validity

Exists to the extent that an experimental variable is truly responsible for any variance in the dependent variable.

CHAPTER 12 Experimental Research • 271

Another example would be if some of our subjects in the self-efficacy exam are offered a position by another firm. A different job offer may affect several of the dependent measures in the study. History effects are particularly prevalent in repeated measures experiments that take place over an extended time. If we wanted to assess how much a change in recipe improves individual subjects’ consumption of a food product, we would first measure their consumption and then compare it with consumption after the change. Since several weeks may pass between the first and second measurement, there are many things that could occur that would also influence subjects’ diets.

Although it would be extreme, examining the effect of a dietary supplement on various health- related outcomes may require that a subject be confined during the experiment’s course. This may take several weeks. Without confining the subject in something like a hospital setting, there would be little way of controlling food and drink consumption, exercise activities, and other factors that may also affect the dependent variables.

A special case of the history effect is the cohort effect, which refers to a change in the depen- dent variable that occurs because members of one experimental group experienced different his- torical situations than members of other experimental groups. For example, groups of managers used as subjects may be in different cohorts because one group encountered different experiences over the course of an experiment. Let’s assume the experimental manipulation involves different levels of financial incentives and performance is the dependent variable. The experiment is being conducted in waves; as the managers come to the home office for training they are told about the financial incentives that are being implemented. During this period, however, a financial crisis occurs. Since the first group participated prior to this development, they would not be affected by it. However, subsequent groups might have different attitudes and increased performance due to the environmental change. The possibility exists that the financial crisis rather than the change in incentive is truly causing differences in performance.

Maturation Maturation effects are effects that are a function of time and the naturally occurring events that coincide with growth and experience. Experiments taking place over longer time spans may see lower internal validity as subjects simply grow older or more experienced. For example, our self- efficacy study shows that job satisfaction seems to decline with time (note in Exhibit 12.2 that the control group subjects that are new employees report a mean of 5.80 while the current employ- ees’ mean is 4.77). Conversely, job skill tends to increase over time. Suppose an experiment were designed to test the impact of a new compensation program on sales productivity. If this program were tested over a year’s time, some of the salespeople probably would mature as a result of more selling experience and gain increased knowledge and skill. Their sales productivity might improve because of their knowledge and experience rather than the compensation program.

Testing Testing effects are also called pretesting effects because the initial measurement or test alerts or primes subjects in a way that affects their response to the experimental treatments. Testing effects only occur in a before-and-after study. A before-and-after study is one requiring that an initial baseline measure be taken before an experimental treatment is administered. So, before-and-after experiments are a special case of a repeated measures design. For example, students taking standard- ized achievement and intelligence tests for the second time usually do better than those taking the tests for the first time. The effect of testing may increase awareness of socially appropriate answers, increase attention to experimental conditions (that is, the subject may watch more closely), or make the subject more conscious than usual of the dimensions of a problem.

Instrumentation A change in the wording of questions, a change in interviewers, or a change in other procedures used to measure the dependent variable causes an instrumentation effect, which may jeopardize internal validity. Sometimes instrumentation effects are difficult to control. For example, if the same interviewers are used to ask questions both before and after measurement, some problems may arise. With practice, interviewers may acquire increased skill in interviewing, or they may become

cohort effect

Refers to a change in the dependent variable that

occurs because members of one experimental group

experienced different historical situations than members of other experimental groups.

maturation effects

Effects that are a function of time and the naturally

occurring events that coincide with growth and experience.

testing effects

A nuisance effect occurring when the initial measurement

or test alerts or primes subjects in a way that affects their

response to the experimental treatments.

instrumentation effect

A nuisance that occurs when a change in the wording of questions, a change in interviewers, or a change

in other procedures causes a change in the dependent

variable.

272 • PART THREE Research Methods for Collecting Primary Data

bored and decide to reword the questionnaire in their own terms. To avoid this problem, new interviewers could be hired. But this introduces another set of issues as different individuals are also a source of extraneous variation. There are numerous other sources of instrument decay or varia- tion. Again, instrumentation effects are problematic with any type of repeated measures design.

Selection The selection effect is a sample bias that results from differential selection of respondents for the comparison groups, or sample selection error, discussed earlier. Researchers must make sure the characteristics of the research subjects accurately reflect the population of relevance. Furthermore, the key characteristics of the subjects must be distributed in such a way to create equal groups; that is, the subjects in the experimental and control groups, or in different experimental cells, must be equal across all variables of interest and those that could affect the dependent measure.

Mortality If an experiment is conducted over a period of a few weeks or more, some sample bias may occur due to the mortality effect (sample attrition). Sample attrition occurs when some subjects withdraw from the experiment before it is completed. Mortality effects may occur if subjects drop from one experimental treatment group disproportionately than from other groups. Con- sider a sales training experiment investigating the effects of close supervision of salespeople (high pressure) versus low supervision (low pressure). The high-pressure condition may misleadingly appear superior if those subjects who completed the experiment did very well. If, however, the high-pressure condition caused more subjects to drop out than the other conditions, this apparent superiority may be due to the fact that only very determined and/or talented salespeople stuck with the program. Similarly, in the self-efficacy study, accountants that did not feel commitment to the organization and maintain a high level of job satisfaction may have left the organization before the final measures.

External Validity External validity is the accuracy with which experimental results can be generalized beyond the experimental subjects. External validity is increased when the subjects comprising the sample truly represent the population of interest and when the results extend to other market segments or groups of people. The higher the external validity, the more researchers and managers can count on the fact that any results observed in an experiment will also be seen in the “real world” (financial market, workplace, sales floor, and so on).

For instance, to what extent would results from our protocol experiment, which represents a simulated financial market data exchange, transfer to a real-world trading situation? Would the FIX protocol prove to be faster across different computer systems, Internet transfer speeds, and traders? Would increases in self-efficacy enhance the job satisfaction of retail store workers, salespeople, or human resource managers as it did for accountants? Can one extrapolate the results from a tachisto- scope to an in-store shopping situation? Lab experiments, such as the protocol experiment, are asso- ciated with low external validity because the limited set of experimental conditions, holding all else constant, do not adequately represent all the influences existing in the real world. In other words, the experimental situation may be too artificial. When a study lacks external validity, the researcher will have difficulty repeating the experiment with any change in subjects, settings, or time.

Student Subjects Basic researchers often use college students as experimental subjects.12 Convenience, time, money, and a host of other practical considerations often result in students being used as research subjects. This practice is widespread in academic studies. Some evidence shows that students are quite similar to household consumers, but other evidence indicates that they do not provide sufficient external validity to represent most consumer or employee groups. This is particularly true when students are used as substitutes or surrogates for businesspeople.

mortality effect (sample attrition)

Occurs when some subjects withdraw from the experiment before it is completed.

external validity

The accuracy with which experimental results can be generalized beyond the experimental subjects.

selection effect

Sample bias from differential selection of respondents for experimental groups.

CHAPTER 12 Experimental Research • 273

The issue of external validity should be seriously considered because the student population is likely to be atypical. Students are easily accessible, but they often are not representative of the total population. This is not always the case, however, and when behaviors are studied for which students have some particular expertise (the purchase of relevant products such as MP3 players or job search skills), then they are certainly appropriate.

Trade-offs between Internal and External Validity Naturalistic field experiments tend to have greater external validity than artificial laboratory experiments. Researchers often must trade internal validity for external validity. A researcher who wishes to test advertising effectiveness by manipulating treatments via a split-cable experiment has the assurance that the advertisement will be viewed in an externally valid situation, the subjects’ homes. However, the researcher has no assurance that some interruption (for example, the tele- phone ringing, a child calling, or a pot boiling over on the stove) will not have some influence that will reduce the internal validity of the experiment. Laboratory experiments with many controlled factors usually are high in internal validity, while field experiments generally have less internal validity but greater external validity. Typically, it is best to establish internal validity first, and then focus on external validity. Thus, results from lab experiments would be followed up with some type of field test.

Classification of Experimental Designs An experimental design may be compared to an architect’s plans for a building. The basic require- ments for the structure are given to the architect by the prospective owner. Several different plans may be drawn up as options for meeting the basic requirements. Some may be more costly than others. One may offer potential advantages that another does not.

There are various types of experimental designs. If only one variable is manipulated, the exper- iment has a basic experimental design. If the experimenter wishes to investigate several levels of the independent variable (for example, four different employee salary levels) or to investigate the interaction effects of two or more independent variables (salary level and retirement package), the experiment requires a complex, or statistical, experimental design.

Symbolism for Diagramming Experimental Designs The work of Campbell and Stanley has helped many students master the subject of basic exper- imental designs.13 The following symbols will be used in describing the various experimental designs:

X = exposure of a group to an experimental treatment

O = observation or measurement of the dependent variable; if more than one observation or measurement is taken, subscripts (that is, O

1 , O

2 , etc.) indicate temporal order

R = random assignment of test units; R symbolizes that individuals selected as subjects for the experiment are randomly assigned to the experimental groups

The diagrams of experimental designs that follow assume a time flow from left to right. Our first example will make this clearer.

Three Examples of Quasiexperimental Designs Quasiexperimental designs do not involve random allocation of subjects to treatment combina- tions. In this sense, they do not qualify as true experimental designs because they do not adequately control for the problems associated with loss of internal validity. However, sometimes quasiexperi- mental designs are the only way to implement a study.

basic experimental design

An experimental design in which only one variable is

manipulated.

quasiexperimental designs

Experimental designs that do not involve random allocation

of subjects to treatment combinations.

274 • PART THREE Research Methods for Collecting Primary Data

One-Shot Design The one-shot design, or after-only design, is diagrammed as follows:

X O 1

Suppose that during a very cold winter an automobile dealer finds herself with a large inven- tory of cars. She decides to experiment for the month of January with a promotional scheme. She offers a free trip to New Orleans with every car sold. She experiments with the promotion (X = experimental treatment) and measures sales (O

1 = measurement of sales after the treatment

is administered). This one-shot design is a case study of a research project fraught with problems. Subjects or

test units participate because of voluntary self-selection or arbitrary assignment, not because of random assignment. The study lacks any kind of comparison or any means of controlling extrane- ous influences. There should be measures of what will happen when the test units have not been exposed to X to compare with the measures of when subjects have been exposed to X. The one- shot experimental design commonly suffers from most of the threats to internal validity discussed above. Nevertheless, under certain circumstances, it is the only viable choice.

One-Group Pretest–Posttest Design Suppose a real estate franchiser wishes to provide a training program for franchisees. If the fran- chiser measures subjects’ knowledge of real estate selling before (O

1 ) they are exposed to the

experimental treatment (X ) and then measures real estate selling knowledge after (O 2 ) they are

exposed to the treatment, the design will be as follows:

O 1 X O

In this example the trainer is likely to conclude that the difference between O 2 and

O 1 (O

2 − O

1 ) is the measure of the influence of the experimental treatment. This one-group

pretest–posttest design offers a comparison of the same individuals before and after training. Although this is an improvement over the one-shot design, this research still has several weak- nesses that may jeopardize internal validity. For example, if the time lapse between O

1 and O

2 was

a period of several months, the trainees may have matured as a result of experience on the job (maturation effect). History effects—such as a change in interest rates—may also influence the dependent measure in this design. Perhaps some subjects dropped out of the training program (mortality effect). The effect of testing may also have confounded the experiment.

Although this design has a number of weaknesses, it is commonly used in business research. Remember, the cost of the research is a consideration in most business situations. While there will be some problems of internal validity, the researcher must always take into account questions of time and cost.

Static Group Design In a static group design, each subject is identified as a member of either an experimental group or a control group (for example, exposed or not exposed to a training program). The experimental group is measured after being exposed to an experimental treatment and the control group is mea- sured without having been exposed to this experimental treatment:

Experimental group: X O 1

Control group: O 2

The results of the static group design are computed by subtracting the observed results in the control group from those in the experimental group (O

1 – O

2 ). A major weakness of this

design is its lack of assurance that the groups were equal on variables of interest before the experi- mental group received the treatment. If entry into either group was voluntary, systematic differ- ences between the groups could invalidate the conclusions about the effect of the treatment. For example, if the real estate franchisor mentioned above asked her franchisees who would like to attend the training program, we have no way of knowing if those who chose to attend are the same as those who did not. Random assignment of subjects may eliminate problems with group

CHAPTER 12 Experimental Research • 275

differences. If groups are established by the experimenter rather than existing as a function of some other causation, the static group design is referred to as an after-only design with control group.

On many occasions, an after-only design is the only possible option. This is particularly true when conducting use tests for new products or brands. Cautious interpretation and recognition of the design’s shortcomings may enhance the value of this design.

Three Alternative Experimental Designs In a formal scientific sense, the three designs just discussed are not pure experimental designs. Subjects for the experiments were not selected from a common pool of subjects and randomly assigned to one group or another. In the following discussion of three basic experimental designs, the symbol to the left of the diagram indicates that the first step in a true experimental design is the random assignment of subjects.

Pretest-Posttest Control Group Design (Before-After with Control) A pretest–posttest control group design, or before–after with control group design, is the classic experimental design:

Experimental group: R O 1 X O

Control group: R O 3 O

As the diagram indicates, the subjects in the experimental group are tested before and after being exposed to the treatment. The control group is tested at the same two times as the experi- mental group, but subjects are not exposed to the experimental treatment. This design has the advantages of the before–after design with the additional advantages gained by its having a control group. The effect of the experimental treatment equals:

(O 2 − O

1 ) − (O

4 − O

3 )

It is important to note that we expect O 1 = O

3 . One of the threats we discussed to internal

validity was selection and the assumption of equal groups. If the two groups are not equal at the beginning of the experiment, the study has a fatal flaw and the researchers should start over. Let’s assume there is brand awareness among 20 percent of the subjects (O

1 = 20 percent, O

3 = 20

percent) before an advertising treatment and then 35 percent awareness in the experimental group (O

2 = 35 percent) and 22 percent awareness in the control group (O

4 = 22 percent) after exposure

to the treatment. The treatment effect would then equal 13 percent:

(0.35 − 0.20) − (0.22 − 0.20) = (0.15) − (0.02) = 0.13 or 13%

Not only are the groups assumed to be equal at the beginning, but the effect of all extraneous variables is assumed to be the same on both the experimental and the control groups. For instance, since both groups receive the pretest, no difference between them is expected for the pretest effect. This assumption is also made for effects of other events between the before and after measure- ments (history), changes within the subjects that occur with the passage of time (maturation), testing effects, and instrumentation effects. In reality there may be some differences in the sources of extraneous variation. Nevertheless, in most cases assuming that the effect is approximately equal for both groups is reasonable.

However, a testing effect is possible when subjects are sensitized to the subject of the research. This is analogous to what occurs when people learn a new vocabulary word. Soon they discover that they notice it much more frequently in their reading. In an experiment the combination of being interviewed on a subject and receiving the experimental treatment might be a potential source of error. For example, a subject exposed to a certain advertising message in a split-cable experiment might say, “Ah, there is an ad about the product I was interviewed about yesterday!” The subject may pay more attention than normal to the advertisement and be more prone to change his or her attitude than in a situation with no interactive testing effects. This weakness in the before–after with control group design can be corrected (see the next two designs).

276 • PART THREE Research Methods for Collecting Primary Data

Testing the effectiveness of television commercials in movie theaters provides an example of the before–after with control group design. Subjects are selected for the experiments by being told that they are going to preview several new television shows. When they enter the theater, they learn that a drawing for several types of products will be held, and they are asked to complete a product preference questionnaire (see Exhibit 12.7). Then a first drawing is held. Next, the televi- sion pilots and commercials are shown. Then the emcee announces additional prizes and a second drawing. Finally, subjects fill out the same questionnaire about prizes. The information from the first questionnaire is the before measurement, and that from the second questionnaire is the after measurement. The control group receives similar treatment except that on the day they view the pilot television shows, different (or no) television commercials are substituted for the experimental commercials.

EXHIBIT 12.7

Product Preference Measure in an Experiment

We are going to give away a series of prizes. If you are selected as one of the winners, which brand from each of the groups listed below would you truly want to win?

Special arrangements will be made for any product for which bulk, or one-time, delivery is not appropriate.

Indicate your answers by filling in the box like this: ■

Do not “X,” check, or circle the boxes please.

Cookies Allergy Relief Products

(A 3-month supply, pick ONE.) (A year’s supply, pick ONE.)

NABISCO OREO ⃞ (1) ALLEREST ⃞ (1) NABISCO OREO DOUBLE STUFF ⃞ (2) BENADRYL ⃞ (2) NABISCO NUTTER BUTTER ⃞ (3) CONTAC ⃞ (3) NABISCO VANILLA CREMES ⃞ (4) TAVIST–D ⃞ (4) HYDROX CHOCOLATE ⃞ (5) DRISTAN ⃞ (5) HYDROX DOUBLES ⃞ (6) SUDAFED ⃞ (6) NABISCO COOKIE BREAK ⃞ (7) CHLOR–TRIMETON ⃞ (7) NABISCO CHIPS AHOY ⃞ (8) KEEBLER E.L. FUDGE ⃞ (9) KEEBLER FUDGE CREMES ⃞ (10) KEEBLER FRENCH VANILLA CREMES ⃞ (11)

Posttest-Only Control Group Design (After-Only with Control) In some situations pretest measurements are impossible. In other situations selection error is not anticipated to be a problem because the groups are known to be equal. The posttest-only control group design, or after-only with control group design, is diagrammed as follows:

Experimental group: R X O 1

Control group: R O 2

The effect of the experimental treatment is equal to O 1 – O

2 .

Suppose the manufacturer of an athlete’s-foot remedy wishes to demonstrate by experimenta- tion that its product is better than a competing brand. No pretest measure about the effectiveness of the remedy is possible. The design is to randomly select subjects, perhaps students, who have contracted athlete’s foot and randomly assign them to the experimental or the control group. With only the posttest measurement, the effects of testing and instrument variation are eliminated. Fur- thermore, researchers make the same assumptions about extraneous variables described above— that is, that they operate equally on both groups, as in the before–after with control group design.

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CHAPTER 12 Experimental Research • 277

Compromise Designs True experimentation is often simply not possible. The researcher may compromise by approxi- mating an experimental design. A compromise design is one that falls short of assigning subjects or treatments randomly to experimental groups.

Consider a situation in which a researcher would ideally implement a pretest–posttest control group design to study the effect of training on employee performance. In this case, subjects may not be able to be assigned randomly to the experimental and control group because the researcher can- not take workers away from their work groups. Thus, one entire work group is used as the experi- mental group and a separate work group is used as a control group. The researcher has no assurance that the groups are equivalent. The situation has forced a compromise to experimental integrity.

The alternative to the compromise design when random assignment of subjects is not pos- sible is to conduct the experiment without a control group. Generally this is considered a greater weakness than using groups that have already been established. When the experiment involves a longitudinal study, circumstances usually dictate a compromise with true experimentation.

Time Series Designs Many experiments may be conducted in a short period of time (a few hours, a week, or a month). However, a business experiment investigating long-term strategic and/or structural changes may require a time series design. Time series designs are quasiexperimental because they generally do not allow the researcher full control over the treatment exposure or influence of extraneous variables. When experiments are conducted over long periods of time, they are most vulnerable to history effects due to changes in population, attitudes, economic patterns, and the like. Although seasonal patterns and other exogenous influences may be noted, the experimenter can do little about them when time is a major factor in the design.

Political tracking polls provide an example. A pollster normally uses a series of surveys to track candidates’ popularity. Consider the candidate who plans a major speech (the experimental treat- ment) to refocus the political campaign. The simple time series design can be diagrammed as follows:

O 1 O

2 O

3 X O

4 O

5 O

Several observations have been taken to identify trends before the speech (X ) is given. After the treatment has been administered, several observations are made to determine if the patterns after the treatment are similar to those before. If the longitudinal pattern shifts after the political speech, the researcher may conclude that the treatment had a positive impact on the pattern. Of course, this time series design cannot give the researcher complete assurance that the treatment caused the change in the trend, rather than some external event. Problems of internal validity are greater than in more tightly controlled before-and-after designs for experiments of shorter duration.

One unique advantage of the time series design is its ability to distinguish temporary from permanent changes. Exhibit 12.8 shows some possible outcomes in a time series experiment.

Complex Experimental Designs The previous discussion focused on simple experimental designs—experiments manipulating a single variable. Here, the focus shifts to more complex experimental designs involving multiple experimental variables. Complex experimental designs are statistical designs that isolate the effects of confounding extraneous variables or allow for manipulation of more than one independent variable in the experiment. Completely randomized designs, randomized block designs, and facto- rial designs are covered in the following section.

Completely Randomized Design A completely randomized design is an experimental design that uses a random process to assign subjects to treatment levels of an experimental variable. Randomization of experimental units is the researcher’s attempt to control extraneous variables while manipulating potential causes.

time series design

Used for an experiment investigating long-term

structural changes.

completely randomized design

An experimental design that uses a random process to

assign subjects to treatment levels of an experimental

variable.

278 • PART THREE Research Methods for Collecting Primary Data

A one-variable experimental design can be completely randomized, so long as subjects are assigned in a random way to a particular experimental treatment level.

Consider a financial institution that wants to increase their response to credit card offers. An experiment is constructed to examine the effects of various incentives on the percentage of poten- tial customers that apply for a credit card with the institution. Thus, the experimental variable is the incentive. This can be manipulated over three treatment levels:

1. No incentive to the control group 2. No interest for the first 90 days with an approved application 3. A free MP3 player with an approved application

The financial institution rents a mailing list of 15,000 prospects. This sample frame is divided into three groups of 5,000 each (n

1 + n

2 + n

3 = 15,000). A random number process could be used

to assign subjects to one of the three groups. Suppose each of the 15,000 subjects is assigned a number ranging from 1 to 15,000. If a random number is selected between 1 and 15,000 (i.e., 1,201), that person can be assigned to the first group, with every third person afterward and before also assigned to the first group (1,204, 1,207, 1,210 . . . all the way back to 1,198). The process can be repeated with the remaining 10,000 subjects by selecting a random number between 1 and 10,000 and then selecting every other subject. At this point, only 5,000 subjects remain and will comprise the third group. All 15,000 subjects are now assigned to one of three groups. Each group corresponds to one of the three levels of incentive. A variable representing which group a subject belongs to becomes the independent variable. The dependent variable is measured for each of the three treatment groups and the number of respondents to the offer is determined. The analysis would compare differences across the number of respondents for each of the three treatment levels.

EXHIBIT 12.8

Selected Time Series Outcomes

1 2 3 4 5 6 Period (Time)

Permanent Change

De gr

ee o

f C ha

ng e

1 2 3 4 5 6 Period (Time)

Temporary Change

De gr

ee o

f C ha

ng e

1 2 3 4 5 6 Period (Time) No Change

De gr

ee o

f C ha

ng e

1 2 3 4 5 6 Period (Time)

Continuation of Trend

De gr

ee o

f C ha

ng e

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CHAPTER 12 Experimental Research • 279

Randomized-Block Design The randomized-block design is an extension of the completely randomized design. A form of randomization is used to control for most extraneous variation; however, the researcher has identi- fied a single extraneous variable that might affect subjects’ responses systematically. The researcher will attempt to isolate the effects of this single variable by blocking out its effects.

A blocking variable is a categorical variable that is expected to be associated with different values of a dependent variable for each group. Sex is a common blocking variable. Many poten- tial dependent variables are expected to be different for men and women. For instance, work- family conflict—conflict between the obligations a person has to their family and with their work commitments—has been found to differ between women and men. So, if a researcher is studying how salary and length of vacation time affects employee job satisfaction, they may want to also record a person’s sex and include it as an extra explanatory variable over and above the experi- mental variable’s salary and vacation time. The concept of a blocking variable was introduced in the self- efficacy study where the researchers “blocked” on length of time the subjects had been an employee (new versus current).

The term randomized block originated in agricultural research that applied several levels of a treatment variable to each of several blocks of land. Systematic differences in agricultural yields due to the quality of the blocks of land may be controlled in the randomized-block design. In business research, the researcher may wish to isolate block effects such as bank branch territories, job work units, employee tenure, and so on. Suppose that a manufacturer of Mexican food is considering two packaging alternatives. Marketers suspect that geographic region might confound the experiment. They have identified three regions where attitudes toward Mexican food may differ (the South- west, the Midwest, and the Atlantic Coast). In a randomized-block design, each block must receive every treatment level. Assigning treatments to each block is a random process. In this example the two treatments will be randomly assigned to different cities within each region.

Sales results such as those in Exhibit 12.9 might be observed. The logic behind the randomized-block design is similar to that underlying the selection of a stratified sample rather than a simple random one. By isolating the block effects, one type of extraneous variation is parti- tioned out and a more efficient experimental design therefore results. This is because experimental error is reduced with a given sample size.

randomized-block design

A design that attempts to isolate the effects of a

single extraneous variable by blocking out its effects on the

dependent variable.

factorial design

A design that allows for the testing of the effects of two or more treatments (experimental

variables) at various levels.

EXHIBIT 12.9

Randomized Block Design

Percentage Who Purchase Product

Treatment Southwest Midwest Atlantic Coast Mean for Treatments

Package A 14.0% (Phoenix) 12.0% (St. Louis) 7.0% (Boston) 11.0%

Package B 16.0% (Albuquerque) 15.0% (Peoria) 10.0% (New York) 13.6%

Mean for cities 15.0% 13.5% 8.5%

Factorial Designs Suppose a human resource manager believes that an experiment that manipulates the level of salary offered is useful, but too limited. The recruiters for the firm have been visiting college campuses and know that graduates seeking jobs are concerned about salary, but they are also concerned about the number of vacation days they will receive. However, the level of salary and actual num- ber of vacation days needs to determined. Thus, an experiment to assess this requires more than one independent variable be incorporated into the research design. Even though the single-factor experiments considered so far may have one specific variable blocked and other confounding sources controlled, they are still limited. A factorial design allows for the testing of the effects of two or more treatments (factors) at various levels.

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280 • PART THREE Research Methods for Collecting Primary Data

We discussed earlier in this chapter that experiments produce main effects and interactions. Main effects are differences (in the dependent variable) between treatment levels. Interactions pro- duce differences (in the dependent variable) between experimental cells based on combinations of variables. In the self-efficacy example, we learned that the experimental treatment had a stronger effect on the current employees than the new employees (see Exhibit 12.3).

To further explain the terminology of experimental designs, let us develop the recruiting experiment more fully. The human resource manager wants to measure the effect of the salary and vacation days on the percentage of job offers accepted. Exhibit 12.10 indicates three treatment levels of salary offered ($37,500, $40,000, and $42,500) and two levels of vacation time (10 days and 14 days). The table shows that every combination of treatment level requires a separate experimental group. In this experiment, with three levels of salary and two levels of vacation, we have a 3 × 2 (read “three by two”) factorial design because the first factor (the salary variable) is varied in three ways and the second factor (the location variable) is varied in two ways. A 3 × 2 design requires six cells, or six experimental groups (3 × 2 = 6). If the subjects each receive only one combination of experi- mental variables, then we use the term 3 × 2 between-subjects design to describe the experiment.

EXHIBIT 12.10

Factorial Design—Salary and Vacation

Vacation Days

Salary 10 Days 14 Days

$37,500 Cell 1 Cell 4

$40,000 Cell 2 Cell 5

$42,500 Cell 3 Cell 6

The number of treatments (factors) and the number of levels of each treatment identify the factorial design. A 3 × 3 design means there are two factors, each having three levels; a 2 × 2 × 2 design has three factors, each having two levels. The treatments need not have the same number of levels; for example, a 3 × 2 × 4 factorial design is possible. The important idea is that in a factorial experiment, each treatment level is combined with every other treatment level.

In addition to the advantage of investigating two or more independent variables simultane- ously, factorial designs allow researchers to measure interaction effects. In a 2 × 2 experiment the interaction is the effect produced by treatments A and B combined. If the effect of one treatment differs at various levels of the other treatment, interaction occurs.

To illustrate the value of a factorial design, suppose a researcher is comparing two magazine ads. The researcher is investigating the believability of ads on a scale from 0 to 100 and wishes to consider the sex of the reader as a blocking factor. The experiment has two independent variables: sex and ads. This 2 × 2 factorial experiment permits the experimenter to test three hypotheses. Two hypotheses examine the main effects:

■ Advertisement A is more believable than ad B. ■ Men believe advertisements more than women.

However, the primary research question may deal with the interaction hypothesis:

■ Advertisement A is more believable than ad B among women, but ad B is more believable than ad A among men.

A high score indicates a more believable ad. Exhibit 12.11 shows that the mean believability score for both sexes is 65. This suggests that there is no main sex effect. Men and women evaluate believ- ability of the advertisements equally. The main effect for ads indicates that ad A is more believable than ad B (70 versus 60), supporting the first hypothesis. However, if we inspect the data and look within the levels of the factors, we find that men find ad B more believable and women find ad A more believable. This is an interaction effect because the believability score of the advertising fac- tor differs at different values of the other independent variable, sex. Thus, the interaction hypothesis is supported.

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CHAPTER 12 Experimental Research • 281

Exhibit 12.12 graphs the results of the believability experiment. The line for men represents the two mean believability scores for ads A and B. The other line represents the same relationship for women. Notice the difference between the slopes of the two lines. This also illustrates support for the interaction of the ad copy with biological sex. The difference in the slopes means that the believability of the advertising copy depends on whether a man or a woman is reading the adver- tisement. We witnessed a similar effect in our self-efficacy example.

EXHIBIT 12.12

Graphic Illustration of Interaction between Sex and Advertising Copy

EXHIBIT 12.11

A 2 × 2 Factorial Design That Illustrates the Effects of Sex and Ad Content on Believability

Ad A Ad B

Men 60 70 65 Main effects of sex

Women 80 50 65

70 60

Main effects of ad

100

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SUMMARY

1. Identify the independent variable, dependent variable, and construct a valid simple experiment to assess a cause and effect relationship. Independent variables are created through manipulation in experiments rather than through measurement. The researcher creates unique experimental conditions that represent unique levels of an independent variable. In our protocol example, the researcher manipulated the type of protocol used for the transmission of financial data. In the self-efficacy study, the subjects were either given or not given the treatment. Levels of an independent variable should be different enough to represent meaningful categories. The dependent variable(s) must be outcome measures that are anticipated to change based on the differing levels of the independent variable. The researchers expected the speed of data transmis- sion to differ based on the protocol used. In the self-efficacy study, job satisfaction (and other key dependent variables) was expected to change if self-efficacy was enhanced.

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282 • PART THREE Research Methods for Collecting Primary Data

2. Understand and minimize systematic experimental error. Systematic experimental error occurs because sampling units (research subjects) in one experimental cell are different from those in another cell in a way that affects the dependent variable. In the self-efficacy study, it is important to have employees randomly assigned to the experiment or control group, rather than choosing those employees that appear to most need their self confidence enhanced to be exposed to the treatment. Randomization is an important way of minimizing systematic experimental error. If research sub- jects are randomly assigned to different treatment combinations, then the differences among people that exist naturally within a population should also exist within each experimental cell.

3. Know ways of minimizing experimental demand characteristics. Demand characteristics are experimental procedures that somehow inform the subject about the actual research pur- pose. Demand effects can result from demand characteristics. When this happens, the results are confounded. Demand characteristics can be minimized by following these simple rules: using an experimental disguise, isolating experimental subjects, using a “blind” experimental administrator, and administering only one experimental treatment combination to each subject.

4. Avoid unethical experimental practices. Experiments naturally involve deception. Addition- ally, research subjects are sometimes exposed to stressful or possibly dangerous manipulations. Every precaution should be made to ensure that subjects are not harmed. Debriefing subjects about the true purpose of the experiment following its conclusion is important for the ethical treatment of subjects. If debriefing can restore subjects to their pre-experimental condition, the experimental procedures are likely consistent with ethical practice. If subjects are affected in some way that makes it difficult to return them to their prior condition, then the experimental proce- dures probably go beyond what is considered ethical.

5. Understand the advantages of a between-subjects experimental design. A between- subjects design means that every subject receives only one experimental treatment combination. In a within-subjects design the subjects receive multiple treatments and measurements. The main advantages of between-subjects designs are the reduced likelihood of demand effects and simpler analysis and presentation.

6. Weigh the trade-off between internal and external validity. Lab experiments, such as the protocol example, offer higher internal validity because they maximize control of extraneous vari- ables. High internal validity is a good thing because we can be more certain that the experimental variable is truly the cause of any variance in the dependent variable. Field experiments, such as the self-efficacy study, maximize external validity because they are conducted in a more natural setting meaning that the results are more likely to generalize to the actual business situation. The increased external validity comes at the expense of internal validity.

7. Use manipulations to implement a completely randomized experimental design, a randomized-block design, and a factorial experimental design. The key to randomization is to assign subjects to experimental cells in a way that spreads extraneous variables out evenly across every condition. Blocking variables can be added to simple randomized experimental designs to control for categorical variables that are expected to be related to the dependent variable. Finally, a factorial design results when multiple experimental and/or blocking variables are included in a single model. Both main effects and interactions result.

KEY TERMS AND CONCEPTS basic experimental design, 274 between-subjects design, 270 blocking variables, 255 cell, 259 cohort effect, 272 completely randomized design, 278 confound, 262 constancy of conditions, 266 control group, 258 counterbalancing, 266 demand characteristic, 263 demand effect, 263 experimental condition, 255 experimental group, 258 experimental treatment, 258

external validity, 273 extraneous variables, 262 factorial design, 280 field experiments, 268 Hawthorne effect, 264 history effect, 271 instrumentation effect, 272 interaction effect, 257 internal validity, 271 laboratory experiment, 268 main effect, 256 manipulation check, 271 maturation effects, 272 mortality effect (sample attrition), 273 nuisance variables, 261

placebo effect, 265 placebo, 265 quasiexperimental designs, 274 randomization, 261 randomized-block design, 280 repeated measures, 260 selection effect, 273 subjects, 255 systematic or nonsampling

error, 261 tachistoscope, 268 test units, 260 testing effects, 272 time series design, 278 within-subjects design, 270

Chapter 12: Experimental Research 283

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Define experimental condition, experimental treatment, and

experimental group. How are these related to the implementa- tion of a valid manipulation?

2. A tissue manufacturer that has the fourth-largest market share plans to experiment with a 50 cents off coupon during November and a buy one, get one free coupon during December. The experiment will take place at Target stores in St. Louis and Kansas City. Sales will be recorded by scanners from which mean tissue sales for each store for each month can be computed and interpreted. a. What are the independent variable and the dependent

variable? b. Prepare a “dummy” table that would describe what the

results of the experiment would look like. 3. What is the difference between a main effect and an interaction

in an experiment? In question 2, what will create a main effect? Is an interaction possible?

4. In what ways might the design in question 2 yield systematic or nonsampling error?

5. What purpose does the random assignment of subjects serve? 6. Why is an experimental confound so damaging to the conclu-

sions drawn from an experiment? 7. What are demand characteristics? How can they be minimized?

8. ETHICS Suppose researchers were experimenting with how much more satisfied consumers are with a “new and improved” version of some existing product. How might the research- ers design a placebo within an experiment testing this research question? Is using such a placebo ethical or not?

9. If a company wanted to know whether to implement a new management training program based on how much it would improve ROI in its southwest division, would you recommend a field or lab experiment?

10. ’NET Suppose you wanted to test the effect of three different e-mail requests inviting people to participate in a survey posted on the Internet. One simply contained a hyperlink with no explanation, the other said if someone participated $10 would be donated to charity, and the other said if someone participated he or she would have a chance to win $1,000. How would this experiment be conducted differently based on whether it was a between-subjects or within-subjects design? What are the advantages of a between-subjects design?

11. What is a manipulation check? How does it relate to internal validity?

12. ETHICS What role does debriefing play in ensuring that experimental procedures are consistent with good ethical practice?

RESEARCH ACTIVITIES 1. Consider the following scenario:

Sea Snapper brand gourmet frozen fish products claimed in advertising that their fish sticks are preferred more than two to one over the most popular brand, Captain John’s. The advertise- ments all include a definitive statement indicating that research exists that substantiates this claim.

Captain John’s reaction was war; or at least legal war. They decided to sue Sea Snapper claiming that the advertisements include false claims based on faulty research. In court, the research is described in great detail. Sea Snapper conducted taste tests involving four hundred consumers who indicated that they regu- larly ate frozen food products. Two hundred tasted Sea Snapper premium fish sticks and the other two hundred tasted Captain John’s premium fish sticks. Consumer preference was measured with a 100-point rating scale. The results showed the average preference score for Sea Snapper was 78.2 compared to 39.0 for Captain John’s.

Captain John’s attorney hires a research firm to assist in the lawsuit. They claim that the research is faulty because the pro- cedures were improperly conducted. First, it turns out that Sea Snapper fish sticks were always presented to consumers on a blue

plate while Captain John’s were always presented to consumers on an orange plate. Second, the Sea Snapper products used in the experiment were taken directly from the Sea Snapper kitchens to the testing facility, while the Captain John’s products were pur- chased at a local warehouse store. a. Provide a critique of the procedures used to support the

claim that Sea Snapper’s product is superior. Prepare it in a way that it could be presented as evidence in court.

b. Design an experiment that would provide a more valid test of the research question, “Do consumers prefer Sea Snapper fish sticks compared to Captain John’s fish sticks?”

2. Conduct a taste test involving some soft drinks with a group of friends. Pour them several ounces of three popular soft drinks and simply label the cups A, B, and C. Make sure they are blind to the actual brands. Then, let them drink as much as they want and record how much of each they drink. You may also ask them some questions about the drinks. Then, allow other sub- jects to participate in the same test, but this time, let them know what the three brands are. Record the same data and draw con- clusions. Does brand knowledge affect behavior and attitudes about soft drinks?

284 Part 3: Research Methods for Collecting Primary Data

Tooheys Sixty-six willing Australian drinkers helped a federal court judge decide that Tooheys didn’t engage in misleading or deceptive advertising for its 2.2 beer.

The beer contains 2.2 percent alcohol, compared to 6 percent for other beers, leading to a claim that could be interpreted as implying it was nonalcoholic.

Volunteers were invited to a marathon drinking session after the Aboriginal Legal Service claimed Tooheys’ advertising implied beer drinkers could imbibe as much 2.2 as desired without becoming legally intoxicated. Drunken driving laws prohibit anyone with a blood- alcohol level above 0.05 from getting behind the wheel in Australia.

So, an experiment was conducted to see what happens when a lot of 2.2 is consumed. But the task wasn’t easy or that much fun. Some subjects couldn’t manage to drink the required 10 “middies,” an Aussie term for a beer glass of 10 fluid ounces, over the course of an hour.

Thirty-six participants could manage only nine glasses. Four threw up and were excluded. Two more couldn’t manage the “minimum” nine glasses and had to be replaced.

Justice J. Beaumont observed that consuming enough 2.2 in an hour to reach the 0.05 level was “uncomfortable and therefore an unlikely process.” Because none of the ads mentioned such extreme quantities, he ruled they couldn’t be found misleading or deceptive.14

Questions 1. Would a lab experiment or a field experiment be more “valid”

in determining whether Tooheys could cause a normal beer consumer to become intoxicated? Explain.

2. Describe an alternate research design that would have higher validity.

3. Is the experiment described in this story consistent with good ethical practice? Likewise, comment on how the design described in question 2 would be made consistent with good ethical practices.

4. Is validity or ethics more important?

CASE 12.1

Chapter 12: Experimental Research 285

P a r t F O U r

Measurement Concepts

CHAPTER 13 Measurement and Scaling Concepts

CHAPTER 14 Attitude Measurement

CHAPTER 15 Questionnaire Design

APPENDIX 15A Question Wording and Measurement Scales for Commonly Researched Topics

After studying this chapter, you should be able to

1. Determine what needs to be measured to address a research question or hypothesis

2. Distinguish levels of scale measurement

3. Know how to form an index or composite measure

4. List the three criteria for good measurement

5. Perform a basic assessment of scale reliability and validity

C H A P T E R

G riff Mitchell is the vice president of customer relationship management (CRM) for one of the world’s largest suppliers of industrial heavy equipment. In this role, he oversees

all sales and service operations. This year, for the first time, the company has decided to perform a CRM employee evalu- ation process that will allow an overall ranking of all CRM employees. Griff knows this will be a difficult task for many reasons, not the least of which is that he oversees over a thousand employees worldwide.

The ranking will be used to single out the best performers. These employees will be rec- ognized at the company’s annual CRM confer- ence. The rankings will also be used to identify the lowest 20 percent of performers. These employees will be put on a probationary list with specific targeted improvement goals that will have to be met within 12 months or they will be fired. Griff is becoming really stressed out trying to define the performance ranking process.

Griff’s key question is, “What is perfor- mance?” Although these employees are now

often referred to as CRM employees, they have traditionally per- formed the sales function. Griff calls a meeting of senior CRM managers to discuss how ranking decisions should be made.

One manager simply argues that sales volume should be the sole criteria. She believes that “sales figures provide an objec- tive performance measure that will make the task easy and

LEARNING OUTCOMES

Measurement and Scaling Concepts

Chapter Vignette:

Money Matters?

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Introduction Not every cook or chef needs to follow a recipe to create a great dish, but most amateur chefs find one very useful. Look at Exhibit 13.1. The recipe shows ingredients that can produce a tasty chicken dish. However, many readers, even those with some cooking ability, may have a difficult time following this recipe. Why? First, many may have difficulty translating all the French terms. Second, even when this is done, many will have difficulty knowing just what amounts of what ingredients should be included. How many could easily deal with the different measures listed by the ingredients? “How much is 50 ml?” “What is 454 g?” “How much is a pinch?” “Can I use my normal measuring utensils (scales)?”

What Do I Measure? The decision statement, corresponding research questions, and research hypotheses can be used to decide what concepts need to be measured in a given project. Measurement is the process of describing some property of a phenomenon of interest, usually by assigning numbers in a reliable and valid way. The numbers convey information about the property being measured. When num- bers are used, the researcher must have a rule for assigning a number to an observation in a way that provides an accurate description.

Measurement can be illustrated by thinking about the way instructors assign students’ grades. A grade represents a student’s performance in a class. Students with higher performance should receive a different grade than do students with lower performance. Even the apparently simple con- cept of student performance is measured in many different ways. Consider the following options:

1. A student can be assigned a letter corresponding to his/her performance. a. A—Represents excellent performance b. B—Represents good performance c. C—Represents average performance d. D—Represents poor performance e. F—Represents failing performance

difficult to refute.” Another counters that for the past 22 years, he has simply used his opinion of each employee’s performance to place each of them into one of three groups: top perform- ers, good performers, and underperformers. “I think about who is easy to work with and doesn’t cause much trouble. It has worked for 22 years, why won’t it work now?” Another responds curtly, “It’s margin! It’s margin! I don’t care about sales volume; I want my guys selling things that improve my division’s profit!” One of the newer managers sits silently through most of the meeting and finally summons up the courage to speak. “Aren’t we CRM? That means performance should not be tied to sales, profits, or convenience, it should be based on how well a salesperson builds and maintains rela- tionships with customers. So, we should see how satisfied the customers assigned to the employee are and use this in the evaluation process!” After this, the meeting disintegrates into a shouting match with each manager believing the others’ ideas are flawed.

Griff feels like he is back to square one. “How do I make sure I have a valid performance measure so that all of our people are treated fairly?” He decides to seek out an opinion from a long-time friend in the research business, Robin Donald. Robin suggests that a research project may be needed to define a reliable and valid measure. She also brings up the fact that because employees from all over the world will be considered, the measure will have to maintain its reliability and validity any- where it is used! Griff agrees to the project. He also feels good about letting someone outside the company develop the mea- sure, because he certainly realizes the tremendous challenges that are present.

Griff’s situation in this vignette illustrates how difficult it can be to define, let alone measure, important business phenom- ena. While some items can be measured quite easily, others present tremendous challenges to the business researcher. This is the first of two chapters that deal with measurement in business research.

measurement

The process of describing some property of a phenomenon of interest, usually by assigning numbers in a reliable and valid way.

CHAPTER 13 Measurement and Scaling Concepts • 289

290

SURVEY

THIS! A comprehensive survey like this one involves many different types of measure- ment. The questionnaire used in this sur- vey contains multiple scale measurement levels. Try to identify one of each of the four categories of scale measurement. Then take a look at the questions shown below. What scale measurement level do these items represent? Each set of items is designed to capture a single construct. In the top portion, the items assess how much work life interferes with life outside of work. In the lower portion, the scales assess self-perceived performance. For each scale, compute a coefficient α to esti- mate reliability and then create a compos- ite scale by summing the items that make up that particular scale.

Likewise, the chapter vignette describes a situation in which Griff must develop a “recipe” for distinguishing employees based on job performance. Before the measurement process can be defined, he will have to decide exactly what it is that needs to be produced. In this case, the outcome should be a valid job performance measure.

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EXHIBIT 13.1

More Ways Than One to Measure Ingredients

(a) Recette de la Jour (b) Dogtes de Poulet Failes avec Crackers

454 g Poitrine de Poulet

50 ml Farine Tout Usage

2 ml De Poudre d’Ail

2 ml De Poudre d’Oignon

1 ml De Sel

2 Blancs d’Oeuf

50 ml De Lait Écrémé

Pincée De le Poivre Rouge

36 Crackers (Tout Crounche)

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EXHIBIT 13.2

Are There Any Validity Issues with This Measurement?

Student Percentage Grade

Difference from Next Highest

Student

Letter Grade

1 79.4% 0.5% C

2 70.0% 9.4% C

3 69.0% 1.0% D

4 79.9% NA B

2. A student can be assigned a number from 1 to 20. a. 20—Represents outstanding performance b. 11–20—Represents differing degrees of passing performance c. Below 11—Represents failing performance

3. A student can be assigned a number corresponding to a percentage performance scale. a. 100 percent—Represents a perfect score. All assignments are performed correctly. b. 60–99 percent—Represents differing degrees of passing performance, each number repre-

senting the proportion of correct work. c. 0–59 percent—Represents failing performance but still captures proportion of correct work.

4. A student can be assigned one of two letters corresponding to performance. a. P—Represents a passing mark b. F—Represents a failing mark

Actually, this is not terribly different than a manager who must assign performance scores to employees. In each case, students with different marks are distinguished in some way. However, some scales may better distinguish students. Each scale also has the potential of producing error or some lack of validity. Exhibit 13.2 illustrates a common measurement application.

Often, instructors may use a percentage scale all semester long and then be required to assign a letter grade for a student’s overall performance. Does this produce any measurement problems? Consider two students who have percentage scores of 79.4 and 70.0, respectively. The most likely outcome when these scores are translated into “letter grades” is that each receives a C (the common 10-point spread would yield a 70–80 percent range for a C). Consider a third student who finishes with a 69.0 percent average and a fourth student who finishes with a 79.9 percent average.

Which students are happiest with this arrangement? The first two students receive the same grade, even though their scores are 9.4 percent apart. The third student gets a grade lower (D) performance than the second student, even though their scores are only 1 percent different. The fourth student, who has a score only 0.5 percent higher than the first student, would receive a B. Thus, the measuring system (final grade) suggests that the fourth student outperformed the first (assuming that 79.9 is rounded up to 80) student (B versus C), but the first student did not

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CHAPTER 13 Measurement and Scaling Concepts • 291

292

outperform the second (each gets a C), even though the first and second students have the greatest difference in percentage scores.

A strong case can be made that error exists in this measurement system. All measurement, particularly in the social sciences, contains error. Researchers, if we are to represent concepts truth- fully, must make sure that the measures used, if not perfect, are accurate enough to yield correct conclusions. Ultimately, research and measurement are tied closely together.

Concepts A researcher has to know what to measure before knowing how to measure something. The problem definition process should suggest the concepts that must be measured. A concept can be thought of as a generalized idea that represents something of meaning. Concepts such as age, sex, education, and number of children are relatively concrete properties. They present few problems in either defini- tion or measurement. Other concepts are more abstract. Concepts such as loyalty, personality, chan- nel power, trust, corporate culture, customer satisfaction, value, and so on are more difficult to both define and measure. For example, loyalty has been measured as a combination of customer share (the relative proportion of a person’s purchases going to one competing brand/store) and commitment (the degree to which a customer will sacrifice to do business with a brand/store).1 Thus, we can see that loyalty consists of two components, the first is behavioral and the second is attitudinal.

Operational Definitions Researchers measure concepts through a process known as operationalization. This process involves identifying scales that correspond to variance in the concept. Scales, just as a scale you may use to check your weight, provide a range of values that correspond to different values in the concept being

concept

A generalized idea that represents something of

meaning.

operationalization

The process of identifying scales that correspond to

variance in a concept that will be involved in a research

process.

scales

A device providing a range of values that correspond to different values in a concept

being measured.

Do friends influence your purchase decisions? Are the clothes you buy based on the approval of others? While we all have experienced “peer pressure,” research has shown that some individuals are more susceptible to such pressure than others. Most often, researchers have thought of this interpersonal influence to be present in conspicuous consumption or socially visible products. Recent research, however, shows such influ- ence can occur even in the selection of less visible products and services, including investments.

Researchers used the construct susceptibility to interpersonal influence (SCII) to investigate how information obtained from others affects investment decisions. First, this construct had to be conceptualized and measured. Based on earlier studies, SCII is thought to be composed of two parts—susceptibility to infor- mational influences (SII) and susceptibility to normative influences (SNI). Susceptibility to informational influences captures the will- ingness of a person to accept information from another as real- ity. Information is gained either from asking others for advice or observing their actions. Susceptibility to normative influences is a person’s willingness to comply with the expectations of others. SNI is motivated by a desire to build self-image through association with some other person or group. Questions were developed to measure both SII and SNI, together capturing the domain of SCII.

The research found:

■ Respondents that do not have sufficient investment knowl- edge, and have strong social needs, perceive high levels of risk associated with investing and are particularly susceptible to interpersonal influences.

■ Respondents with greater susceptibility to informational influences trade less, while individuals with greater suscepti- bility to normative influences trade more.

■ Respondents do react to outside influence, to the point that they are willing to sacrifice investment returns for social rewards.

Consumers need to carefully consider the information they are exposed to and the investment decisions they make. Are they choosing what they believe to be the best investment, or one that wins them favor with their friends?

Source: Hoffmann, A. O. I. and T. L. J. Broekhuizen, “Susceptibility to and Impact of Interpersonal Influence in an Investment Context,” Journal of the Academy of Marketing Science 37(4) (2009): 488–803. Reproduced with permission of Sage Publications Inc. in the format Journal via Copyright Clearance Center.

Peer Pressure and Investing Behavior

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measured. In other words, scales provide correspondence rules that indicate that a certain value on a scale corresponds to some true value of a concept. Hopefully, they do this in a truthful way.

Here is an example of a correspondence rule: “Assign the numbers 1 through 7 according to how much trust that you have in your sales representative. If the sales representative is perceived as completely untrustworthy, assign the numeral 1; if the sales rep is completely trustworthy, assign a 7.”

Variables Researchers use variance in concepts to make diagnoses. Therefore, when we defined variables in an earlier chapter, we really were suggesting that variables capture different concept values. Scales capture variance in concepts and, as such, the scales provide the researcher’s variables. Thus, for practical purposes, once a research project is underway, there is little difference between a concept and a variable. Consider the following hypothesis:

H1: Experience is positively related to job performance.

The hypothesis implies a relationship between two variables, experience and job performance. The variables capture variance in the experience and performance concepts. One employee may have 15 years of experience and be a top performer. A second may have 10 years experience and be a good performer. The scale used to measure experience is quite straightforward in this case and would involve simply providing the number of years an employee has been with the company. Job performance, on the other hand, can be quite complex, as described in the opening vignette.

Constructs Sometimes, a single variable cannot capture a concept alone. Using multiple variables to measure one concept can often provide a more complete account of some concept than could any single variable. Even in the physical sciences, multiple measurements are often used to make sure an accurate rep- resentation is obtained. In social science, many concepts are measured with multiple measurements.

A construct is a term used for concepts that are measured with multiple variables. For instance, when a business researcher wishes to measure the customer orientation of a salesperson, several variables like these may be used, each captured on a 1–5 scale:

1. I offer the product that is best suited to a customer’s problem. 2. A good employee has to have the customer’s best interests in mind. 3. I try to find out what kind of products will be most helpful to a customer.2

Constructs can be very helpful in operationalizing a concept. An operational definition is like a manual of instructions or a recipe: even the truth of a state-

ment like “Gaston Gourmet likes key lime pie” depends on the recipe. Different instructions lead to different results. In other words, how we define the construct will affect the way we measure it.3

An operational definition tells the investigator, “Do such-and-such in so-and-so manner.”4 Exhibit 13.3 presents a concept definition and an operational definition from a study on a con- struct called susceptibility to interpersonal influence.

Levels of Scale Measurement Business researchers use many scales or number systems. Not all scales capture the same richness in a measure. Not all concepts require a rich measure. Traditionally, the level of scale measurement is seen as important because it determines the mathematical comparisons that are allowable. The four levels or types of scale measurement are nominal, ordinal, interval, and ratio level scales. Each type offers the researcher progressively more power in analyzing and testing the validity of a scale.

Nominal Scale Nominal scales represent the most elementary level of measurement. A nominal scale assigns a value to an object for identification or classification purposes only. The value can be, but does not

correspondence rules

Indicate the way that a certain value on a scale corresponds to some true value of a concept.

‘‘Not everything that can be counted counts, and not everything that counts can be counted.’’—ALBERT EINSTEIN

construct

A term used to refer to concepts measured with multiple variables.

nominal scales

Represent the most elementary level of measurement in which values are assigned to an object for identification or classification purposes only.

CHAPTER 13 Measurement and Scaling Concepts • 293

have to be, a number because no quantities are being represented. In this sense, a nominal scale is truly a qualitative scale. Nominal scales are extremely useful, and are sometimes the only appropri- ate measure, even though they can be considered elementary.

Business researchers use nominal scales quite often. Suppose a soft drink company was experi- menting with three different types of sweeteners (cane sugar, corn syrup, or fruit extract). The researchers would like the experiment to be blind, so the three drinks that subjects are asked to taste are labeled A, B, or C, not cane sugar, corn syrup, or fruit extract. Or, a researcher interested in examining the production efficiency of a company’s different plants might refer to them as “Plant 1,” “Plant 2,” and so forth.

Nominal scaling is arbitrary. What we mean is that each label can be assigned to any of the categories without introducing error. For instance, in the soft drink example above, the researcher

can assign the letter C to any of the three options without damaging scale validity. The researcher could just as easily use numbers instead of letters, as in the plant efficiency example, and vice versa. If so, cane sugar, corn syrup, and fruit extract might be iden- tified with the numbers 1, 2, and 3, respectively, or even 543, 26, and 2010, respectively. The important thing to note is the numbers are not represent- ing different quantities or the value of the object. Thus any set of numbers, letters, or any other identification is equally valid.

We encounter nominal number- ing systems all the time. Sports uni- form numbers are nominal numbers. Ben Roethlisberger is identified on the football field by his jersey number. ©

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Athletes wear nominal numbers on their jerseys. Ben Roethlisberger (number 7) wears number 7 for the Pittsburgh Steelers, while Trai Essex wears number 79. This does not mean that Trai is more than 11 times better than Ben, or bigger than Ben, or faster than Ben, or anything else.

EXHIBIT 13.3

Susceptibility to Interpersonal Influence: An Operational Definition

Concept Conceptual Definition Operational Definition

Susceptibility to interpersonal influence

Susceptibility to interpersonal influence is “the need to identify with or enhance one’s image in the opinion of significant others through the acquisition and use of products and brands, the willingness to conform to the expectations of others regarding purchase decisions, and/or the tendency to learn about products and services by observing others or seeking information from others.” Susceptibility to interpersonal influence is a general trait that varies across individuals.

Please tell me how much you agree or disagree with each of the following statements:

1. I frequently gather information about stocks from friends or family before I invest in them.

2. To make sure I buy the right stock, I often observe what other investors invest in.

3. I often consult other people to help choose the best stock to invest in. 4. If I have little experience with a (type of) stock, I often ask my friends and

acquaintances about the stock. 5. I like to know what investment decisions make good impressions on others. 6. I generally purchase those stocks that I think others will approve of. 7. I often identify with other people by purchasing or selling the same stocks

they sell or purchase. 8. I achieve a sense of belonging by purchasing or selling the same stocks that

others purchase or sell. 9. If others can see in which stocks I invest, I often invest in stocks that they

invest in.

Sources: Bearden, W. O., R. G. Netemeyer, and M. F. Mobley, Handbook of Marketing Scales: Multi Item Measures for Marketing and Consumer Behavior Research, 2nd ed. (Newbury Park, Calif: Sage Publications, 1999); Hoffmann, A. O. I. and T. L. J. Broekhuizen, “Susceptibility to and Impact of Interpersonal Influence in an Investment Context,” Journal of the Academy of Marketing Science 37(4) (2009), 488–503.

294 • PART FOUR Measurement Concepts

School bus numbers are nominal in that they simply identify a bus. Elementary school buses some- times use both a number and an animal designation to help small children get on the right bus. So, bus number 8 may also be the “tiger” bus, but it could just as easily be the “horse” bus or the “cardinal” bus.

The first drawing in Exhibit 13.4 depicts the number 7 on a horse’s colors. This is merely a label to allow bettors and racing enthusiasts to identify the horse. The assignment of a 7 to this horse does not mean that it is the seventh fastest horse or that it is the seventh biggest, or anything else meaningful. But the 7 does let you know when you have won or lost your bet!

In sum, nominal scale properties mean the numbering system simply identifies things. Exhibit 13.5 lists some nominal scales commonly used by business researchers.

Ordinal Scale Ordinal scales allow things to be arranged in order based on how much of some concept they possess. In other words, an ordinal scale is a ranking scale. In fact, we often use the term rank order to describe an ordinal scale. When class rank for high school stu- dents is determined, we use an ordinal scale. We know that the student ranked seventh finished ahead of the student ranked eighth, who finished ahead of the ninth ranked student. However, we do not really know what the actual GPA was or how close these three stu- dents are to each other in overall grade point average.

Research participants often are asked to rank things based on pref- erence. So, preference is the concept,

EXHIBIT 13.4

Nominal, Ordinal, Interval, and Ratio Scales Provide Different Information

Show Place

Win

20 Seconds 1.0 Second

Nominal

Ordinal

Interval

Ratio 1 minute 59 2/5 seconds for 11/4 miles

5 6

us an

V an

E tt

Without nominal scales, how would you know which terminal to go to at this airport?

ordinal scales

Ranking scales allowing things to be arranged based on how much of some concept they possess.

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CHAPTER 13 Measurement and Scaling Concepts • 295

and the ordinal scale lists the options from most to least preferred, or vice versa. Five objects can be ranked from 1–5 (least preferred to most preferred) or 1–5 (most preferred to least preferred) with no loss of meaning. In this sense, ordinal scales are somewhat arbitrary, but not nearly as arbitrary as a nominal scale.

When business professors take some time off and go to the race track, even they know that a horse finishing in the “show” position has finished after the “win” and “place” horses (see the second drawing in Exhibit 13.4). The order of finish can be accurately represented by an ordinal scale using an ordered number rule:

■ Assign 1 to the “win” position ■ Assign 2 to the “place” position ■ Assign 3 to the “show” position

Perhaps the winning horse defeated the place horse by a nose, but the place horse defeated the show horse by 20 seconds. The ordinal scale does not tell how far apart the horses were, but it is good enough to let someone know the result of a wager. Typical ordinal scales in business research ask respondents to rank their three favorite brands, have personnel managers rank potential employees after job interviews, or judge investments as “buy,” “hold,” or “sell.” Researchers know how each item, person, or stock is judged relative to others, but they do not know by how much.

EXHIBIT 13.5

Facts About the Four Levels of Scales

Level Examples Numerical Operations Descriptive Statistics

Nominal Student ID number Yes – No Male – Female Buy – Did Not Buy East region Central region West region

Counting • Frequencies • Mode

Ordinal Student class rank Please rank your three favorite movies. Choose from the following: • Dissatisfied • Satisfied • Very satisfied • Delighted Indicate your level of education: • Some high school • High school diploma • Some college • College degree • Graduate degree

Counting Ordering

• Frequencies • Mode • Median • Range

Interval Student grade point average (GPA) Temperature (Celsius and Fahrenheit) Points given on an essay question 100-point job performance rating provided by supervisor

Common arithmetic operations

• Frequencies • Mode • Median • Range • Mean • Variance • Standard deviation

Ratio Amount spent on last purchase Salesperson sales volume Number of stores visited on a shopping trip Annual family income Time spent viewing a web page

All arithmetic operations

• Frequencies • Mode • Median • Range • Mean • Variance • Standard deviation

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Interval Scale Interval scales have both nominal and ordinal properties, but they also capture information about differences in quantities of a concept. So, not only would a sales manager know that a particular salesperson outperformed a colleague, information that would be available with an ordinal mea- sure, but the manager would know by how much. If a professor assigns grades to term papers using a numbering system ranging from 1.0–20.0, not only does the scale represent the fact that a student with a 16.0 outperformed a student with 12.0, but the scale would show by how much (4.0).

The third drawing in Exhibit 13.4 depicts a horse race in which the win horse is one second ahead of the place horse, which is 20 seconds ahead of the show horse. Not only are the horses identified by the order of finish, but the difference between each horse’s performance is known. So, horse number 7 and horse number 6 performed similarly (1 second apart), but horse number 5 performed not nearly as well (20 seconds slower).

The classic example of an interval scale is temperature. Consider the following weather:

■ June 6 was 80°F ■ December 7 was 40°F

The interval Fahrenheit scale lets us know that December 7 was 40°F colder than June 6. But, we cannot conclude that December 7 was twice as cold as June 6. Although the actual numeral 80 is indeed twice as great as 40, remember that this is a scaling system. In this case, the scale is not iconic, meaning that it does not exactly represent some phenomenon. In other words, there is no naturally occurring zero point—a temperature of 0° does not mean an absence of heat (or cold for that matter).

Since temperature scales are interval, the gap between the numbers remains constant (i.e., the difference between 20° and 30° is 10°, just as the difference between 68° and 78° is 10°). This is an important element of interval scales and allows us to convert one scale to another. In this case, we can convert Fahrenheit temperatures to Celsius scale. Then, the following would result:

■ June 6 was 26.7°C ■ December 7 was 4.4°C

Obviously, now we can see that December 7 was not twice as cold as June 6. December 7 was 40°F or 22.3°C cooler, depending upon your thermometer. Interval scales are very useful because they capture relative quantities in the form of distances between observations. No matter what thermometer is used, December 7 was colder than June 6.

Exhibit 13.5 provides some examples of interval level scales.

Ratio Scale Ratio scales represent the highest form of measurement in that they have all the properties of inter- val scales with the additional attribute of representing absolute quantities. Interval scales possess only relative meaning, whereas ratio scales represent absolute meaning. In other words, ratio scales provide iconic measurement.

Zero, therefore, has meaning in that it represents an absence of some concept. An absolute zero is the defining characteristic differentiating between ratio and interval scales. For example, money is a way to measure economic value. Consider the following items offered for sale in an online auction:

■ “Antique” 1970s digital watch—did not sell and there were no takers for free ■ Gold-filled Elgin wristwatch circa 1950—sold for $100 ■ Vintage stainless steel Omega wristwatch—sold for $1,000 ■ Antique rose gold Patek Philippe “Top Hat” wristwatch—sold for $9,000

We can make the ordinal conclusions that the Patek was worth more than the Omega, and the Omega was worth more than the Elgin. All three of these were worth more than the 1970s digital watch. We can make interval conclusions such as that the Omega was worth $900 more than the Elgin. We can also conclude that the Patek was worth nine times as much as the Omega and that

ratio scales

Represent the highest form of measurement in that they have all the properties of interval scales with the additional attribute of representing absolute quantities; characterized by a meaningful absolute zero.

interval scales

Scales that have both nominal and ordinal properties, but that also capture information about differences in quantities of a concept from one observation to the next.

CHAPTER 13 Measurement and Scaling Concepts • 297

the 1970s watch was worthless (selling price = $0.00). The latter two conclusions are possible because price represents a ratio scale.

The fourth drawing in Exhibit 13.4 shows the time it took horse 7 to complete the race. If we know that horse 7 took 1 minute 59 2/5 seconds to finish the race, and we know the time it took for all the other horses, we can determine the time difference between horses 7, 6, and 5. In other words, if we knew the ratio information regarding the performance of each horse—the time to complete the race—we could determine the interval level information and the ordinal level information. However, if we only knew the ordinal level information, we could not create the interval or ratio information. Similarly, with only the interval level data we cannot create the ratio level information.

Using our opening vignette as an example, Griff could decide to use a ratio measure— salesperson annual sales volume—as the indicator of performance for the CRM division. If he did this, he could create interval level data (groups of salespeople) or ordinal level data (the rank of each salesperson). However, this would be valid only if performance was truly equal to sales.

Mathematical and Statistical Analysis of Scales While it is true that mathematical operations can be performed with numbers from nominal scales, the result may not have a great deal of meaning. For instance, a school district may perform math- ematical operations on the nominal school bus numbers. With this, they may find that the average school bus number is 77.7 with a standard deviation of 20.5. Will this help them use the buses more efficiently or better assign bus routes? Probably not. Can a professor judge the quality of her classes by the average ID number? While it could be calculated, the result is meaningless. Thus, although you can put numbers into formulas and perform calculations with almost any numbers, the researcher has to know the meaning behind the numbers before meaningful conclusions can be drawn.5

Discrete Measures Discrete measures are those that take on only one of a finite number of values. A discrete scale is most often used to represent a classification variable. Therefore, discrete scales do not represent intensity of measures, only membership. Common discrete scales include any yes-or-no response, matching, color choices, or practically any scale that involves selecting from among a small number of categories. Thus, when someone is asked to choose from the following responses

■ Disagree ■ Neutral ■ Agree

the result is a discrete value that can be coded 1, 2, or 3, respectively. This is also an ordinal scale to the extent that it represents an ordered arrangement of agreement. Nominal and ordinal scales are discrete measures.

Certain statistics are most appropriate for discrete measures. Exhibit 13.5 shows statistics for each scale level. The largest distinction is between statistics used for discrete versus continuous measures. For instance, the central tendency of discrete measures is best captured by the mode. When a student wants to know what the most likely grade is for a management class, the mode will be very useful. Observe the results below from the previous semester:

discrete measures

Measures that take on only one of a finite number of values.

‘‘When you can measure what you are talking about and express it in numbers, you know something about it.’’—WILLIAM THOMPSON,

LORD KELVIN

A 3 Students D 3 Students

B 9 Students F 1 Students

C 6 Students

The mode is a “B” since more students obtained that value than any other value. Therefore, the “average” student would expect a B in that class.

298 • PART FOUR Measurement Concepts

Continuous Measures Continuous measures are those assigning values anywhere along some scale range in a place that corresponds to the intensity of some concept. Ratio measures are continuous measures. Thus, when Griff measures sales for each salesperson using the dollar amount sold, he is assigning a con- tinuous measure. A number line could be constructed ranging from the least amount sold to the most, and a spot on the line would correspond exactly to a salesperson’s performance.

Strictly speaking, interval scales are not necessarily continuous. Consider the following com- mon type of survey question:

Strongly Disagree Disagree Neutral Agree Strongly Agree

I enjoy participating in online auctions

1 2 3 4 5

continuous measures

Measures that reflect the intensity of a concept by assigning values that can take on any value along some scale range.

index measures

An index assigns a value based on how much of the concept being measured is associated with an observation. Indexes often are formed by putting several variables together.

attribute

A single characteristic or fundamental feature of an object, person, situation, or issue.

This is a discrete scale because only the values 1, 2, 3, 4, or 5 can be assigned. Furthermore, it is an ordinal scale because it only orders based on agreement. We really have no way of knowing that the difference in agreement of somebody marking a 5 instead of a 4 is the same as the difference in agree- ment of somebody marking a 2 instead of a 1. Therefore, the mean is not an appropriate way of stating central tendency and, technically, we really shouldn’t use many common statistics on these responses.

However, as a scaled response of this type takes on more values, the error introduced by assum- ing that the differences between the discrete points are equal becomes smaller. This may be seen by imagining a Likert scale (the traditional business research agreement scale shown above) with a thou- sand levels of agreement rather than three. The differences between the different levels become so small with a thousand levels that only tiny errors could be introduced by assuming each interval is the same. Therefore, business researchers generally treat interval scales containing five or more categories of response as interval. When fewer than five categories are used, this assumption is inappropriate.

The researcher should keep in mind, however, the distinction between ratio and interval mea- sures. Errors in judgment can be made when interval measures are treated as ratio. For example, attitude is usually measured with an interval scale. An attitude of zero means nothing. In fact, atti- tude would only have meaning in a relative sense. In other words, attitude takes on meaning when one person’s response is compared to another or through some other comparison. A single attitude score alone contains little useful information.

The mean and standard deviation may be calculated from continuous data. Using the actual quantities for arithmetic operations is permissible with ratio scales. Thus, the ratios of scale values are meaningful. A ratio scale has all the properties of nominal, ordinal, and interval scales. However, the same cannot be said in reverse. An interval scale, for example, has ordinal and nominal proper- ties, but it does not have ratio properties (see Exhibit 13.5).

Chapters 19 through 23 further explore the limitations scales impose on the mathematical analysis of data.

Index Measures Earlier, we distinguished constructs as concepts that require multiple variables to measure them adequately. Looking back to the chapter vignette, could it be that multiple items will be required to adequately represent job performance? Likewise, a consumer’s attitude toward some product is usually a function of multiple attributes. An attribute is a single characteristic or fundamental feature of an object, person, situation, or issue.

Indexes and Composites Multi-item instruments for measuring a construct are called index measures, or composite measures. An index measure assigns a value based on how much of the concept being measured is associated with an observation. Indexes often are formed by putting several variables together. For example,

CHAPTER 13 Measurement and Scaling Concepts • 299

a social class index might be based on three weighted variables: occupation, education, and area of residence. Usually, occupation is seen as the single best indicator and would be weighted highest. With an index, the different attributes may not be strongly correlated with each other. A person’s education does not always relate strongly to their area of residence. The American Consumer Satis- faction Index shows how satisfied American consumers are based on an index of satisfaction scores. Readers are likely not surprised to know that Americans appear more satisfied with soft drinks than they are with cable TV companies based on this index.6

Composite measures also assign a value based on a mathematical derivation of multiple vari- ables. For example, salesperson satisfaction may be measured by combining questions such as, “How satisfied are you with your job? How satisfied are you with your territory? How satisfied are you with the opportunity your job offers?” For most practical applications, composite measures and indexes are computed in the same way.7

Computing Scale Values Exhibit 13.6 demonstrates how a composite measure can be created from common rating scales. This scale was developed to assess how much a consumer trusts a website.8 This particular compos- ite represents a summated scale. A summated scale is created by simply summing the response to each item making up the composite measure. For this scale, a respondent that judged the website as extremely trustworthy would choose SA (value of 5) for each question. Across the five ques- tions, this respondent’s score would be 25. Conversely, a respondent that thought the website was very untrustworthy would chose SD (value of 1) for each question; a total of 5. Most respondents would likely be somewhere between these extremes. For the example respondent in Exhibit 13.6, the summated scale score would be 13 based on his responses to the five items (2 + 3 + 2 + 2 + 4 = 13). A researcher may sometimes choose to average the scores rather than summing them. The advantage to this is that the composite measure is expressed on the same scale (1–5 rather than 5–25) as the original items. So, instead of a 13, the consumer would have a score of 2.6. While this approach might be more easily understood, the information contained in either situa- tion (13 versus 2.6) is the same.

composite measures

Assign a value to an observation based on a

mathematical derivation of multiple variables.

summated scale

A scale created by simply summing (adding together) the response to each item making

up the composite measure.

EXHIBIT 13.6

Computing a Composite Scale

Item Strongly Disagree (SD) → Strongly Agree (SA)

This site appears to be more trustworthy than other sites I have visited.

SD D N A SA

My overall trust in this site is very high. SD D N A SA

My overall impression of the believability of the information on this site is very high.

SD D N A SA

My overall confidence in the recommendations on this site is very high.

SD D N A SA

The company represented in this site delivers on its promises.

SD D N A SA

Computation: Scale Values: SD = 1, D = 2, N = 3, A = 4, SA = 5

Thus, the Trust score for this consumer is 2 + 3 + 2 + 2 + 4 = 13

Sometimes, a response may need to be reverse-coded before computing a summated or aver- aged scale value. Reverse coding means that the value assigned for a response is treated oppositely from the other items. If a sixth item was included on the website trust scale that said, “I do not trust this website,” reverse coding would be necessary to make sure the composite made sense. For example, the respondent that judged the website is extremely trustworthy would choose SA for the

reverse coding

Changing the value of a response to a scale so it is

opposite of the original value. For example, a scale from 1–5 is reversed so 1=5, 2=4, 3=3,

4=2, and 5=1. This if often done so negative items in a scale are scored in the same

direction as positive items.

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300 • PART FOUR Measurement Concepts

301

first five items, then SD for the sixth. We can see that we would not want to just add these up, as this score of 21 would not really reflect someone that felt very positive about the trustworthiness of the site. Since the content of the sixth item is the reverse of trust (distrust), so the scale itself should be reversed. Thus, on a 5-point scale, the values are reversed as follows:

■ 5 becomes 1 ■ 4 becomes 2 ■ 3 stays 3 ■ 2 becomes 4 ■ 1 becomes 5

After the reverse coding, our respondent that felt the website was trustworthy would have a sum- mated score of 25, which does correctly reflect a very positive attitude. If the respondent described in Exhibit 13.6 responded to this new item with a SA (5), it would be reverse-coded as a 1 before computing the summated scale. Thus, the summated scale value for the six items would become 14. The process of reverse coding is discussed in the Research Snapshot “Recoding Made Easy.”

Three Criteria for Good Measurement The three major criteria for evaluating measurements are reliability, validity, and sensitivity.

Reliability Reliability is an indicator of a measure’s internal consistency. Consistency is the key to understand- ing reliability. A measure is reliable when different attempts at measuring something converge on the same result. For example, consider an exam that has three parts: 25 multiple-choice questions, two essay questions, and a short case. If a student gets 20 of the 25 (80 percent) multiple-choice questions correct, we would expect she would also score about 80 percent on the essay and case portions of the exam. Further, if a professor’s research tests are reliable, a student should tend toward

RESEARCH SNaPSHOt

Most computer statistical software makes scale recoding easy. The screenshot shown here is from SPSS (Statistical Package for the Social Sciences), perhaps the most widely used statistical software in business-related research. All that needs to be done to reverse-code a scale is to go through the right click-through sequence described below:

1. Click on transform. 2. Click on recode. 3. Choose to recode into the same variable. 4. Select the variable(s) to be recoded. 5. Click on old and new values. 6. Use the menu that appears to enter the old values and the

matching new values. Click add after entering each pair. 7. Click continue.

This process would successfully recode a variable that needed to be reverse-coded.

Recoding Made Easy

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reliability

An indicator of a measure’s internal consistency.

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consistent scores on all tests. In other words, a student who scores 80 percent on the first test should score close to 80 percent on all subsequent tests. Another way to look at this is that the student who achieves the best score on one test will exhibit scores close to the best score in the class on the other tests. If it is difficult to predict what students would score on a test by examining their previous test scores, the tests probably lack reliability or the students are not preparing the same each time.

So, the concept of reliability revolves around consistency. Think of a scale to measure weight. You would expect this scale to be consistent from one time to the next. If you stepped on the scale and it read 140 pounds, then got off and back on, you would expect it to again read 140. If it read 110 the second time, while you may be happier, the scale would not be reliable.

Internal Consistency Internal consistency represents a measure’s homogeneity. An attempt to measure trustworthiness may require asking several similar but not identical questions, as shown in Exhibit 13.6. The set of items that make up a measure are referred to as a battery of scale items. Internal consistency of a multiple-item measure can be measured by correlating scores on subsets of items making up a scale.

The split-half method of checking reliability is performed by taking half the items from a scale (for example, odd-numbered items) and checking them against the results from the other half (even-numbered items). The two scale halves should produce similar scores and correlate highly. The problem with split-half method is determining the two halves. Should it be even- and odd- numbered questions? Questions 1–3 compared to 4–6? Coefficient alpha provides a solution to this dilemma.

Coefficient alpha is the most commonly applied estimate of a multiple-item scale’s reliability.9 Coefficient α represents internal consistency by computing the average of all possible split-half reliabilities for a multiple-item scale. The coefficient demonstrates whether or not the different items converge. Although coefficient α does not address validity, many researchers use α as the sole indicator of a scale’s quality. Coefficient alpha ranges in value from 0, meaning no consistency, to 1, meaning complete consistency (all items yield corresponding values). Generally speaking, scales with a coefficient α between 0.80 and 0.95 are considered to have very good reliability. Scales with a coefficient α between 0.70 and 0.80 are considered to have good reliability, and an α value between 0.60 and 0.70 indicates fair reliability. When the coefficient α is below 0.6, the scale has poor reliability.10 Most statistical software packages, such as SPSS, will easily compute coefficient α.

Test-Retest Reliability The test-retest method of determining reliability involves administering the same scale or measure to the same respondents at two separate times to test for stability. If the measure is stable over time, the test, administered under the same conditions each time, should obtain similar results. Test-retest reliability represents a measure’s repeatability.

Suppose a researcher at one time attempts to measure buying intentions and finds that 12 percent of the population is willing to purchase a product. If the study is repeated a few weeks later under similar conditions, and the researcher again finds that 12 percent of the population is willing to purchase the product, the measure appears to be reliable. High stability correlation or consistency between two measures at time 1 and time 2 indicates high reliability.

Let’s assume that a person does not change his or her attitude about dark beer. Attitude might be measured with an item like the one shown below:

I prefer dark beer to all other types of beer.

If repeated measurements of that individual’s attitude toward dark beer are taken with the same scale, a reliable instrument will produce the same results each time the scale is measured. Thus one’s attitude in October 2009 should tend to be the same as one’s attitude in May 2010. When a measuring instrument produces unpredictable results from one testing to the next, the results are said to be unreliable because of error in measurement.

internal consistency

Represents a measure’s homogeneity or the extent

to which each indicator of a concept converges on some

common meaning.

split-half method

A method for assessing internal consistency by checking the

results of one-half of a set of scaled items against the results

from the other half.

coefficient alpha (a)

The most commonly applied estimate of a multiple-item

scale’s reliability. It represents the average of all possible

split-half reliabilities for a construct.

test-retest method

Administering the same scale or measure to the same

respondents at two separate points in time to test for

stability.

302 • PART FOUR Measurement Concepts

As another example, consider these remarks by a Gillette executive made about the reliability problems in measuring reactions to razor blades:

There is a high degree of noise in our data, a considerable variability in results. It’s a big mish-mash, what we call the night sky in August. There are points all over the place. A man will give a blade a high score one day, but the next day he’ll cut himself a lot and give the blade a terrible score. But on the third day, he’ll give the same blade a good score. What you have to do is try to see some pattern in all this. There are some gaps in our knowledge.11

Measures of test-retest reliability pose two problems that are common to all longitudinal stud- ies. First, the premeasure, or first measure, may sensitize the respondents to their participation in a research project and subsequently influence the results of the second measure (you may recall we referred to this as “demand characteristics” in Chapter 12). Furthermore, if the time between mea- sures is long, there may be an attitude change or other maturation of the subjects. Thus, a reliable measure can indicate a low or a moderate correlation between the first and second administration, but this low correlation may be due to an attitude change over time rather than to a lack of reli- ability in the measure itself.

Validity Good measures should be both consistent and accurate. Reliability represents how consistent a measure is, in that the different attempts at measuring the same thing converge on the same point. Accuracy deals more with how a measure assesses the intended concept. Validity is the accuracy of a measure or the extent to which a score truthfully represents a concept. In other words, are we accurately measuring what we think we are measuring?

Achieving validity is not a simple matter. The opening vignette describes this point. The job performance measure should truly reflect job performance. If a supervisor’s friendship affects the performance measure, then the scale’s validity is diminished. Likewise, if the performance scale is defined as effort, the result may well be a reliable scale but not one that actually reflects perfor- mance. Effort may well lead to performance but effort probably does not equal performance.

Students should be able to empathize with the following validity problem. Consider the con- troversy about highway patrol officers using radar guns to clock speeders. A driver is clocked at 83 mph in a 55 mph zone, but the same radar gun aimed at a house registers 28 mph. The error occurred because the radar gun had picked up impulses from the electrical system of the squad car’s idling engine. Obviously, the house was not moving, thus how can we be sure the car was speeding? In this case, we would certainly question if the accusation that the car was actually going 83 mph is completely valid.

Establishing Validity Researchers have attempted to assess validity in many ways. They attempt to provide some evidence of a measure’s degree of validity by answering a variety of questions. Is there a consensus among other researchers that my attitude scale measures what it is supposed to measure? Does my measure cover everything that it should? Does my measure correlate with other measures of the same concept? Does the behavior expected from my measure predict actual observed behavior? The four basic approaches to establishing validity are face validity, content validity, criterion validity, and construct validity.

Face validity refers to the subjective agreement among professionals that a scale logically reflects the concept being measured. Do the test items look like they make sense given a concept’s definition? When an inspection of the test items convinces experts that the items match the defini- tion, the scale is said to have face validity.

Clear, understandable questions such as “How many children do you have?” generally are agreed to have face validity. But it becomes more difficult to assess face validity in regard to more compli- cated business phenomena. For instance, consider the concept of customer loyalty. Does the statement “I prefer to purchase my groceries at Delavan Fine Foods” appear to capture loyalty? How about “I am very satisfied with my purchases from Delavan Fine Foods”? What about “Delavan Fine Foods offers very good value”? While the first statement appears to capture loyalty, it can be argued the

validity

The accuracy of a measure or the extent to which a score truthfully represents a concept.

face validity

A scale’s content logically appears to reflect what was intended to be measured.

CHAPTER 13 Measurement and Scaling Concepts • 303

second question is not loyalty but rather satisfaction. What does the third statement reflect? Do you think it looks like a loyalty statement?

In scientific studies, face validity might be considered a first hurdle. In comparison to other forms of validity, face validity is relatively easy to assess. However, researchers are generally not satisfied with simply establishing face validity. Because of the elusive nature of attitudes and other business phenomena, additional forms of validity are sought.

Content validity refers to the degree that a measure covers the domain of interest. Do the items capture the entire scope, but not go beyond, the concept we are measuring? If an exam is supposed to cover Chapters 1–5, it is fair for students to expect that questions should come from all five chapters, rather than just one or two. It is also fair to assume that the questions will not come from Chapter 6. Thus, when students complain about the material on an exam, they are often claiming it lacks content validity. Similarly, an evaluation of an employee’s job performance should cover all important aspects of the job, but not something outside of the employee’s speci- fied duties.

It has been argued that shoppers receive value from two primary elements.12 Hedonic shop- ping value refers to the pleasure and enjoyment one gets from the shopping experience, while util- itarian shopping value refers to value received from the actual acquisition of the product desired. If a researcher assessing shopping value only asked questions regarding the utilitarian aspects of shopping, we could argue the measure lacks content validity since part of the domain (hedonic value) is ignored.

Criterion validity addresses the question, “How well does my measure work in practice?” Because of this, criterion validity is sometimes referred to as pragmatic validity. In other words, is my measure practical? Criterion validity may be classified as either concurrent validity or predictive validity depending on the time sequence in which the new measurement scale and the criterion measure are correlated. If the new measure is taken at the same time as the criterion measure and is shown to be valid, then it has concurrent validity. Predictive validity is established when a new measure predicts a future event. The two measures differ only on the basis of a time dimension—

that is, the criterion measure is separated in time from the predictor measure.

For instance, a home pregnancy test is designed to have concurrent validity—to accurately determine if a person is preg- nant at the time of the test. Fertility tests, on the other hand, are designed for pre- dictive validity—to determine if a person can become pregnant in the future. In a business setting, participants in a train- ing seminar might be given a test to assess their knowledge of the concepts covered, establishing concurrent validity. Personnel managers may give potential employees an exam to predict if they will be effective salespeople (predictive validity). While face validity is a subjective evaluation, criterion validity provides a more rigorous empiri- cal test.

Construct validity exists when a measure reliably measures and truthfully represents a unique concept. Construct validity consists of several components, including:

■ Face validity ■ Content validity ■ Criterion validity ■ Convergent validity ■ Discriminant validity

content validity

The degree that a measure covers the breadth of the

domain of interest.

criterion validity

The ability of a measure to correlate with other standard

measures of similar constructs or established criteria.

construct validity

Exists when a measure reliably measures and truthfully

represents a unique concept; consists of several components including face validity, content

validity, criterion validity, convergent validity, and

discriminant validity.

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304 • PART FOUR Measurement Concepts

We have discussed face validity, content validity, and criterion validity. Before we move further, we must be sure our measures look like they are measuring what they are intended to measure (face validity) and adequately cover the domain of interest (content validity). If so, we can assess convergent validity and discriminant validity.

These forms of validity represent how unique or distinct a measure is. Convergent valid- ity requires that concepts that should be related are indeed related. For example, in business we believe customer satisfaction and customer loyalty are related. If we have measures of both, we would expect them to be positively correlated. If we found no significant correlation between our measures of satisfaction and our measures of loyalty, it would bring into question the convergent validity of these measures. On the other hand, our customer satisfaction measure should not cor- relate too highly with the loyalty measure if the two concepts are truly different. If the correlation is too high, we have to ask if we are measuring two different things, or if satisfaction and loyalty are actually one concept. As a rough rule of thumb, when two scales are correlated above 0.75, discriminant validity may be questioned. So, we expect related concepts to display a significant correlation (convergent validity), but not to be so highly correlated that they are not independent concepts (discriminant validity).

Multivariate procedures like factor analysis can be useful in establishing construct validity. The reader is referred to other sources for a more detailed discussion.13

Reliability versus Validity Reliability is a necessary but not sufficient condition for validity. A reliable scale may not be valid. For example, a purchase intention measurement technique may consistently indicate that 20 percent of those sampled are willing to purchase a new product. Whether the measure is valid depends on whether 20 percent of the population indeed purchases the product. A reliable but invalid instrument will yield consistently inaccurate results.

The differences between reliability and validity can be illustrated by the rifle targets in Exhibit 13.7. Suppose an expert sharpshooter fires an equal number of rounds with a century-old rifle and a modern rifle.14 The shots from the older gun are considerably scattered, but those from the newer gun are closely clustered. The variability of the old rifle compared with that of the new one indicates it is less reliable. The target on the right illustrates the concept of a systematic bias influ- encing validity. The new rifle is reliable (because it has little variance), but the sharpshooter’s vision is hampered by glare. Although shots are consistent, the sharpshooter is unable to hit the bull’s-eye.

Sensitivity The sensitivity of a scale is an important measurement concept, particularly when changes in atti- tudes or other hypothetical constructs are under investigation. Sensitivity refers to an instrument’s ability to accurately measure variability in a concept. A dichotomous response category, such as “agree or disagree,” does not allow the recording of subtle attitude changes. A more sensitive

sensitivity

A measurement instrument’s ability to accurately measure variability in stimuli or responses.

convergent validity

Concepts that should be related to one another are in fact related; highly reliable scales contain convergent validity.

discriminant validity

Represents the uniqueness or distinctiveness of a measure; a scale should not correlate too highly with a measure of a different construct.

EXHIBIT 13.7

Reliability and Validity on Target

Old Rifle Low Reliability

(Target A)

New Rifle High Reliability

(Target B)

New Rifle Sunglare Reliable but not Valid

(Target C)

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CHAPTER 13 Measurement and Scaling Concepts • 305

measure with numerous categories on the scale may be needed. For example, adding “strongly agree,” “mildly agree,” “neither agree nor disagree,” “mildly disagree,” and “strongly disagree” will increase the scale’s sensitivity.

The sensitivity of a scale based on a single question or single item can also be increased by adding questions or items. In other words, because composite measures allow for a greater range of possible scores, they are more sensitive than single-item scales. Thus, sensitivity is generally increased by adding more response points or adding scale items.

SUMMARY

1. Determine what needs to be measured to address a research question or hypothe- sis. Researchers can determine what concepts must be measured by examining research questions and hypotheses. A hypothesis often states that one concept is related to another. Therefore, the concepts listed in the hypotheses must have operational measures if the research is to be performed.

2. Distinguish levels of scale measurement. Four levels of scale measurement can be identified. Each level is associated with increasingly more complex properties. Nominal scales assign numbers or letters to objects for identification or classification. Ordinal scales arrange objects based on rela- tive magnitude of a concept. Thus, ordinal scales represent rankings. Interval scales also represent an ordering based on relative amounts of a concept, but they also capture the differences between scale values. Thus, interval scales allow stimuli to be compared to each other based on the dif- ference in their scale scores. Ratio scales are absolute scales, starting with absolute zeros at which there is a total absence of the attribute. Nominal and ordinal scales are discrete. The mode is the best way to represent central tendency for discrete measures. Ratio measures are continuous and interval scales are generally treated as continuous. For continuous measures, the mean represents a valid representation of central tendency.

3. Know how to form an index or composite measure. Indexes and composite measures are formed by combining scores from multiple items. For instance, a composite score can be formed by adding the scores to multiple items, each intended to represent the same concept.

4. List the three criteria for good measurement. Good measurement exists when a measure is reliable, valid, and sensitive. Thus, reliability, validity, and sensitivity are characteristics of good measurement. Reliability represents the consistency and repeatability of a measure. Validity refers to the degree to which the instrument measures the concept the researcher wants to measure. Sensitivity is the instrument’s ability to accurately measure variability in stimuli or responses.

5. Provide a basic assessment of scale reliability and validity. Reliability is most often assessed using coefficient alpha. Coefficient alpha should be at least 0.6 for a scale to be considered as acceptably reliable. Validity is assessed in components. A measure that has adequate construct validity is one that is likely to be well measured. Construct validity consists of face validity, content validity, criterion validity, convergent validity, and discriminant validity. Statistical procedures like factor analysis can be helpful in providing evidence of construct validity.

KEY TERMS AND CONCEPTS attribute, 299 coefficient alpha (a), 302 composite measures, 300 concept, 292 construct validity, 304 construct, 293 content validity, 304 continuous measures, 299 convergent validity, 305 correspondence rules, 293

criterion validity, 304 discrete measures, 298 discriminant validity, 305 face validity, 303 index measures, 299 internal consistency, 302 interval scales, 297 measurement, 289 nominal scales, 293 operationalization, 292

ordinal scales, 295 ratio scales, 297 reliability, 301 reverse coding, 300 scales, 292 sensitivity, 305 split-half method, 302 summated scale, 300 test-retest method, 302 validity, 303

306 • PART FOUR Measurement Concepts

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Define measurement. How is your performance in your

research class being measured? 2. What is the difference between a concept and a construct? 3. Suppose a researcher takes over a project only after a pro-

posal has been written by another researcher. Where will the researcher find the things that need to be measured?

4. Describe, compare, and contrast the four different levels of scale measurement.

5. Consider the different grading measuring scales described at the beginning of the chapter. Describe what level of measurement is represented by each. Which method do you think contains the least opportunity for error?

6. Look at the responses to the following survey items that describe how stressful consumers believed a Christmas shopping trip was using a 10-point scale ranging from 1 (= no stress at all) to 10 (= extremely stressful): a. How stressful was finding a place to park? 7 b. How stressful was the checkout procedure? 5 c. How stressful was trying to find exactly the right product? 8 d. How stressful was finding a store employee? 6

i. What would be the stress score for this respondent based on a summated scale score?

ii. What would be the stress score for this respondent based on an average composite scale score?

iii. Do any items need to be reverse-coded? Why or why not? 7. How is it that business researchers can justify treating a seven-

point Likert scale as interval?

8. What are the components of construct validity? Describe each. 9. Why might a researcher wish to use more than one question to

measure satisfaction with a particular aspect of retail shopping? 10. How can a researcher assess the reliability and validity of a

multi-item composite scale? 11. Comment on the validity and reliability of the following:

a. A respondent’s report of an intention to subscribe to Consumer Reports is highly reliable. A researcher believes this constitutes a valid measurement of dissatisfaction with the economic system and alienation from big business.

b. A general-interest magazine claimed that it was a better advertising medium than television programs with similar content. Research had indicated that for a soft drink and other test products, recall scores were higher for the maga- zine ads than for 30-second commercials.

c. A respondent’s report of frequency of magazine reading consistently indicates that she regularly reads Good House- keeping and Gourmet and never reads Cosmopolitan.

12. Indicate whether the following measures use a nominal, ordinal, interval, or ratio scale: a. Prices on the stock market b. Marital status, classified as “married” or “never married” c. A yes/no question asking whether a respondent has ever

been unemployed d. Professorial rank: assistant professor, associate professor, or

professor e. Grades: A, B, C, D, or F

RESEARCH ACTIVITIES 1. Go to the library and find out how Sales and Marketing

Management magazine constructs its buying-power index. 2. Define each of the following concepts, and then operationally

define each one by providing correspondence rules between the definition and the scale: a. A good bowler b. The television audience for The Tonight Show c. Purchasing intention for an iPhone d. Consumer involvement with cars e. A workaholic f. Outstanding supervisory skills g. A risk-averse investor

3. ’NEt Use the ACSI scores found at http://www.theacsi.org to respond to this question. Using the most recent two years of data, test the following two hypotheses: a. American consumers are more satisfied with breweries than

they are with wireless telephone services.

b. American consumers are more satisfied with discount and department stores than they are with automobile companies.

4. Refer back to the opening vignette. Use a search engine to find stories dealing with job performance. In particular, pay attention to stories that may be related to CRM. Make a recommenda- tion to Griff concerning a way that job performance should be measured. Would your scale be nominal, ordinal, interval, or ratio?

5. ’NEt Go to http://www.queendom.com/tests. Click on the lists of personality tests. Take the hostility test. Do you think this is a reliable and valid measure of how prone someone is to gen- erally act in a hostile manner?

Chapter 13: Measurement and Scaling Concepts 307

FlyAway Airways Wesley Shocker, research analyst for FlyAway Airways, was asked by the director of research to make recommendations regarding the best approach

for monitoring the quality of service provided by the airline.15 FlyA- way Airways is a national air carrier that has a comprehensive route structure consisting of long-haul, coast-to-coast routes and direct, nonstop routes between short-haul metropolitan areas. Current competitors include Midway and Alaska Airlines. FlyAway Airlines is poised to surpass the billion-dollar revenue level required to be designated as a major airline. This change in status brings a new set of competitors. To prepare for this move up in competitive status, Shocker was asked to review the options available for monitor- ing the quality of FlyAway Airways service and the service of its competitors. Such monitoring would involve better understanding the nature of service quality and the ways in which quality can be tracked for airlines.

After some investigation, Shocker discovered two basic approaches to measuring quality of airline service that can produce similar rank- ing results. His report must outline the important aspects to consider in measuring quality as well as the critical points of difference and similarity between the two approaches to measuring quality.

Some Background on Quality In today’s competitive airline industry, it’s crucial that an airline do all it can do to attract and retain customers. One of the best ways to do this is by offering quality service to consumers. Perceptions of service quality vary from person to person, but an enduring element of service quality is the consistent achievement of customer satisfac- tion. For customers to perceive an airline as offering quality service, they must be satisfied, and that usually means receiving a service outcome that is equal to or greater than what they expected.

An airline consumer usually is concerned most with issues of schedule, destination, and price when choosing an airline. Given that most airlines have competition in each of these areas, other factors that relate to quality become important to the customer when mak- ing a choice between airlines. Both subjective aspects of quality (that is, food, pleasant employees, and so forth) and objective aspects (that is, on-time performance, safety, lost baggage, and so forth) have real meaning to consumers. These secondary factors may not be as criti- cal as schedule, destination, and price, but they do affect quality judg- ments of the customer.

There are many possible combinations of subjective and objec- tive aspects that could influence a customer’s perception of quality at different times. Fortunately, since 1988, consumers of airline services have had access to objective information from the Department of Transportation regarding service performance in some basic catego- ries. Unfortunately, the average consumer is most likely unaware of or uninterested in these data on performance; instead, consumers rely on personal experience and subjective opinion to judge quality of service. Periodic surveys of subjective consumer opinion regarding airline service experience are available through several sources. These efforts rely on contact with a sample of consumers who may or may not have informed opinions regarding the quality of airline service for all airlines being compared.

a Consumer Survey approach In his research, Shocker discovered a recent study conducted to identify favorite airlines of frequent fliers. This study is typical of the survey-based, infrequent (usually only annually), subjective efforts conducted to assess airline quality. A New York firm, Research & Forecasts, Inc., published results of a consumer survey of frequent fliers that used several criteria to rate domestic and international airlines. Criteria included comfort, service, reliability, food quality, cost, delays, routes served, safety, and frequent-flier plans. The ques- tionnaire was sent to 25,000 frequent fliers.

The 4,462 people who responded were characterized as pre- dominantly male (59 percent) professional managers (66 percent) whose average age was 45 and who traveled an average of at least 43 nights a year for both business and pleasure. This group indi- cated that the most important factors in choosing an airline were (1) route structure (46 percent), (2) price (42 percent), (3) reliability (41 percent), (4) service (33 percent), (5) safety (33 percent), (6) frequent-flier plans (33 percent), and (7) food (12 percent). When asked to rate 20 different airlines, respondents provided the rankings in Case Exhibit 13.1-1.

a Weighted average approach Shocker also discovered a newer, more objective approach to mea- suring airline quality in a study recently published by the National Institute for Aviation Research at the Wichita State University in Wichita, Kansas. The Airline Quality Rating (AQR) is a weighted average of 19 factors that have relevance when judging the quality of airline services (see Case Exhibit 13.1-2). The AQR is based on data that are readily obtainable (most of the data are updated monthly) from published sources for each major airline operating in the United States. Regularly published data on such factors as consumer complaints, on-time performance, accidents, number of aircraft, and financial performance are available from the Department of Trans- portation, the National Transportation Safety Board, Moody’s Bond Record, industry trade publications, and annual reports of individual airlines.

CASE 13.1

CASE EXHIBIT 13.1-1

Ranking of Major Airlines: Consumer Survey Approach

1. American 2. United 3. Delta 4. TWA 5. SwissAir 6. Singapore 7. British Airways 8. Continental 9. Air France

10. Pan Am

11. Lufthansa 12. USAir 13. KLM 14. America West 15. JAL 16. Alaska 17. Qantas 18. Midway 19. Southwest 20. SAS

(Continued )

308 Part 4: Measurement Concepts

To establish the 19 weighted factors, an opinion survey was con- ducted with a group of 65 experts in the aviation field. These experts included representatives of most major airlines, air travel experts, Federal Aviation Administration (FAA) representatives, academic researchers, airline manufacturing and support firms, and individual consumers. Each expert was asked to rate the importance that each individual factor might have to a consumer of airline services using a scale of 0 (no importance) to 10 (great importance). The average

importance ratings for each of the 19 factors were then used as the weights for those factors in the AQR. Case Exhibit 13.1-2 shows the factors included in the Airline Quality Rating, the weight associated with each factor, and whether the factor has a positive or negative impact on quality from the consumer’s perspective.

Using the AQR formula and recent data, rankings for the 10 major U.S. airlines shown in Case Exhibit 13.1-3.

What Course to Chart? Shocker has discovered what appear to be two different approaches to measuring quality of airlines. One relies on direct consumer opinion and is mostly subjective in its approach to quality and the elements considered. The other relies on performance data that are available through public sources and appear to be more objective. Both approaches incorporate pertinent elements that could be used by consumers to judge the quality of an airline. Shocker’s recom- mendation must consider the comprehensiveness and usefulness of these approaches for FlyAway Airways as it moves into a more com- petitive environment. What course of action should he recommend?

Questions 1. How comparable are the two different methods? In what ways

are they similar? In what ways are they different? 2. What are the positive and negative aspects of each approach

that Shocker should consider before recommending a course of action for FlyAway Airways?

3. What aspects of service quality does each approach address well and not so well?

4. Considering the two methods outlined, what types of validity would you consider to be demonstrated by the two approaches to measuring quality? Defend your position.

5. Which of the methods should Shocker recommend? Why?

CASE EXHIBIT 13.1-2

Factors Included in the Airline Quality Rating (AQR)a

Factor Weight

1. Average age of fleet 25.85

2. Number of aircraft 14.54

3. On-time performance 18.63

4. Load factor 26.98

5. Pilot deviations 28.03

6. Number of accidents 28.38

7. Frequent-flier awards 27.35

8. Flight problemsb 28.05

9. Denied boardingsb 28.03

10. Mishandled baggageb 27.92

11. Faresb 27.60

12. Customer serviceb 27.20

13. Refunds 27.32

14. Ticketing/boardingb –7.08

15. Advertisingb –6.82

16. Creditb 25.94

17. Otherb 27.34

18. Financial stability 26.52

19. Average seat-mile cost 24.49

AQR = w

1 F

1 − w

2 F

2 + w

3 F

3 + … − w

19 F

w 1 + w

2 + w

3 + … + w

a. The 19-item rating has a reliability coefficient (Cronbach’s Alpha) of 0.87. b. Data for these factors come from consumer complaints registered with the Department

of Transportation.

CASE EXHIBIT 13.1-3

Airline Rankings

Rank Airline AQR Score

1 American 10.328

2 Southwest 10.254

3 Delta 10.209

4 United 10.119

5 USAir 10.054

6 Pan Am 10.003

7 Northwest 20.063

8 Continental 20.346

9 America West 20.377

10 TWA 20.439

Case 13.1 (Continued )

Chapter 13: Measurement and Scaling Concepts 309

310

After studying this chapter, you should be able to

1. Describe how business researchers think of attitudes

2. Identify basic approaches to measuring attitudes

3. Discuss the use of rating scales for measuring attitudes

4. Represent a latent construct by constructing a summated scale

5. Summarize ways to measure attitudes with ranking and sorting techniques

6. Discuss major issues involved in the selection of a measurement scale

C H A P T E R

T he history of steel factories and the challenges of the furnace workers who work in those fac- tories is well documented. While many people are familiar with the stories of the great steel

industrialists such as Carnegie and Frick of the nineteenth century, few people realize that the job of the furnace worker continues to this day. It is hard and difficult work—despite the great advances in technology and an ever-increasing focus on safety, furnace workers still face dangerous work environments filled with heat and smoke. The molten metal must be carefully managed within the furnace, with temperatures in the vessel exceeding thousands of degrees. The possibility of critical injury or death is ever-present, either through the long-term exposure to metallic fumes or from the immediate effects of a furnace explosion.

A company that specializes in making high-grade metals knew how important these furnace workers were to their suc- cess, and was keenly interested in what kept them satisfied with their company. Several of their furnace employees had been with them for over 20 years, and the skills and expertise of these experienced employees were invaluable to the train- ing of new furnace workers, and the manufacturing process itself.

A team of business researchers was asked to do an

assessment of furnace employee attitudes, with the goal of identifying what aspects of their work environment contributed to their overall satisfaction. Using a survey questionnaire, a series of statements related to the company’s benefits, super- visory relationships, and general work-related conditions was developed. These researchers asked the furnace workers to indicate their level of agreement, on a scale which ranged from

LEARNING OUTCOMES

Attitude Measurement

Chapter Vignette:

Heat and Smoke—What Keeps Them Happy?

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Introduction For social scientists, an attitude is as an enduring disposition to respond consistently to spe cific aspects of the world, including actions, people, or objects. One way to understand an attitude is to break it down into its components. Consider this brief statement: “Sally believes Walmart is clean, conveniently located, and has low prices. She likes Walmart and enjoys shopping there. She intends to shop there every Thursday.” This simple example demonstrates attitude’s three components: cognitive, affective, and behavioral. A person’s attitudinal feelings are typically driven directly by his/her beliefs or cognitions. This cognitive component represents an indi- vidual’s knowledge about attributes and their consequences. One person might feel happy about the purchase of an automobile because she believes the car “gets great gas mileage” or knows that the dealer is “the best in Peoria.” The affective component refers to an individual’s general feelings or emotions toward an object. Statements such as “I really like my Corvette,” “I enjoy reading Harry Potter books,” and “I hate cranberry juice” reflect an emotional character of atti- tudes. The behavioral component of an attitude reflects a predisposition to action by reflecting an individual’s intentions.

Attitudes as Hypothetical Constructs Business researchers often pose questions involving psychological variables that cannot directly be observed. For example, someone may have an attitude toward working on a commission basis. We cannot actually see this attitude. Rather, we can measure an attitude by making an inference based on the way an individual responds to multiple scale indicators. Because we can’t directly see these phenomena, they are known as latent constructs (discussed in Chapter 3), hypothetical constructs, or just simply constructs. Common constructs include job satisfaction, organizational commitment, personal values, feelings, role stress, perceived value, and many more. The Research Snapshot “Is It Positive Emotionality, or Is It LOVE?” talks about measuring love. Is love a latent construct?

attitude

An enduring disposition to consistently respond in a given manner to various aspects of the world, composed of affective, cognitive, and behavioral components.

hypothetical constructs

Variables that are not directly observable but are measurable through indirect indicators, such as verbal expression or overt behavior.

strongly disagree to strongly agree, to these statements. Exam- ples of these statements included:

1. Our company has a health plan that addresses the needs of my family.

2. My experiences with the health plan coordinator have been good.

3. My supervisor sees me as an asset to the company. 4. My supervisor encourages me to contribute ideas that

can make our work space better. 5. My company puts safety as a top priority.

For each of the statements, the researchers compiled responses from the furnace workers to see if these areas were positively related to the overall work satisfaction of the furnace employees. Results showed some interesting outcomes. While the furnace workers viewed company health benefits and the company’s safety program as important to overall work satisfac- tion, the opportunity to have a supervisor who they perceived

as valuing their input and who saw them as important assets to the company was a very important factor related to their satisfaction.

In a nutshell, the very dangerous work environment of the furnace floor did require a focus on safety and health benefits in their minds. But not unlike workers in safer environments, it was the positive and supportive relation- ship with their immediate supervisor that really made the difference.

Measurement of attitudes is a common objective in busi- ness research. Just as a company can apply this study’s results to craft practical responses—for example, hiring and training supervisors who are oriented towards positively supporting their employees—other business researchers explore attitudes to answer questions that range from identifying needs to evaluating satisfaction with other aspects of a company’s work processes. This chapter describes various methods of attitude measurement.

CHAPTER 14 Attitude Measurement • 311

312

Importance of Measuring Attitudes Most managers hold the intuitive belief that changing consumers’ or employees’ attitudes toward their company or their company’s products or services is a major goal. Because modi- fying attitudes plays a pervasive role in developing strategies to address these goals, the mea- surement of attitudes is an important task. For example, after Whiskas cat food had been sold in Europe for decades, the brand faced increased competition from new premium brands, and consumers had difficulty identifying with the brand. The company conducted attitude research to determine how people felt about their cats and their food alternatives. The study revealed that cat owners see their pets both as independent and as dependent fragile beings.1 Cat owners held the attitude that cats wanted to enjoy their food but needed nutrition. This attitude research was directly channeled into managerial action. Whiskas marketers begin positioning the product as having “Catisfaction,” using advertisements that featured a purring

silver tabby—a pedigreed cat—which symbolizes premium quality but also presents the image of a sweet cat. The message: “Give cats what they like with the nutrition they need. If you do, they’ll be so happy that they’ll purr for you.” This effort reversed the sales decline the brand had been experiencing.

Techniques for Measuring Attitudes A remarkable variety of techniques has been devised to measure atti- tudes. This variety stems in part from lack of consensus about the exact definition of the concept. In addition, the cognitive, affective, and behav- ioral components of an attitude may be measured by different means. For example, sympathetic nervous system responses may be recorded using physiological measures to quantify affect, but they are not good measures of behavioral intentions. Direct verbal statements concerning belief, affect, or behavior are used to measure behavioral intent. How- ever, attitudes may also be interpreted using qualitative techniques like those discussed in Chapter 7.

While cats’ attitudes may be difficult to measure, we can easily measure how consumers feel about different types of cat food.

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THIS! This chapter focuses on different ways to assess respondent attitudes. One popular way is to use a multiattribute model. The process begins by asking respondents in one way or another to evaluate the attributes that help form an attitude toward the activ- ity involved. In our survey, we assess attitudes toward working in a specific business career (marketing, manage- ment, finance, or accounting). Each respondent’s attribute evaluation is multiplied by the corresponding belief about whether or not the particular activity is associated with the attri- bute. This process is described in the chapter. After reading the chapter, see if you can compute respondents’ attitudes toward working in a business career (just consider each of the four disciplines a business career). Later, we’ll actually revisit these attitude scores to see which business area is trly preferred.

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Research may assess the affective (emotional) components of attitudes through physiological measures such as galvanic skin response (GSR), blood pressure, and pupil dilation. These measures provide a means of assessing attitudes without verbally questioning the respondent. In general, they can provide a gross measure of likes or dislikes, but they are not extremely sensitive to the different gradients of an attitude.

Obtaining verbal statements from respondents generally requires that the respondents perform a task such as ranking, rating, sorting, or making choices. A ranking task requires the respondent to rank order a small number of stores, brands, feelings, or objects on the basis of overall preference or some characteristic of the stimulus. Rating asks the respondent to estimate the magnitude or the extent to which some characteristic exists. A quantitative score results. The rating task involves marking a response indicating one’s position using one or more cognitive or attitudinal scales. A sorting task might present the respondent with several different concepts printed on cards and require the respondent to classify the concepts by placing the cards into groups (stacks of cards). Another type of attitude measurement is choice between two or more alternatives. If a respondent chooses one object over another, the researcher assumes that the respondent prefers the chosen object, at least in this setting. The following sections describe the most popular techniques for measuring attitudes.

Attitude Rating Scales Perhaps the most common practice in business research is using rating scales to measure attitudes. This section discusses many rating scales designed to enable respondents to report the intensity of their attitudes.

Simple Attitude Scales In its most basic form, attitude scaling requires that an individual agree or disagree with a statement or respond to a single question. For example, respondents in a political poll may be asked whether they agree or disagree with the statement, “The president should run for re-election.” Or, an individ- ual might indicate whether he or she likes or dislikes jalapeño bean dip. This type of self-rating scale

ranking

A measurement task that requires respondents to rank order a small number of stores, brands, or objects on the basis of overall preference or some characteristic of the stimulus.

rating

A measurement task that requires respondents to estimate the magnitude of a characteristic or quality that a brand, store, or object possesses.

sorting

A measurement task that presents a respondent with several objects or product concepts and requires the respondent to arrange the objects into piles or classify the product concepts.

choice

A measurement task that identifies preferences by requiring respondents to choose between two or more alternatives.

RESEARCH SNAPSHOT

Love is a four-letter word. Or is it more than that? Psycholo- gists and cognitive scientists view love as just one example of positive emotionality, and have developed numerous defini- tions to describe what love is and how it works. In fact, a recent study found there are nine different ways love can be defined and/or measured! The concept of love is a hypo- thetical construct—that is, a term that psychologists use to describe or explain a particular pattern of human behavior. Love, hate, thirst, learning, intelligence—all of these are hypothetical constructs. They are hypothetical in that they do not exist as physical entities; therefore, they cannot be seen, heard, felt, or measured directly. There is no love center in the brain that, if removed, would leave a person incapable of responding positively and affectionately toward other people and things. Love and hate are constructs in that we invent

these terms to explain why, for instance, a young man spends all his time with one young woman while completely avoiding another. From a scientific point of view, we might be bet- ter off if we said that this young man’s behavior suggested that he had a relatively enduring, positive-approach attitude toward the first woman and a negative avoidance attitude toward the second!

Source: Based on Myers, Jane and Matthew Shurts, “Measuring Positive Emotionality: A Review of Instruments Assessing Love,” Measurement and Evaluation in Counseling and Development 34 (2002), 238–254.

Is It Positive Emotionality, or Is It LOVE?

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merely classifies respondents into one of two categories, thus having only the properties of a nominal scale, and the types of mathematical analysis that may be used with this basic scale are limited.

Despite the disadvantages, simple attitude scaling may be used when questionnaires are extremely long, when respondents have little education, or for other specific reasons. A number of simplified scales are merely checklists: a respondent indicates past experience, preference, and the like merely by checking an item. In many cases the items are adjectives that describe a particular object. In a survey of small-business owners and managers, respondents indicated whether they found working in a small firm more rewarding than working in a large firm, as well as whether they agreed with a series of attitude statements about small businesses. For example, 77 percent said small and midsized businesses “have less bureaucracy,” and 76 percent said smaller companies “have more flexibility” than large ones.2

Most attitude theorists believe that attitudes vary along continua. Early attitude researchers pioneered the view that the task of attitude scaling is to measure the distance from “bad” to “good,” “low” to “high,” “dislike” to “like,” and so on. Thus, the purpose of an attitude scale is to find an individual’s position on the continuum. However, simple scales do not allow for fine distinctions between attitudes. Several other scales have been developed for making more precise measurements.

Category Scales The simplest rating scale described earlier contains only two response categories, such as yes/no or agree/disagree. Expanding the response categories provides the respondent with more flexibility in the rating task and the ability to more precisely indicate his/her attitude. Even more information is provided if the categories are ordered according to a particular descriptive or evaluative dimension. Consider the following question:

How often do you disagree with your spouse about how much to spend on vacation?

Never Rarely Sometimes Often Very often

□ □ □ □ □

Businesses today face an increasing need to be perceived as having an interest in social responsibility. In many instances, products and services have been promoted based upon the fact that the product or service is environmentally friendly, or has a tie to improving the social environment. Companies such as Toyota (with an emphasis on hybrid vehicles) and Yoplait (with its contributions to breast cancer research) highlight a trend that showing interest in improving the world can also have bot- tom line implications. The Alloy Eighth Annual College Explorer study, conducted with the assistance of the Harris Group, recently surveyed 1,554 college students to determine their opinions about corporate social responsibility. Results indicate that 41 percent of college students consciously prefer products and services from companies they perceive as having a social role. Large companies such as Toyota and smaller companies

like Burt’s Bees were ranked as highly socially responsible brands.

The implications for business leaders are quite interesting. Increasingly, perceptions of the company itself, and not just its products, drive purchasing decisions among this important demographic.

Source: Based on Bush, Michael, “Students Rank Social Responsibility,” Advertising Age 79 (August 4, 2008), 11.

Students Ask—Are You Responsible?

RESEARCH SNAPSHOT

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This category scale is a more sensitive measure than a scale that has only two response catego- ries. By having more choices for a respondent, the potential exists to provide more information. Compare the question above to one that asks the respondent, “Do you disagree with your spouse about how much to spend on a vacation?” and offers only yes/no as possible responses. The ben- efits of additional points in the measurement scale should be obvious. However, if the researcher tries to represent something that is truly bipolar or dichotomous (yes/no, female/male, member/ nonmember, and so on) with more than two categories, error may be introduced.

Question wording is an extremely important factor in the usefulness of these scales. Exhibit 14.1 shows some common wordings used in category scales. The issue of question wording is discussed in Chapter 15.

Method of Summated Ratings: The Likert Scale A method that is simple to administer and therefore extremely popular is business researchers’ adap- tation of the method of summated ratings, developed by Rensis Likert (pronounced “Lick-urt”).3

category scale

A rating scale that consists of several response categories, often providing respondents with alternatives to indicate positions on a continuum.

Quality

Poor Fair Good Excellent

Not good at all Not very good Neither good nor bad Fairly good Very good

Well below average Below average Average Above average Well above average

Importance

Not at all important Not so important Neutral Fairly important Very important

Interest

Not very interested Somewhat interested Very interested

Satisfaction

Completely dissatisfied Somewhat dissatisfied Neither satisfied nor dissatisfied

Somewhat satisfied Completely satisfied

Not at all satisfied Somewhat satisfied Quite satisfied Very satisfied

Frequency

Hardly ever Sometimes Often Very often All of the time

Never Rarely Sometimes Often Very often

Just now and then Some of the time Most of the time All of the time

Truth

Not at all true Not very true Somewhat true Very true

Definitely no Probably no Probably yes Definitely yes

Uniqueness

Not at all different Slightly different Somewhat different Very different

Not at all unique Slightly unique Somewhat unique Very unique Extremely unique

EXHIBIT 14.1

Selected Category Scales

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CHAPTER 14 Attitude Measurement • 315

With the Likert scale, respondents indicate their attitudes by checking how strongly they agree or disagree with carefully constructed statements, ranging from very negative to very positive attitudes toward some object. Individuals generally choose from approximately five response alternatives—strongly disagree, disagree, uncertain, agree, and strongly agree—although the number of alternatives may range from three to ten or more. In the following example, from a study of food-shopping behavior, there are five alternatives:

I really enjoy my business research class.

Strongly Disagree Disagree Uncertain Agree Strongly Agree

□ □ □ □ □

(1) (2) (3) (4) (5)

Researchers assign scores, or weights, to each possible response. In this example, numerical scores of 1, 2, 3, 4, and 5 are assigned to each level of agreement, respectively. The numerical scores, shown in parentheses, are typically not printed on the questionnaire or computer screen. Strong agreement indicates the most favorable attitude on the statement, and a numerical score of 5 is assigned to this response.

Reverse Coding The statement given in this example (“I really enjoy my business research class”) is positively framed. A student that loves their research class would choose “strongly agree,” with a scale value of 5. If a second statement used to measure attitude about your business research class is framed negatively (such as “My business research class is my least favorite class”), the same student would choose “strongly disagree,” with a scale value of 1. To make these questions consistent, the numeri- cal scores would need to be reversed. This is done by reverse coding the negative item so that a strong agreement really indicates an unfavorable response rather than a favorable attitude, and vice- versa. In the case of a five-point scale, the recoding is done as follows:

Old Value New Value

1 5

2 4

3 3

4 2

5 1

Recoding in this fashion turns agreement with a negatively worded item into a mirror image, meaning the result is the same as disagreement with a positively worded item. SPSS has a recode function that allows simple recoding to be done by entering “old” and “new” scale values. Alterna- tively, a simple mathematical formula can be entered. For a typical 1–5 scale, the formula

X new value

= 6 − X old value

would result in the same recoding.

Composite Scales A Likert scale may include several scale items to form a composite scale. Each statement is assumed to represent an aspect of a common attitudinal domain. For example, Exhibit 14.2 shows the items in a Likert scale for measuring attitudes toward patients’ interaction with a physician’s service staff. The total score is the summation of the numerical scores assigned to an individual’s responses.

Likert scale

A measure of attitudes designed to allow respondents

to rate how strongly they agree or disagree with carefully

constructed statements, ranging from very positive to

very negative attitudes toward some object.

reverse coding

A method of making sure all the items forming a

composite scale are scored in the same direction. Negative

items can be recoded into the equivalent responses for a

non-reverse-coded item.

composite scale

A way of representing a latent construct by summing

or averaging respondents’ reactions to multiple items

each assumed to indicate the latent construct.

316 • PART FOUR Measurement Concepts

EXHIBIT 14.2

Likert Scale Items for Measuring Attitudes toward Patients’ Interaction with a Physician’s Service Staff

1. My doctor’s office staff takes a warm and personal interest in me. 2. My doctor’s office staff is friendly and courteous. 3. My doctor’s office staff is more interested in serving the doctor’s needs than in serving my

needs. 4. My doctor’s office staff always acts in a professional manner.

Source: Brown, S. W., Swartz, T. A. (1989), “A gap analysis of professional services quality,” Journal of Marketing, Vol. 54, pp. 92–8. Copyright 1989 by Am. Marketing Assn (AMA (Chic)). Reproduced with permission of Am. Marketing Assn (AMA (Chic)) in the format Textbook via Copyright Clearance Center.

EXHIBIT 14.3

Semantic Differential Scales for Measuring Attitudes toward Supermarkets

Inconvenient location __ __ __ __ __ __ __ Convenient location

Low prices __ __ __ __ __ __ __ High prices

Pleasant atmosphere __ __ __ __ __ __ __ Unpleasant atmosphere

Modern __ __ __ __ __ __ __ Old-fashioned

Cluttered __ __ __ __ __ __ __ Spacious

Fast checkout __ __ __ __ __ __ __ Slow checkout

Dull __ __ __ __ __ __ __ Exciting

Source: Yu, Julie H., Gerald Albaum, and Michael Swenson (2003), “Is a Central Tendency Error Inherent in the Use of Semantic Differential Scales in Different Cultures?” International Journal of Market Research, 45 (Quarter 2) 213–228.

Note that item 3 is negatively worded and therefore it must be reverse-coded prior to being used to create the composite scale. In other words, a person that felt extremely positively about their doctor’s staff would indicate “strongly agree” with items 1, 2, and 4, and “strongly disagree” with item 3. If we use the typical 5-point “strongly disagree” to “strongly agree” scale, the possible scores for each respondent’s composite would range from 4 (extremely negative) to 20 (extremely posi- tive) after recoding.

In Likert’s original procedure, a large number of statements are generated, and an item analysis is performed. The purpose of the item analysis is to ensure that the final items evoke a wide response and discriminate among those with negative and positive attitudes. Items that are poor because they lack clarity or elicit mixed response patterns are eliminated from the final statement list. Scales that use multiple items can be analyzed for reliability and validity. Only a set of items that demonstrates good reliability and validity should be summed or aver- aged to form a composite scale representing a hypothetical construct. Unfortunately, not all researchers are willing or able to thoroughly assess reliability and validity. Without this test, the use of Likert scales can be disadvantageous because there is no way of knowing exactly what the items represent or how well they represent anything of interest. Without valid and reliable measures, researchers cannot guarantee they are measuring what they say they are measuring.

Semantic Differential The semantic differential is actually a series of attitude scales. This popular attitude measure- ment technique consists of getting respondents to react to some concept using a series of seven- point bipolar rating scales. Bipolar adjectives—such as “terrible” and “excellent,” “cramped” and “spacious,” or “dirty” and “clean”—anchor the beginning and the end (or poles) of the scale. The subject makes repeated judgments about the concept under investigation on each of the scales. Exhibit 14.3 shows 7 of 18 scales used in a research project that measured attitudes toward supermarkets.

semantic differential

A measure of attitudes that consists of a series of seven- point rating scales that use bipolar adjectives to anchor the beginning and end of each scale.

CHAPTER 14 Attitude Measurement • 317

The scoring of the semantic differential can be illustrated using the scale bounded by the anchors “modern” and “old-fashioned.” Respondents are instructed to check the place that indi- cates the nearest appropriate adjective. While only the anchors are included on the survey, the scale intervals are assumed to be interpreted as something like “extremely modern,” “very mod- ern,” “slightly modern,” “both modern and old-fashioned,” “slightly old-fashioned,” “very old- fashioned,” and “extremely old-fashioned”:

Modern __ __ __ __ __ __ __ Old-fashioned

The semantic differential technique originally was developed as a method for measuring the meanings of objects or the “semantic space” of interpersonal experience.4 Researchers have found the semantic differential versatile and useful in business applications. However, the validity of the semantic differential depends on finding scale anchors that are semantic opposites, which can sometimes prove difficult. In attitude or image studies simple anchors such as very unfavorable and very favorable work well.

For scoring purposes, a numerical score is assigned to each position on the rating scale. Tradi- tionally, score ranges such as 1, 2, 3, 4, 5, 6, 7 or −3, −2, −1, 0, +1, +2, +3 are used. Many business researchers find it desirable to assume that the semantic differential provides interval data. This assumption, although widely accepted, has its critics, who argue that the data have only ordinal properties because the numerical scores are arbitrary. Practically speaking, most researchers will treat semantic differential scales as metric (at least interval). This is because the amount of error introduced by assuming the intervals between choices are equal (even though this is uncertain) is fairly small.

Exhibit 14.4 illustrates a typical image profile based on semantic differential data. Because the data are assumed to be interval, either the arithmetic mean or the median will be used to compare the profile of one company, unit, product, brand, store, or so forth with that of another.

image profile

A graphic representation of semantic differential

data for competing brands, products, or stores to highlight

comparisons.

EXHIBIT 14.4

Image Profile of Private versus Public High Schools

Source: Andrewski, Lauren, Ashley Carrasquillo, Taylor Kalhammer, Lindsey Sansone, and Tara Vornkahl, “Perceptions of Private and Public High Schools in Peoria,” Project for Bradley University Marketing Research Class, 2011.

Limited Technology

High Student: Teacher Ratio

Limited AP Courses

Limited Variety of Classes

Low Graduation Rate

Low College Placement

Few Athletics Offered

Few Opportunities to Participate

Poor Athletics Facilities

Dangerous Environment

Extensive Technology

Low Student: Teacher Ratio

Many AP Courses

Extensive Variety of Classes

High Graduation rate

High College Placement

Many Athletics Offered

Many Opportunities to Participate

Excellent Athletics Facilities

Safe Environment

Negative

Private Schools Public Schools

Neutral Positive

318 • PART FOUR Measurement Concepts

319

numerical scale

An attitude rating scale similar to a semantic differential except that it uses numbers, instead of verbal descriptions, as response options to identify response positions.

RESEARCH SNAPSHOT

How good is a website? There are a lot of factors that go into answering that question, but one of the most important is the usability of the site. There are a variety of ways to assess website usability. From an objective perspective, the time required by research subjects to complete a task can be recorded. However, subjective evaluations—a respondent’s perception of the website’s usability—can be as or more effective. One study approached this by comparing five different questionnaires developed to assess the respondent’s perceptions of the usability of sites.

The different questionnaires use several of the attitude measurement scales discussed in this chapter. The first, System Usability Scale (SUS), uses 10 different statements, each rated on a five-point Likert scale from “Strongly Disagree” to “Strongly Agree.” The second scale is the Questionnaire for User Interface Satisfaction (QUIS), which consists of 27 ques- tions rated on a 10-point semantic differential scale with bipolar adjectives such as “Terrible” and “Wonderful.” The third approach is the Computer System Usability Question- naire (CSUQ) developed at IBM. The CSUQ has 19 questions rated on a seven-point Likert scale. The fourth scale is derived from a series of 118 words used by Microsoft on product reac- tion cards. The respondent places a check by each word they feel describes their interaction with the website well. The final

approach is a questionnaire comprised of nine statements eval- uated on a scale, ranging from −3 to +3, which is somewhat of a combination of a Likert statement and a Stapel scale.

Respondents were asked to perform two different tasks on two financial websites, http://finance.yahoo.com and http:// kiplinger.com. After interacting with the website, each respondent responded to one of the usability scales. The results of the study indicate that the different approaches appear to be quite consis- tent in their ability to assess website usability. While only two web- sites were evaluated, all five approaches found that one of the websites was viewed as significantly more useable than the other. Thus, while a wide variety of scales measuring attitudes have been developed, it appears that multiple approaches can come to the same conclusion, at least as far as website usability is concerned.

Source: Based on Thomas S. Tullis and Jacqueline N. Stetson, “A Compari- son of Questionnaires for Assessing Website Usability,” presented at the Usability Professionals Association (UPA)2004 Conference, Minneapolis, MN, June 7–11, 2004, http://home.comcast.net/~tomtullis/ publications/ UPA2004TullisStetson.pdf, accessed July 12, 2011.

Measuring Website Usability

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Numerical Scales A numerical scale simply provides numbers rather than a semantic space or verbal descriptions to identify response options or categories (response positions). For example, a scale using five response positions is called a five-point numerical scale. A six-point scale has six positions and a seven-point scale seven positions, and so on. Consider the following numerical scale:

Now that you’ve had your automobile for about one year, please tell us how satisfied you are with your Volkswagen Jetta TDI.

Extremely Dissatisfied 1 2 3 4 5 6 7 Extremely Satisfied

This numerical scale uses bipolar adjectives in the same manner as the semantic differential, but replaces the blanks with numbers.

In practice, researchers have found that a scale with numerical labels for intermediate points on the scale is as effective a measure as the true semantic differential. The Research Snapshot “ Measuring Website Usability” demonstrates that a variety of scales can be helpful in assessing website usability.

Stapel Scale The Stapel scale, named after Jan Stapel, was originally developed in the 1950s to measure simul- taneously the direction and intensity of an attitude. Modern versions of the scale, with a single adjective, are used as a substitute for the semantic differential when it is difficult to create pairs of bipolar adjectives. The modified Stapel scale places a single adjective in the center of an even number of numerical values (ranging, perhaps, from +3 to −3). The scale measures how close to or distant from the adjective a given stimulus is perceived to be. Exhibit 14.5 illustrates a Stapel scale item that could be used to measure a website’s usability.

Stapel scale

A measure of attitudes that consists of a single adjective in the center of an even number of numerical values.

The advantages and disadvantages of the Stapel scale are very similar to those of the semantic differential. However, the Stapel scale is markedly easier to administer, especially over the tele- phone. Because the Stapel scale does not require bipolar adjectives, it is easier to construct than the semantic differential. Research comparing the semantic differential with the Stapel scale indicates that results from the two techniques are largely the same.5

Constant-Sum Scale A constant-sum scale requires respondents to divide a fixed number of points among several attri- butes corresponding to their relative importance or weight. Suppose United Parcel Service (UPS) wishes to determine the importance of the attributes of accurate invoicing, delivery as promised, and price to organizations that use its service in business-to-business settings. Respondents might be asked to divide a constant sum of 100 points to indicate the relative importance of those attributes:

Divide 100 points among the following characteristics of a delivery service according to how important each characteristic is to you when selecting a delivery company. ____ Accurate invoicing ____ Package not damaged ____ Delivery as promised ____ Lower price 100 points

The constant-sum scale works best with respondents who have high educational levels. If respon- dents follow the instructions correctly, the results will approximate interval measures. As the num- ber of stimuli increases, this technique becomes increasingly complex.

This technique may be used for measuring brand preference. The approach, which is similar to the paired-comparison method (discussed later), is as follows:

Divide 100 points among the following brands according to your preference for each brand. ____ BMW ____ Cadillac ____ Lexus 100 points

In this case, the constant-sum scale is a rating technique. However, with minor modifications, it can be classified as a sorting technique. Although the constant-sum scale is widely used, strictly speak- ing, the scale is flawed because the last response is completely determined by the way the respon- dent has scored the other choices. While with only three brands this is not a serious issue as the respondent can make adjustments, we can see that if a large number of brands were included the constant-sum scale becomes a rather challenging mental task. Although this is probably somewhat complex to understand, the fact is that practical reasons often outweigh this concern.

EXHIBIT 14.5

A Stapel Scale for Measuring a Website’s Usability

−3

−2

−1

Easy to Navigate

Please select a plus number for words or terms that you think describe this website accurately. Select a minus number for words or terms that you think do not describe this website accurately. You can choose any number from +3, for words you think are very accurate, to -3,for words you think are very inaccurate.

constant-sum scale

A measure of attitudes in which respondents are asked

to divide a constant sum to indicate the relative importance

of attributes; respondents often sort cards, but the task

may also be a rating task.

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320 • PART FOUR Measurement Concepts

Graphic Rating Scales A graphic rating scale presents respondents with a graphic continuum. The respondents are allowed to choose any point on the continuum to indicate their attitude. Exhibit 14.6 shows a traditional graphic scale, ranging from one extreme position to the opposite position. Typically a respondent’s score is determined by measuring the length (in millimeters) from one end of the graphic continuum to the point marked by the respondent. Web surveys can also use a graphic rating scale by asking the respondent to click on the line or drag and drop a line across the screen. Many researchers believe that scoring in this manner strengthens the assumption that graphic rating scales of this type are interval scales. Alternatively, the researcher may divide the line into predetermined scoring categories (lengths) and record respondents’ marks accordingly. In other words, the graphic rating scale has the advantage of allowing the researcher to choose any interval desired for scoring purposes. The disad- vantage of the graphic rating scale is that there are no standard answers.

graphic rating scale

A measure of attitudes that allows respondents to rate an object by choosing any point along a graphic continuum.

EXHIBIT 14.6

Graphic Rating Scale Please evaluate each attribute in terms of how important it is to you by placing an X at the position on the horizontal line that most reflects your feelings.

Seating comfort Not important _____________________ Very important

In-flight meals Not important _____________________ Very important

Airfare Not important _____________________ Very important

Graphic rating scales are not limited to straight lines as sources of visual communication. Picture response options or another type of graphic continuum may be used to enhance com- munication with respondents. A variation of the graphic ratings scale is the ladder scale shown in Exhibit 14.7. This scale also includes numerical options.

The diagram below represents the “ladder of life.” As you see, it is a ladder with 11 rungs numbered 0 to 10. Let’s suppose the top of the ladder represents the best possible life for you as you describe it, and the bottom rung represents the worst possible life for you as you describe it. On which rung of the ladder do you feel your life is today? 0 1 2 3 4 5 6 7 8 9 10

EXHIBIT 14.7

A Ladder ScaleBest Possible Life

Worst Possible Life

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CHAPTER 14 Attitude Measurement • 321

322

Research to investigate children’s attitudes has used happy-face scales (see Exhibit 14.8). The children are asked to indicate which face shows how they feel about candy, a toy, or some other concept. Research with the happy-face scale indicates that children tend to choose the faces at the ends of the scale. Although this may be because children’s attitudes fluctuate more widely than adults’ or because they have stronger feelings both positively and negatively, the tendency to select the extremes is a disadvantage of the scale.

Thurstone scale

An attitude scale in which judges assign scale values to

attitudinal statements and subjects are asked to respond

to these statements.

Homebuilders need to know what consumers like, but before they invest in a lot of expensive features, they should know what consumers will pay for. If consumers’ budgets require some hard choices, the homebuilder needs to know which fea- tures are extremely valued, which are nice but not important, and which are difficult to trade off because they are so close in buyer’s minds. When a group of researchers at the University of British Columbia wanted to measure attitudes toward features of “healthy houses,” they compared the scores with a Thur- stone scale.

A healthy house refers to one built with materials and a design affording superior indoor air quality, lighting, and acoustics. The researchers mailed a survey asking respondents whether they would be willing to pay extra if the builder could guarantee better indoor air quality, lighting systems, and acoustics. The survey also presented nine attributes associ- ated with superior indoor air quality and energy efficiency. These were presented in every combination of pairs, and the respondents were directed to choose which item in each pair

they considered more important. Responses to the paired-com- parison questions generated a ranking, which the research- ers used to create a Thurstone scale. The highest-ranked attri- bute (energy efficiency) appears at the top of the scale, with the next attribute (natural light) significantly below it. Thicker insulation, antiallergic materials, and airtight- ness are grouped close together below natural light, and artifi- cial light falls noticeably below the other features.

Source: Spetic, Wellington, Robert Kozak, and David Cohen, “Willing- ness to Pay and Preferences for Healthy Home Attributes in Canada,” Forest Products Journal 55 (October 2005), 19–24; Bower, John, Healthy House Building for the New Millennium, (Bloomington, IN: Healthy House Institute, 1999).

How Much Is a Healthy Home Worth?

RESEARCH SNAPSHOT

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EXHIBIT 14.8

Graphic Rating Scale with Picture Response Categories Stressing Visual Communication

Very GoodVery Poor

321

Happy-Face Scale

Thurstone Interval Scale In 1927, attitude research pioneer Louis Thurstone developed the concept that attitudes vary along continua and should be measured accordingly. The construction of a Thurstone scale is a fairly complex process that requires two stages. The first stage is a ranking operation, performed by judges who assign scale values to attitudinal statements. The second stage consists of asking subjects to respond to the attitudinal statements.

The Thurstone method is time-consuming and costly. From a historical perspective, it is valuable, but its current popularity is low. This method is rarely used in applied research settings. Exhibit 14.9 summarizes the attitude-rating techniques discussed in this section.

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EXHIBIT 14.9

Summary of Advantages and Disadvantages of Rating Scales

Measuring Behavioral Intention The behavioral component of an attitude involves the behavioral expectations of an individual toward an attitudinal object. The component of interest to researchers may be turnover intentions, a tendency to make business decisions in a certain way, or plans to expand operations or product offerings. For example, category scales for measuring the behavioral component of an attitude ask about a respondent’s likelihood of purchase or intention to perform some future action, using questions such as the following:

How likely is it that you will purchase an iPad 2?

• I definitely will not buy. • I probably will not buy. • I might buy. • I probably will buy. • I definitely will buy.

How likely are you to write a letter to your representative in Congress or other government official in support of this company if it were in a dispute with government?

• Absolutely unlikely • Very unlikely • Somewhat unlikely • About a 50–50 chance • Somewhat likely • Very likely • Extremely likely

The wording of statements used in these scales often includes phrases such as “I would recommend,” “I would write,” or “I intend to buy” to indicate action tendencies.

Rating Measure Subject Must Advantages Disadvantages

Category scale Indicate a response category

Flexible, easy to respond to

Items may be ambiguous; with few categories, only gross distinctions can be made

Likert scale Evaluate statements on a scale of agreement

Easiest scale to construct

Hard to judge what a single score means

Semantic differential and numerical scales

Choose points between bipolar adjectives on relevant dimensions

Easy to construct; norms exist for comparison, such as profile analysis

Bipolar adjectives must be found; data may be ordinal, not interval

Stapel scale Choose points on a scale with a single adjective in the center

Easier to construct than semantic differential, easy to administer

Endpoints are numerical, not verbal, labels

Constant-sum scale

Divide a constant sum among response alternatives

Approximates an interval measure

Difficult for respondents with low education levels

Graphic scale Choose a point on a continuum

Visual impact, unlimited scale points

No standard answers

Graphic scale with picture response categories

Choose a visual picture

Visual impact, easy for poor readers

Hard to attach a verbal explanation to a response

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CHAPTER 14 Attitude Measurement • 323

Expectations also may be measured using a scale of subjective probabilities, ranging from 0 for “absolutely no chance” to 100 for “absolutely certain.” Researchers have used the following subjective probability scale to estimate the chance that a job candidate will accept a position within a company:

0% (Certainly not) I will accept 10% (Almost certainly not) I will accept 20% (Very small chance) I will accept 30% (Smaller chance) I will accept 40% (Small chance) I will accept 50% (About even) I will accept 60% (Not so big a chance) I will accept 70% (Big chance) I will accept 80% (Very big chance) I will accept 90% (Almost sure) I will accept

100% (Absolutely certain) I will accept

Behavioral Differential A general instrument, the behavioral differential, is used to measure the behavioral intentions of subjects toward an object or category of objects. As in the semantic differential, a description of the object to be judged is followed by a series of scales on which subjects indicate their behavioral intentions toward this object. For example, one item might be something like this:

A 25-year-old female sales representative Would not ___ ___ ___ ___ ___ ___ ___ Would

ask this person for advice.

Ranking Consumers often rank order their preferences. An ordinal scale may be developed by asking respondents to rank order (from most preferred to least preferred) a set of objects or attributes. Respondents easily understand the task of rank ordering the importance of product attributes or arranging a set of brand names according to preference. Like the constant-sum scale, technically the ranking scale also suffers from inflexibility in that if we know how someone ranked five out of six alternatives, we know the answer to the sixth. In other words, the rankings are not all indepen- dent, but rather dependent on the other objects to be ranked.

Paired Comparisons Paired comparisons is a technique that allows all possible combinations of options to be compared and contrasted. The respondent is asked their preference between each pair of options. The following question illustrates the typical format for asking about paired comparisons.

I would like to know your overall opinion of two brands of adhesive bandages. They are Curad and Band-Aid. Overall, which of these two brands—Curad or Band-Aid—do you think is the better one? Or are both the same?

Curad is better. Band-Aid is better. They are the same.

Consider a situation in which a chainsaw manufacturer learned that a competitor had intro- duced a new lightweight (6-pound) chainsaw. The manufacturer’s lightest chainsaw weighed 9 pounds. Executives wondered if they needed to introduce a 6-pound chainsaw into the product line. The research design chosen was a paired comparison. A 6-pound chainsaw was designed,

behavioral differential

A rating scale instrument similar to a semantic

differential, developed to measure the behavioral

intentions of subjects toward future actions.

paired comparisons

A measurement technique that involves presenting the

respondent with two objects and asking the respondent

to pick the preferred object; more than two objects may be

presented, but comparisons are made in pairs.

‘‘My tastes are very simple. I only want the best.’’—OSCAR WILDE

324 • PART FOUR Measurement Concepts

and a prototype built. To control for color preferences, the competitor’s chainsaw was painted the same color as the 9- and 6-pound chainsaws. Thus, there are four chainsaws of the same color, two 6-pound models and two 9-pound models. Respondents were presented with two chainsaws at a time and asked to pick the one they preferred. Three pairs of comparisons were required to determine the most preferred chainsaw.

If researchers wish to compare four retirement investment plans on the basis of employee attractiveness, six comparisons [(n)(n − 1)/2] will be necessary. In each case, the employee would be asked their preference between two of the retirement plans. In the end, each plan would have been compared to each of the other plans.

When comparing only a few items, ranking objects with respect to one attribute is not dif- ficult. As the number of items increases, the number of comparisons increases geometrically. If the number of comparisons is too large, respondents may become fatigued and no longer carefully discriminate among them.

Sorting Sorting tasks ask respondents to indicate their attitudes or beliefs by arranging items on the basis of perceived similarity or some other attribute. One advertising agency has had consumers sort photographs of people to measure their perceptions of a brand’s typical user. Another agency used a sorting technique in which consumers used a deck of 52 cards illustrating elements from adver- tising for the brand name being studied. The study participants created a stack of cards showing elements they recalled seeing or hearing, then the interviewer asked the respondent to identify the item on each of those cards. National City Corporation, a banking company, has used sorting as part of its research into the design of its website. Consumers participating in the research were given a set of cards describing various parts of processes that they might engage in when they are banking online. The participants were asked to arrange the cards to show their idea of a logical way to complete these processes. This research method shows the website designers how con- sumers go about doing something—sometimes very differently from the way bankers expect.6

Sorting techniques can be applied in a wide variety of environments. A variant of the constant- sum technique uses physical counters (such as coins or poker chips) to be allocated among the alternatives. In an airline study of customer preferences, the following sorting technique could be used:

Below is a list of several airlines. Next to the name of each airline is a pocket. Here are 10 cards. I would like you to put these cards in the pockets next to the airlines you would prefer to fly on your next trip. Assume that all of the airlines fly to wherever you would choose to travel. You can put as many cards as you want next to an airline, or you can put no cards next to an airline.

Cards

American Airlines ___ AirTran Airways ___ Delta Airlines ___ United Airlines ___ Southwest Airlines ___

Other Methods of Attitude Measurement Attitudes, as hypothetical constructs, cannot be observed directly. We can, however, infer one’s attitude by the way he or she responds to multiple attitude indicators. A summated rating scale can be made up of three indicators of attitude. Consider the following three semantic differential items that may capture a person’s attitude towards their immediate supervisor:

Not supportive — — — — — — — Very supportive

Very favorable — — — — — — — Very unfavorable

Very negative — — — — — — — Very positive

CHAPTER 14 Attitude Measurement • 325

Notice that the middle question would need to be reverse-coded before a composite scale is calculated. The terminology is such that the scores from these measures would reflect the latent (unobservable) construct of attitude toward a supervisor.

Selecting a Measurement Scale: Some Practical Decisions Now that we have looked at a number of attitude measurement scales, a natural question arises: “Which is most appropriate?” As in the selection of a basic research design, there is no single best answer for all research projects. The answer to this question is relative, and the choice of scale will depend on the nature of the attitudinal object to be measured, the manager’s prob- lem definition, and the backward and forward linkages to choices already made (for example, telephone survey versus mail survey). However, several questions will help focus the choice of a measurement scale:

1. Is a ranking, sorting, rating, or choice technique best? 2. Should a monadic or a comparative scale be used? 3. What type of category labels, if any, will be used for the rating scale? 4. How many scale categories or response positions are needed to accurately measure an attitude? 5. Should a balanced or unbalanced rating scale be chosen? 6. Should an even or odd number of response categories be provided? 7. Should a scale that forces a choice among predetermined options be used? 8. Should a single measure or an index measure be used?

We will discuss each of these issues briefly.

Ranking, Sorting, Rating, or Choice Technique? The decision whether to use ranking, sorting, rating, or a choice technique is determined largely by the problem definition and especially by the type of statistical analysis desired. For example, ranking provides only ordinal data, limiting the statistical techniques that may be used. In general, more information is gained from a rating approach than from ranking, sorting, or choice. How- ever, if the researcher feels that little variance would be generated from a rating approach, then an alternative may be preferred. For example, if a researcher was trying to determine how important salary, opportunities for advancement, and an enjoyable work environment were on the selection of a career opportunity, a rating scale might not generate much variance as all three factors are likely to be considered “very important.” If a ranking or sorting approach is used, at least the relative importance of each factor could be determined.

Monadic or Comparative Scale? If the scale to be used is not a ratio scale, the researcher must decide whether to include a standard of comparison in the verbal portion of the scale. Consider the following rating scale:

Please indicate how satisfied you are with the amount of authority you are given in your present position.

Completely Dissatisfied Dissatisfied

Somewhat Satisfied Satisfied

Completely Satisfied

□ □ □ □ □

326 • PART FOUR Measurement Concepts

This is a monadic rating scale, because it asks about a single concept (level of authority) in isola- tion. The respondent is not given a specific frame of reference. A person that is dissatisfied with their level of authority could feel so because they have either too little or too much responsibility. A comparative rating scale asks a respondent to rate a concept, such as a specific amount of responsibility or authority, in comparison with a benchmark—perhaps another similar concept— explicitly used as a frame of reference. In many cases, the comparative rating scale presents an ideal situation as a reference point for comparison with the actual situation. For example:

Please indicate how the amount of authority in your present position compares with the amount of author- ity that would be ideal for this position.

Too little □ About right □ Too much □

What Type of Category Labels, If Any? We have discussed verbal labels, numerical labels, and unlisted choices. Many rating scales have verbal labels for response categories because researchers believe they help respondents better understand the response positions. The maturity and educational levels of the respondents will influence this decision. The semantic differential, with unlabeled response categories between two bipolar adjectives, and the numerical scale, with numbers to indicate scale positions, often are selected because the researcher wishes to assume interval-scale data. It is more challenging to identify a series of verbal labels (i.e., extremely disappointed, very disappointed, disappointed, and so on) that have a consistent gap than to use numbers (i.e., 1, 2, 3 and so on) that we assume have an equal gap.

How Many Scale Categories or Response Positions? Should a category scale have four, five, or seven response positions or categories? Or should the researcher use a graphic scale with an infinite number of positions? The original developmental research on the semantic differential indicated that five to eight points is optimal. However, the researcher must determine the number of meaningful positions that is best for the specific project. This issue of identifying how many meaningful distinctions respondents can practically make is basically a matter of sensitivity, but at the operational rather than the conceptual level. In general, more scale points (7-10) seem to perform better than fewer (3-4). Often a 10-point scale is very effective as it is easy for respondents to think in those terms.

Balanced or Unbalanced Rating Scale? The fixed-alternative format may be balanced or unbalanced. For example, the following question, which asks about parent-child decisions relating to television program watching, is a balanced rating scale:

Who decides which television programs your children watch?

Child decides all of the time. □ Child decides most of the time. □ Child and parent decide together. □ Parent decides most of the time. □ Parent decides all of the time. □

This scale is balanced because a neutral point, or point of indifference, is at the center of the scale.

monadic rating scale

Any measure of attitudes that asks respondents about a single concept in isolation.

comparative rating scale

Any measure of attitudes that asks respondents to rate a concept in comparison with a benchmark explicitly used as a frame of reference.

balanced rating scale

A fixed-alternative rating scale with an equal number of positive and negative categories; a neutral point or point of indifference is at the center of the scale.

CHAPTER 14 Attitude Measurement • 327

Unbalanced rating scales may be used when responses are expected to be distributed at one end of the scale. Unbalanced scales, such as the following one, may eliminate this type of “end piling”:

Completely Dissatisfied Dissatisfied

Somewhat Satisfied Satisfied

Completely Satisfied

□ □ □ □ □

Notice that there are three “satisfied” responses and only two “dissatisfied” responses above. The choice of a balanced or unbalanced scale generally depends on the nature of the concept or the researcher’s knowledge about attitudes toward the stimulus to be measured.

Even or Odd Number of Scale Points? The researcher needs to decide if an even or odd number of response categories should be provided. The issue here is should there be a “neutral” point in the scale? With an odd number of scale points there is often a middle ground that is noncommittal. An even number of scale points forces the respondent to commit to a “pro” or “con” response on the issue. If the researcher feels that respondents can truly hold a neutral attitude regarding the subject of investigation, an odd number of scale points is appropriate. However, issues that tend to evoke strong opinions, for example political candidates or social issues such as legalization of marijuana, are likely better captured by an even number of scale points where a respondent is forced to come down on one side of the issue.

Use a Scale That Forces a Choice among Predetermined Options? In many situations, a respondent has not formed an attitude toward the concept being studied and simply cannot provide an answer. If a forced-choice rating scale compels the respondent to answer, the response is merely a function of the question. If answers are not forced, the midpoint of the scale may be used by the respondent to indicate unawareness as well as indifference. If many respondents in the sample are expected to be unaware of the attitudinal object under investigation, this problem may be eliminated by using a non-forced-choice scale that provides a “no opinion” category, as in the following example:

How does the Bank of Commerce compare with the First National Bank?

□ Bank of Commerce is better than First National Bank. □ Bank of Commerce is about the same as First National Bank. □ Bank of Commerce is worse than First National Bank. □ Can’t say.

Asking this type of question allows the investigator to separate respondents who cannot make an honest comparison from respondents who have had experience with both banks. The argu- ment for forced choice is that people really do have attitudes, even if they are unfamiliar with the banks, and should be required to answer the question. Still, the use of forced-choice questions is associated with higher incidences of item non-response (no answer). Internet surveys make forced-choice questions easy to implement because the delivery can be set up so that a respon- dent cannot go to the next question until the previous question is answered. Realize, however, if a respondent truly has no opinion, and the no opinion option is not included, he or she may simply quit responding to the questionnaire.

‘‘Refusing to have an opinion is a way of having one, isn’t it?’’ —LUIGI PIRANDELLO

forced-choice rating scale

A fixed-alternative rating scale that requires respondents

to choose one of the fixed alternatives.

unbalanced rating scale

A fixed-alternative rating scale that has more response

categories at one end than the other, resulting in an unequal

number of positive and negative categories.

328 • PART FOUR Measurement Concepts

Single Measure or an Index Measure? Whether to use a single measure or an index measure depends on the complexity of the issue to be investigated, the number of dimensions the issue contains, and whether individual attributes of the stimulus are part of a holistic attitude or are seen as separate items. Very simple concepts that do not vary from context to context can be measured by single items. However, most psychological concepts are more complex and require multiple-item measurement. Additionally, multiple-item measures are easier to test for reliability and construct validity (as we discussed in Chapter 13). The researcher’s conceptual definition will be helpful in making this choice.

The researcher has many scaling options. Generally, the choice is influenced by plans for the later stages of the research project. As with virtually all decisions in the research process, problem definition becomes a determining factor influencing the research design.

SUMMARY 1. Describe how business researchers think of attitudes. Attitudes are enduring dispositions to consistently respond in a given manner to various aspects of the world, including persons, events, and objects. Attitudes consist of three components: the cognitive, or awareness or knowledge; the affective, or the emotions or feelings involved; and the behavioral, or the predisposition to action. Attitudes are latent constructs and they are not directly observable.

2. Identify basic approaches to measuring attitudes. Many attitude measurement methods have been developed. Most fall into the categories of ranking, rating, sorting, and choice techniques.

3. Discuss the use of rating scales for measuring attitudes. One class of rating scales, category scales, provides several response categories to allow respondents to indicate the intensity of their attitudes. The Likert scale uses a series of statements with which subjects indicate agreement or disagreement. The levels of agreement with some statement are assigned numerical scores. A semantic differential uses a series of attitude scales anchored by bipolar adjectives. The respon- dent indicates where his or her attitude falls between the polar attitudes. Variations on this method, such as numerical scales and the Stapel scale, are also used. The Stapel scale puts a single adjective in the center of a range of numerical values, for example from +3 to –3. Constant-sum scales require the respondent to divide a constant sum into parts, indicating the weights to be given to various attributes of the item being studied.

4. Represent a latent construct by constructing a summated scale. Researchers use composite scales to represent latent constructs. An easy way to create a composite scale is to add the responses to multiple items together to form a total. Thus, a respondent’s scores to four items can be simply added together to form a summated scale. The researcher must check to make sure that each scale item is worded positively, or at least all in the same direction. For example, if multiple items are used to form a satisfaction construct, a higher score for each item should lead to higher satisfaction. If one of the items represents dissatisfaction, such that a higher score represents lower satisfaction, this item must be reverse-coded prior to creating the composite scale.

5. Summarize ways to measure attitudes with ranking and sorting techniques. People often rank order their preferences. Thus, ordinal scales that ask respondents to rank order a set of objects or attributes may be developed. In the paired-comparison technique, two alternatives are paired and respondents are asked to pick the preferred one. Sorting requires respondents to indicate their attitudes by arranging items into piles or categories.

6. Discuss major issues involved in the selection of a measurement scale. The researcher can choose among a number of attitude scales. Choosing among the alternatives requires consider- ing several questions, each of which is generally answered by comparing the advantages of each alternative to the problem definition. A monadic rating scale asks about a single concept. A com- parative rating scale asks a respondent to rate a concept in comparison with a benchmark used as a frame of reference. Scales may be balanced or unbalanced. Unbalanced scales may prevent responses from piling up at one end. Scales with an odd number of response categories provide a neutral point; an even number of response categories makes the respondent express a negative or positive attitude. Forced-choice scales require the respondent to select an alternative; non-forced- choice scales allow the respondent to indicate an inability to select an alternative.

CHAPTER 14 Attitude Measurement • 329

KEY TERMS AND CONCEPTS attitude, 311 balanced rating scale, 327 behavioral differential, 324 category scale, 315 choice, 313 comparative rating scale, 327 composite scale, 316 constant-sum scale, 320

forced-choice rating scale, 328 graphic rating scale, 321 hypothetical constructs, 311 image profile, 318 Likert scale, 316 monadic rating scale, 327 numerical scale, 319 paired comparisons, 324

ranking, 313 rating, 313 reverse coding, 316 semantic differential, 317 sorting, 313 Stapel scale, 319 Thurstone scale, 322 unbalanced rating scale, 328

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is an attitude? Is there a consensus concerning its

definition? 2. Distinguish between rating and ranking. Which is a better

attitude measurement technique? Why? 3. Assume the researcher wanted to create a summated scale

indicating a respondent’s attitude toward the trucking industry. What would the result be for the respondent whose response is as indicated below?

Strongly Strongly Disagree Agree

a. Our company has a health plan that addresses the needs of my family .............. ➀ ➁ ➂ ➃ ➄ ➅ ➐ ➇ ➈ ➉

b. My experiences with the health plan coordinator have been good ......................... ➀ ➁ ➂ ➃ ➄ ➅ ➆ ➑ ➈ ➉

c. My supervisor sees me as an asset to the company ............. ➀ ➁ ➂ ➃ ➄ ➅ ➆ ➇ ➒ ➉

d. My supervisor encourages me to contribute ways that can make our work space better ............................... ➀ ➁ ➂ ➃ ➄ ➅ ➆ ➇ ➒ ➉

e. My company puts safety as a top priority ......................... ➀ ➁ ➂ ➃ ➄ ➅ ➆ ➇ ➈ ➓

4. How would you perform reverse coding using statistical software like SAS or SPSS?

5. What advantages do numerical scales have over semantic differential scales?

6. Identify the issues a researcher should consider when choosing a measurement scale.

7. Should a Likert scale ever be treated as though it had ordinal properties?

8. In each of the following, identify the type of scale and evaluate it:

a. A U.S. representative’s questionnaire sent to constituents: Do you favor or oppose the Fair Tax Proposal?

In Favor Opposed No Opinion □ □ □

How favorable are you toward the Fair Tax Proposal?

Very Unfavorable □ □ □ □ □ Very Favorable

b. I believe the Fair Tax Proposal will reduce the federal deficit.

Strongly Disagree

Disagree

Neutral

Agree

Strongly Agree

1 2 3 4 5

9. What is the difference between a measured variable and a latent construct?

10. If a Likert summated scale has 10 scale items, do all 10 items have to be phrased as either positive or negative statements, or can the scale contain a mix of positive and negative statements?

11. If a semantic differential has 10 scale items, should all the positive adjectives be on the right and all the negative adjectives on the left?

12. ETHICS A researcher thinks many respondents will answer “don’t know” or “can’t say” if these options are printed on an attitude scale along with categories indicating level of agreement. The researcher does not print either “don’t know” or “can’t say” on the questionnaire because the resulting data would be more complicated to analyze and report. Is this proper?

13. ’NET Strategic Business Insights investigates U.S. consumers by asking questions about their attitudes and values. It has a website so people can VALS-type themselves. To find out your VALS type, go to http://www.strategicbusinessinsights.com/vals/ presurvey.shtml.

330 Part 4: Measurement Concepts

RESEARCH ACTIVITY 1. A researcher wishes to compare two hotels on the following

attributes: Convenience of location Friendly personnel Value for money

a. Design a Likert scale to accomplish this task. b. Design a semantic differential scale to accomplish this task. c. Design a graphic rating scale to accomplish this task.

A decade ago, the talk in business circles was all about the central role of technology, especially the Internet, in the success of new businesses. Some

investors seemed eager to back almost any start-up with “dot-com” in its name or its business plan. Although the go-go investment cli- mate of the 1990s seems far away, entrepreneurs still start companies every year, and they are still making their case to the investment community. What business ideas do investors like? Is high-tech still important? Public relations firm Roeder-Johnson Corporation, which specializes in start-up companies and those involved in tech- nology innovation, conducted an online survey into the attitudes of 70 subjects, including venture capitalists, entrepreneurs, journalists, and company analysts.7 The central question was this:

Deo you believe that unique technology is crucial to the success of start-up companies today? 1. Rarely 2. Occasionally 3. Frequently

Roeder-Johnson Corporation 4. Usually 5. Always

The remainder of the survey asked for reasons why technology is important to start-ups and invited comments from the respondents.

In its news release, Roeder-Johnson reported that 91 percent of respondents consider technology to be important at least frequently. The breakdown was 39 percent frequently, 39 percent usually, and 13 per- cent always. The remaining 9 percent of respondents cited technology as important only occasionally, and none said it is rarely important.

Questions 1. Evaluate the rating scale used for the question in this survey. Is it

balanced? Are the category labels clear? Is the number of catego- ries appropriate?

2. Suggest three ways that Roeder-Johnson could improve this survey without a major cost increase.

3. Based on the information given here, what do you think the research objectives for this survey might have been? Do you think the survey met its objectives? Explain.

CASE 14.1

A marketing research company sent the attitude scales in Case Exhibit 14.2-1 to members of its con- sumer panel. Other questions on the questionnaire

were about ownership and/or use of computers, consumer electronic devices, satellite TV ownership, cellular phones, and Internet activity.

Attitudes toward Technology and Lifestyle Questions 1. What type of attitude scale appears in the case study? 2. Evaluate the list of statements. Do the statements appear to mea-

sure a single concept? 3. What do they appear to be measuring?

CASE 14.2

(Continued )

Chapter 14: Attitude Measurement 331

CASE EXHIBIT 14.2-1

Attitude Scale

Below is a list of statements that may or may not be used to describe your attitudes toward technology and your lifestyle. Please indicate to what extent each statement describes your attitudes by placing an X in a box from 1 to 10, where 10 means that statement “Describes your attitudes completely” and a 1 means that statement “Does not describe your attitudes at all.” (X ONE BOX ACROSS FOR EACH STATEMENT.)

Does Not Describe Your Attitudes At All

Describes Your Attitudes Completely

1 2 3 4 5 6 7 8 9 10

I like to impress people with my lifestyle. □ □ □ □ □ □ □ □ □ □

Technology is important to me. □ □ □ □ □ □ □ □ □ □

I am very competitive when it comes to my career. □ □ □ □ □ □ □ □ □ □

Having fun is the whole point of life. □ □ □ □ □ □ □ □ □ □

Family is important, but I have other interests that are just as important to me.

□ □ □ □ □ □ □ □ □ □

I am constantly looking for new ways to entertain myself. □ □ □ □ □ □ □ □ □ □

Making a lot of money is important to me. □ □ □ □ □ □ □ □ □ □

I spend most of my free time doing fun stuff with my friends. □ □ □ □ □ □ □ □ □ □

I like to spend time learning about new technology products. □ □ □ □ □ □ □ □ □ □

I like to show off my taste and style. □ □ □ □ □ □ □ □ □ □

I like technology. □ □ □ □ □ □ □ □ □ □

My family is by far the most important thing in my life. □ □ □ □ □ □ □ □ □ □

I put a lot of time and energy into my career. □ □ □ □ □ □ □ □ □ □

I am very likely to purchase new technology products or services.

□ □ □ □ □ □ □ □ □ □

I spend most of my free time working on improving myself. □ □ □ □ □ □ □ □ □ □

Case 14.2 (Continued )

332 Part 4: Measurement Concepts

333

15 C H A P T E R

After studying this chapter, you should be able to

1. Explain the significance of decisions about questionnaire design and wording

2. Define alternatives for wording open-ended and fixed-alternative questions

3. Summarize guidelines for questions that avoid mistakes in questionnaire design

4. Describe how the proper sequence of questions may improve a questionnaire

5. Discuss how to design a questionnaire layout

6. Describe criteria for pretesting and revising a questionnaire and for adapting it to global markets

LEARNING OUTCOMES

Questionnaire Design

A re you driving your dream car? Most of us can’t, because we bump up against the practical reality that we can’t pay for every great new feature. As car makers consider adding new

features, they have to evaluate not only which ones appeal to consumers but also which ones will actually sell, consid- ering their likely cost. J.D. Power and Associates recently addressed this issue in a survey of about seventeen thousand consumers.1

In the J.D. Power survey, consumers were asked whether they were familiar with 22 different emerging technologies. Then they were asked about their interest in each technol- ogy, rating their interest using a scale (“definitely interested,” “probably interested,” and so on). Next, the study indicated the likely price of each technology, and consumers were again asked their interest, given the price. The results ranked the features according to interest level, based on the percent- age who indicated they were either definitely or probably interested in the feature.

Learning price information often changed consumers’ interest levels. Night vision systems appealed to 72 percent of consumers, placing it in second place in the rankings. But when

consumers learned the systems would likely add $1,500 to the price of a car, this technology dropped to a rank of 17, near the bottom. In contrast, HD radio ranked in sixteenth place until consumers saw a price tag of just $150. That price pushed the feature up to third place. Still, two features remained in the top five even with pricing information: run-flat tires and stability

Chapter Vignette:

J.D. Power Asks: It’s Interesting, But Do You Really Want It?

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Introduction Each stage in the business research process is important and interdependent. The research question- naire development stage is critically important as the information provided is only as good as the questions asked. However, the importance of question wording is easily, and far too often, overlooked.

Businesspeople who are inexperienced at research frequently believe that constructing a ques- tionnaire is a simple task. Amateur researchers think a short questionnaire can be written in min- utes. Unfortunately, newcomers who naively believe that good grammar is all a person needs to construct a questionnaire generally end up with useless results. Ask a bad question, get bad results.

Good questionnaire design requires far more than correct grammar. People don’t understand questions just because they are grammatically correct. Respondents simply may not know what is being asked. They may be unaware of the business issue or topic of interest. They may confuse the subject with something else. The question may not mean the same thing to everyone interviewed. Finally, people may refuse to answer personal questions. Most of these problems can be minimized, however, if a skilled researcher composes the questionnaire.

Questionnaire Quality and Design: Basic Considerations For a questionnaire to fulfill a researcher’s purposes, the questions must meet the basic criteria of relevance and accuracy. To achieve these ends, a researcher who is systematically planning a ques- tionnaire’s design will be required to make several decisions—typically, but not necessarily, in the following order:

1. What should be asked? 2. How should questions be phrased? 3. In what sequence should the questions be arranged? 4. What questionnaire layout will best serve the research objectives? 5. How should the questionnaire be pretested? Does the questionnaire need to be revised?

This chapter provides guidelines for answering each question.

What Should Be Asked? Certain decisions made during the early stages of the research process will influence the ques- tionnaire design. The preceding chapters stressed good problem definition and clear research questions. This leads to specific research hypotheses that, in turn, clearly indicate what must be measured. Different types of questions may be better at measuring certain things than are others. In addition, the communication medium used for data collection—that is, telephone interview, personal interview, or self-administered questionnaire—must be determined. This decision is another forward linkage that influences the structure and content of the questionnaire. Therefore, the specific questions to be asked will be a function of previous decisions made in the research

control. And three of the bottom-five features—a reconfigu- rable cabin, lane departure warning system, and smart sensing power-swing front doors—stayed in the bottom rankings. Automakers can use findings such as these to determine which features are price-sensitive and which might be appealing even at a higher price.

The J.D. Power survey shows how extremely useful information can be gathered with a questionnaire. It also shows how results can differ by exactly what question is asked and the amount of information provided. This chapter outlines a procedure for questionnaire design, which addresses concerns such as the wording and order of questions and the layout of the questionnaire.

334 • PART FOUR Measurement Concepts

335

process. At the same time, the latter stages of the research process will also have an important impact on questionnaire wording and measurement. For example, when designing the question- naire, the researcher should consider the types of statistical analysis that will be conducted.

Questionnaire Relevancy A questionnaire is relevant to the extent that all information collected addresses a research question that will help the decision maker address the current business problem. Asking a wrong question or an irrelevant question is a common pitfall. If the task is to pinpoint store image problems, ques- tions asking for political opinions are likely irrelevant. The researcher should be specific about data needs and have a rationale for each item requesting information. Irrelevant questions are more than a nuisance because they make the survey needlessly long. In a study where two samples of the same group of businesses received either a one-page or a three-page questionnaire, the response rate was nearly twice as high for the one-page survey.2

Conversely, many researchers, after conducting surveys, find that they omitted some important questions. Therefore, when planning the questionnaire design, researchers must think about possible omissions. Is information on the relevant demographic and psychographic variables being collected? Would certain questions help clarify the answers to other questions? Will the results of the study provide the answer to the manager’s problem?

Questionnaire Accuracy Once a researcher decides what should be asked, the criterion of accuracy becomes the primary concern. Accuracy means that the information is reliable and valid. While experienced research- ers generally believe that questionnaires should use simple, understandable, unbiased, unambig- uous, and nonirritating words, no step-by-step procedure for ensuring accuracy in question writing can be generalized across projects. Obtaining accurate answers from respondents depends strongly on the researcher’s ability to design a questionnaire that will facilitate recall and motivate respondents to cooperate. Respondents tend to be more cooperative when the subject of the research interests them. When questions are not lengthy, difficult to answer, or ego threatening, there is a higher probability of obtaining unbiased answers.

Question wording and sequence also substantially influence accuracy, which can be particularly challenging when designing a survey for technical audiences. The Department of the Treasury commissioned a survey of insurance companies to evaluate their offering of terrorism insurance as required by the government’s terrorism reinsurance program. But industry members complained

‘‘How often misused words generate misleading thoughts.’’—HERBERT SPENCER

This chapter deals with asking questions in a way that best assures the data will be of high quality and actually address the issues involved in research questions in an unbiased fashion. By now, you are familiar with the survey instrument. Critique it from a standpoint of the learning objectives of this chap- ter. In particular, comment on the order of questions or on the presence of any leading or double-barreled items. What are the strengths and weaknesses of this questionnaire?

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that the survey misused terms such as “contract” and “high risk,” which have precise meanings for insurers, and asked for policy information “to date,” without specifying which date. These questions caused confusion and left room for interpretation, calling the survey results into question.3

Wording Questions There are many ways to phrase questions, and many standard question formats have been developed in previous research studies. This section presents a classification of question types and provides some helpful guidelines for writing questions.

Open-Ended Response versus Fixed-Alternative Questions The first decision in questionnaire design is based on the amount of freedom respondents have in answering. Should the question be open-ended, allowing the participants freedom to choose their manner of response, or closed, where the participants choose their response from an already determined fixed set of choices?

Open-ended response questions pose some problem or topic and ask respondents to answer in their own words. If the question is asked in a personal interview, the interviewer may probe for more information, as in the following examples:

What names of local banks can you think of? What comes to mind when you look at this advertisement? In what way, if any, could this product be changed or improved? I’d like you to tell me anything you can think of, no matter how minor it seems. What things do you like most about working for Federal Express? What do you like least? Why do you buy more of your clothing in Nordstrom than in other stores? How would you describe your supervisor’s management style? Please tell us how our stores can better serve your needs.

Open-ended response questions are free-answer questions. They may be contrasted with fixed- alternative questions—sometimes called closed-ended questions—which give respondents specific limited-alternative responses and ask them to choose the one closest to their own viewpoints. For example:

Did you use any commercial feed or supplement for livestock or poultry in 2010?

□ Yes □ No

Would you say that the labor quality in Japan is higher, about the same, or not as good as it was 10 years ago?

□ Higher □ About the same □ Not as good

Do you think the Renewable Energy Partnership Program has affected your business?

□ Yes, for the better □ Not especially □ Yes, for the worse

How much of your welding supplies do you purchase from our Tier One suppliers?

□ All of it □ Most of it □ About one-half of it □ About one-quarter of it □ Less than one-quarter of it

open-ended response questions

Questions that pose some problem and ask respondents to answer in their own words.

fixed-alternative questions

Questions in which respondents are given specific,

limited-alternative responses and asked to choose the one

closest to their own viewpoint.

336 • PART FOUR Measurement Concepts

337

The Research Snapshot “Corporate Reputations: Consumers Put Johnson & Johnson, Microsoft, and Google on Top” illustrates the use of a multifaceted survey to assess corporate reputation.

Using Open-Ended Response Questions Open-ended response questions are most beneficial when the researcher is conducting exploratory research, especially when the range of responses is not yet known. Respondents are free to answer with whatever is foremost in their minds. Such questions can be used to learn which words and phrases people spontaneously give to the free-response question. Such responses will reflect the flavor of the language that people use in talking about the issue and thus may provide guidance in the wording of questions and responses for follow up surveys.

Also, open-ended response questions are valuable at the beginning of an interview. They are good first questions because they allow respondents to warm up to the questioning process. They are also good last questions for a fixed-alternative questionnaire, when a researcher can ask the respon- dent to expand in a manner that provides greater richness to the data. For example, an employee sat- isfaction survey may collect data with a series of fixed-alternative questions, and then conclude with, “Can you provide one suggestion on how our organization can enhance employee satisfaction?”

The cost of administering open-ended response questions is substantially higher than that of administering fixed-alternative questions because the job of editing, coding, and analyzing the data is quite extensive. As each respondent’s answer is somewhat unique, there is some difficulty in categorizing and summarizing the answers. The process requires that an editor go over a sample of questions to develop a classification scheme. This scheme is then used to code all answers according to the classification scheme.

To report the reputations of well-known companies, the Wall Street Journal sponsors an annual research project. Harris Interactive has used the Harris Reputation Quotient (RQ) to assess the reputations of the 60 most visible companies in the U.S. since 1999. This corporate reputation survey allows U.S. adults to provide their perceptions of corporations.

The study has two phases. In the first phase, the researchers identify the companies that were most “visible,” meaning companies that people were most likely to think about—and therefore have an attitude toward. This phase avoids the problem of asking individuals to rate the qualities of a company they have never heard of. This research uses open-ended questions asking respondents to name two companies they felt had the best repu- tation and two that had the worst. The researchers determine the number of times each company was mentioned and select the 60 named most often for the second phase of the study.

The second phase is aimed at generating rankings of the corporations, so questions and answer choices need to be more specific. The researchers identify six dimensions of a corporate reputation: products and services, financial performance, work- place environment, social responsibility, vision and leadership, and emotional appeal. Within these categories, they identify 20 attributes, such as whether respondents would trust the company if they had a problem with its goods or services, and

how sincere its corpo- rate communications were. In an online survey, each respon- dent is asked to rate one company on all 20 attributes. Then the respondent is invited (not required) to rate a second company. Each year, about 20,000 people participate in the study and more than 250 ratings are generated for each company. These responses are combined to create an overall rating for the company.

The top-ranked company for each of the first seven years of the survey was Johnson & Johnson. On the six dimensions of reputation, J&J was tops in emotional appeal and its goods and services, and it made the top five on the other dimensions. This honor is more than just good publicity; J&J also was the firm from which the largest share of people said they would “definitely purchase” products.

Sources: Based on Alsop, Ronald, “Ranking Corporation Reputations,” Wall Street Journal (December 6, 2005), http://online.wsj.com; “The Annual RQ 2007: The Reputations of the Most Visible Companies,” Harris Interac- tive, Inc. (2008), http://www.harrisinteractive.com/vault/HI-News-Release- RQ-2011-05-02.pdf.

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Another potential disadvantage of the open-ended response question is the possibility that interviewer bias will influence the answer. While most interviewer instructions state that answers are to be recorded verbatim, rarely does even the best interviewer get every word spoken by the respondent. Interviewers have a tendency to take shortcuts. When this occurs, the interviewer may well introduce error because the final answer may reflect a combination of the respondent’s and interviewer’s ideas.

In addition, articulate individuals tend to give longer answers to open-ended response ques- tions. Such respondents often are better educated and from higher income groups and there- fore may not be good representatives of the entire population. Yet, they may provide the most information.

Using Fixed-Alternative Questions In contrast, fixed-alternative questions require less interviewer skill, take less time, and are easier for the respondent to answer. This is because answers to closed questions are classified into stan- dardized groupings prior to data collection. Standardizing alternative responses to a question provides comparability of answers, which facilitates coding, tabulating, and ultimately interpreting the data.

However, when a researcher is unaware of the potential responses to a question, fixed- alternative questions obviously cannot be used. If the researcher assumes what the responses will be, but is in fact wrong, he or she will have no way of knowing the extent to which the assumption was incorrect. Sometimes this type of error comes to light after the questionnaire has been used. Researchers found cross-cultural misunderstandings in a survey of mothers called the Preschooler Feeding Questionnaire. By talking to a group of African-American mothers, a researcher at the University of Chicago determined that they had experiences with encouraging children to eat more and using food to calm children, but they used different language for these situations than the questionnaire used, so they misinterpreted some questions.4

Unanticipated alternatives emerge when respondents believe that closed answers do not ade- quately reflect their feelings. They may make comments to the interviewer or write additional answers on the questionnaire indicating that the exploratory research did not yield a complete array of responses. After the fact, little can be done to correct a closed question that does not provide the correct responses or enough alternatives. Therefore, a researcher may find exploratory research with open-ended responses valuable before writing a descriptive questionnaire. The researcher should strive to ensure that there are sufficient response choices to include almost all possible answers.

Respondents may check off obvious alternatives, such as salary or health benefits in an employee survey, if they do not see opportunities for advancement, the choice they would prefer. Also, a fixed-alternative question may tempt respondents to check an answer that is more presti- gious or socially acceptable than the true answer. Rather than stating that they do not know why they chose a given product, they may select an alternative among those presented, or as a matter of convenience, they may select a given alternative rather than think of the most correct response.

Most questionnaires mix open-ended and closed questions. As we have discussed, each form has unique benefits. In addition, a change of pace can eliminate respondent boredom and fatigue.

Types of Fixed-Alternative Questions Earlier in the chapter a variety of fixed-alternative questions were presented. Here we identify and categorize the various types.

The simple-dichotomy (dichotomous) question requires the respondent to choose one of two alternatives. The answer can be a simple “yes” or “no” or a choice between “this” and “that.” For example:

Did you have any overnight travel for work-related activities last month?

□ Yes □ No

simple-dichotomy (dichotomous) question

A fixed-alternative question that requires the respondent to choose one of two alternatives.

338 • PART FOUR Measurement Concepts

Several types of questions provide the respondent with multiple-choice alternatives. The determinant-choice question requires the respondent to choose one—and only one—response from among several possible alternatives. For example:

Please give us some information about your flight. In which section of the aircraft did you sit?

□ First class □ Business class □ Coach class

The frequency-determination question is a determinant-choice question that asks for an answer about the general frequency of occurrence. For example:

How frequently do you watch MTV?

□ Every day □ 5–6 times a week □ 2–4 times a week □ Once a week □ Less than once a week □ Never

Attitude rating scales, such as the Likert scale, semantic differential, Stapel scale, and so on, are also fixed-alternative questions. These scales were discussed in Chapter 14.

The checklist question allows the respondent to provide multiple answers to a single question. The respondent indicates past experience, preference, and the like merely by checking off items. In many cases the choices are adjectives that describe a particular object. A typical checklist question might ask the following:

Please check which, if any, of the following sources of information about investments you regularly use.

□ Personal advice of your broker(s) □ Brokerage newsletters □ Brokerage research reports □ Investment advisory service(s) □ Conversations with other investors □ Web page(s) □ None of these □ Other (please specify) __________

A major problem in developing dichotomous or multiple-choice alternatives is establishing the response alternatives. Alternatives should be totally exhaustive, meaning that all the response options are covered and that every respondent has an alternative to check. The alternatives should also be mutually exclusive, meaning there should be no overlap among categories and only one dimension of an issue should be related to each alternative. So, there is a response category for every- one, but only a single response category for each individual. In other words, a place for everything and everything in its place! The following listing of income groups illustrates common errors:

□ $10,000 –$30,000 □ $30,000 –$50,000 □ $50,000 –$70,000 □ $70,000 –$90,000 □ $90,000 –$110,000 □ Over $110,000

Which category would a respondent with an annual income of $30,000 check? How many people with incomes of $30,000 will be in the second group, and how many will be in the third group? Researchers have no way to determine the answer. This is an example of failing to have mutually exclusive response categories. The question also is not totally exhaustive, as there is no category for those earning less than $10,000 to check. Also, few people relish being in the lowest category. To negate the potential bias caused by respondents’ tendency to avoid an extreme category,

determinant-choice question

A fixed-alternative question that requires the respondent to choose one response from among multiple alternatives.

frequency-determination question

A fixed-alternative question that asks for an answer about general frequency of occurrence.

checklist question

A fixed-alternative question that allows the respondent to provide multiple answers to a single question by checking off items.

totally exhaustive

A category exists for every respondent among the fixed- alternative categories

mutually exclusive

No overlap exists among the fixed-alternative categories

CHAPTER 15 Questionnaire Design • 339

researchers often include a category lower than the lowest expected answers. The following response categories address the totally exhaustive and mutually exclusive issues:

□ Less than $10,000 □ $10,000 –$29,999 □ $30,000 –$49,999 □ $50,000 –$69,999 □ $70,000 –$89,999 □ $90,000 –$109,999 □ Over $110,000

While this example makes the totally exhaustive and mutually exclusive categories rather clear, it can actually become quite challenging. Consider the preceding frequency-determination question regarding MTV. With a question such as this, it can become difficult to establish response categories that meet these rules.

Phrasing Questions for Self-Administered, Telephone, and Personal Interview Surveys The means of data collection—telephone interview, personal interview, self-administered questionnaire—will influence the question format and question phrasing. In general, questions for telephone in particular, as well as Internet and mail surveys, must be less complex than those used in personal interviews. Questionnaires for telephone and personal interviews should be written in a conversational style. It is particularly important that telephone surveys use easy to understand response categories. Exhibit 15.1 illustrates how a question may be revised for a different medium.

In a telephone survey about attitudes toward police services, the questionnaire not only asked about general attitudes such as how much respondents trust their local police officers and whether the police are “approachable,” “dedicated,” and so on, but also provided basic scenarios to help respondents put their expectations into words. For example, the interviewer asked respondents

Mail Form:

How satisfied are you with your community?

1. Very satisfied 2 Quite satisfied 3 Somewhat satisfied 4 Slightly satisfied 5 Neither satisfied nor dissatisfied 6 Slightly dissatisfied 7 Somewhat dissatisfied 8 Quite dissatisfied 9 Very dissatisfied

Revised for Telephone:

How satisfied are you with your community? Would you say you are very satisfied, somewhat satisfied, neither satisfied nor dissatisfied, somewhat dissatisfied, or very dissatisfied?

Very satisfied 1 Somewhat satisfied 2 Neither satisfied nor dissatisfied 3 Somewhat dissatisfied 4 Very dissatisfied 5

Source: Dillman, Don A., Mail and Telephone Surveys: The Total Design Method (New York: John Wiley & Sons, 1978), p. 209. Reproduced with permission of John Wiley & Sons, Inc.

EXHIBIT 15.1

Reducing Question Complexity by Providing Fewer Responses for Telephone Interviews

340 • PART FOUR Measurement Concepts

to imagine that someone had broken into their home and stolen items, and that the respondent called the police to report the crime. The interviewer asked how quickly or slowly the respondent expected the police to arrive.5

When a question is read aloud, remembering the alternative choices can be difficult. Consider the following question from a personal interview:

There has been a lot of discussion about the potential health risks to nonsmokers from tobacco smoke in public buildings, restaurants, and business offices. How serious a health threat to you personally is the inhaling of this secondhand smoke, often called passive smoking: Is it a very serious health threat, somewhat serious, not too serious, or not serious at all?

1. Very serious 2. Somewhat serious 3. Not too serious 4. Not serious at all 5. (Don’t know)

The last portion of the question was a listing of the four alternatives that serve as answers. This list- ing at the end is often used in interviews to remind the respondent of the alternatives, since they are not presented visually. The fifth alternative, “Don’t know,” is in parentheses because, although the interviewer knows it is an acceptable answer, it is not read. The researcher only uses this response when the respondent truly cannot provide an answer.

The data collection technique also influences the layout of the questionnaire. Layout will be discussed later in the chapter.

Guidelines for Constructing Questions Developing good business research questionnaires is a combination of art and science. Few hard- and-fast rules exist in guiding the development of a questionnaire. Fortunately, research experience has yielded some guidelines that help prevent the most common mistakes.

Avoid Complexity: Use Simple, Conversational Language Words used in questionnaires should be readily understandable to all respondents. The researcher usually has the difficult task of adopting the conversational language of people at the lower educa- tion levels without talking down to better-educated respondents. Remember, not all people have the vocabulary of a college graduate. In fact, in the U.S., less than 25 percent of the population has a bachelor’s degree.

Respondents can probably tell an interviewer whether they are married, single, divorced, separated, or widowed, but providing their marital status may present a problem. The technical jargon of top corporate executives should be avoided when surveying retailers or industrial users. “Brand image,” “positioning,” “marginal analysis,” and other corporate language may not have the same meaning for, or even be understood by, a store owner-operator in a retail survey. The vocabu- lary used in the following question from an attitude survey on social problems probably would confuse many respondents:

When effluents from a paper mill can be drunk and exhaust from factory smokestacks can be breathed, then humankind will have done a good job in saving the environment…. Don’t you agree that what we want is zero toxicity: no effluents?

Besides being too long and confusing, this question is leading. Survey questions should be short and to the point. Like this:

How risky is the stock market to invest in currently?

The stock market is too risky to invest in these days.

‘‘I don’t know the rules of grammar.... If you’re trying to persuade people to do something, or buy something, it seems to me you should use their language, the language they use every day, the language in which they think. We try to write in the vernacular.’’—DAVID OGILVY

CHAPTER 15 Questionnaire Design • 341

Avoid Leading and Loaded Questions Leading and loaded questions are a major source of bias in question wording. A leading question suggests or implies certain answers. A study of the dry cleaning industry asked this question:

Many people are using dry cleaning less because of improved wash-and-wear clothes. How do you feel wash-and-wear clothes have affected your use of dry cleaning facilities in the past 4 years?

□ Use less □ No change □ Use more

It should be clear that this question leads the respondent to report lower usage of dry cleaning. The potential “bandwagon effect” implied in this question threatens the study’s validity. Partial mention of alternatives is a variation of this phenomenon:

Do accounting graduates who attended state universities, such as Washington State University, make better auditors?

A loaded question suggests a socially desirable answer or is emotionally charged. Consider the following question from a survey about media influence on politics:6

What most influences your vote in major elections?

□ My own informed opinion □ Major media outlets such as CNN □ Newspaper endorsements □ Popular celebrity opinions □ Candidate’s physical attractiveness □ Family or friends □ Video advertising (television or web video) □ Other

The vast majority of respondents chose the first alternative. Although this question is not overly emotionally loaded, many people could be reluctant to say they are swayed by the media or advertis- ing as opposed to their independent mindset. In fact, a research question dealing with what influences decisions like these may best be done by drawing some inference based on less direct questioning.

Certain answers to questions are more socially desirable than others. For example, a truthful answer to the following classification question might be painful:

Where did you rank academically in your high school graduating class?

□ Top quarter □ Second quarter □ Third quarter □ Fourth quarter

When taking personality or psychographic tests, respondents frequently can interpret which answers are most socially acceptable even if those answers do not portray their true feelings. For exam- ple, which are the socially desirable answers to the following questions on a self-confidence scale?

I feel capable of handling myself in most social situations.

□ Agree □ Disagree

I fear my actions will cause others to have low opinions of me.

□ Agree □ Disagree

Invoking the status quo is a form of loading that results in bias because most people tend to resist change.7 An experiment conducted in the early days of polling illustrates the unpopularity of change.8 Comparable samples of respondents were simultaneously asked two questions about presidential succession. One sample was asked:

Would you favor or oppose adding a law to the Constitution preventing a president from succeeding himself more than once?

leading question

A question that suggests or implies certain answers.

loaded question

A question that suggests a socially desirable answer or is

emotionally charged.

342 • PART FOUR Measurement Concepts

The other sample was asked:

Would you favor or oppose changing the Constitution in order to prevent a president from succeeding himself more than once?

About half of respondents answered negatively to the first question. For the second question, about two out of three respondents answered negatively. Thus, the public would rather add to than change the Constitution.

The field of political research is fraught with bias. Consider the question asked by the National Republican Senatorial Committee (www.nrsc.org) on a survey:

Should foreign terrorists caught in the future or currently being held in U.S. detainment facilities be given the same legal rights and privileges as U.S. citizens?

Clearly, the authors are asking for a “No” response. A pro-Democrat pollster might word the ques- tion something like this:9

Do you believe it is acceptable for the United States to detain potentially innocent battlefield detainees without legal representation and to inhumanely interrogate them by means that violate the Geneva Con- vention and the United Nations Convention against torture?

Obviously, this question is likewise biased toward a “No” response. A more straightforward question might ask:

Does the presumption of innocence apply to suspected enemy combatants?

Asking respondents “how often” they use a product or visit a store leads them to generalize about their habits, because there usually is some variance in their behavior. In generalizing, a person is likely to portray an ideal behavior rather than an average behavior. For instance, brushing your teeth after each meal may be ideal, but busy people may skip a brushing or two. An introductory counterbiasing statement or preamble to a question that reassures respondents that their “embar- rassing” behavior is not abnormal may yield truthful responses:

Some people have time to brush three times daily but others do not. How often did you brush your teeth yesterday?

If a question embarrasses the respondent, it may elicit no answer or a biased response. This is particularly true with respect to personal or classification data such as income or education. The problem may be mitigated by introducing the section of the questionnaire with a statement such as this:

To help classify your answers, we’d like to ask you a few questions. Again, your answers will be kept in strict confidence.

A question statement may be leading because it is phrased to reflect either the negative or the positive aspects of an issue. To control for this bias, the wording of attitudinal questions may be reversed for 50 percent of the sample. This split-ballot technique is used with the expectation that two alternative phrasings of the same question will yield a more accurate total response than will a single phrasing. For example, in a study on small-car buying behavior, one-half of a sample of imported-car purchasers received a questionnaire in which they were asked to agree or disagree with the statement, “Small U.S. cars are cheaper to maintain than small imported cars.” The other half of the imported-car owners received a questionnaire in which the statement read, “Small imported cars are cheaper to maintain than small U.S. cars.”

All of these illustrations are meant as examples of one questionnaire flaw, writing questions in a manner that leads participants to respond in a way that does not accurately reflect their feelings, attitudes, or behaviors. The business researcher should read all the questions and insure that each does not contain bias.

Avoid Ambiguity: Be as Specific as Possible Items on questionnaires often are ambiguous because they are too general. Consider such indefinite words as often, occasionally, regularly, frequently, many, good, and poor. Each of these words has many different meanings. For one consumer, frequent reading of Fortune magazine may be reading

counterbiasing statement

An introductory statement or preamble to a potentially embarrassing question that reduces a respondent’s reluctance to answer by suggesting that certain behavior is not unusual.

split-ballot technique

Using two alternative phrasings of the same question for respective halves of a sample to elicit a more accurate total response than would a single phrasing.

CHAPTER 15 Questionnaire Design • 343

all 25 issues in a year, while another might think 12, or even 6 issues a year is frequent. Earlier, we used the following question as an example of a checklist question:

Please check which, if any, of the following sources of information about investments you regularly use.

What exactly does regularly mean? It can certainly vary from respondent to respondent. How exactly does hardly any differ from occasionally? Where is the cutoff? It is much better to use specific time periods whenever possible.

A brewing industry study on point of purchase advertising (store displays) asked their distribu- tors the following question:

How often does the distributor prefer to update their point of purchase advertising displays??

□ Annually (once a year) □ Semiannually (once every six months) □ Quarterly (about every three months) □ At least once monthly □ Less frequently (less often than once a year)

Here the researchers clarified the terms annually, semiannually, quarterly, and less frequently by defining them for the respondent. However, the question remained somewhat ambiguous. Beer marketers often use a variety of point-of-purchase devices to serve different purposes—in this case, what is the purpose? In addition, analysis was difficult because respondents were merely asked to indicate a preference rather than a degree of preference. Thus, the meaning of a question may not be clear because the frame of reference is inadequate for interpreting the context of the question.

A student research group asked this question:

What media do you rely on most?

□ Television □ Radio □ Internet □ Newspapers

This question is ambiguous because it does not provide information about the context. “Rely on most” for what—news, sports, entertainment? When—while getting dressed in the morn- ing, driving to work, at home in the evening? Knowing the specific circumstance can affect the choice made.

Each of these examples shows how a question can be ambiguous and interpreted differently by different individuals. While we might not be able to completely eliminate ambiguity, by using words or descriptions that have universal meaning, replacing terms with specific response catego- ries, and defining the situation surrounding the question, we can improve our business research questionnaires.

Avoid Double-Barreled Items A question covering several issues at once is referred to as a double-barreled question and should always be avoided. Making the mistake of asking two questions rather than one is easy—for example, “Do you feel our hospital emergency room waiting area is clean and comfortable?” What do we learn from this question? If the respondent responds positively, we could likely infer that our wait- ing area is clean and comfortable. However, if the response is negative, is it because the room is not clean, or not comfortable? Or both? Certainly for a manager to make improvements it is important to know which element needs attention. When multiple questions are asked in one question, the results may be exceedingly difficult to interpret.

One of the questions we presented earlier when discussing fixed-alternative questions provides a good example of a double-barreled question:

Did your plant use any commercial feed or supplement for livestock or poultry in 2010?

□ Yes □ No

double-barreled question

A question that may induce bias because it covers two

issues at once.

344 • PART FOUR Measurement Concepts

Here, the question could actually be thought of as a “double-double-barreled” question. Both commercial feed or supplement and livestock or poultry are double-barreled. Interpreting the answer to this question would be challenging.

The following comment offers good advice regarding double-barreled questions:

Generally speaking, it is hard enough to get answers to one idea at a time without complicating the prob- lem by asking what amounts to two questions at once. If two ideas are to be explored, they deserve at least two questions. Since question marks are not rationed, there is little excuse for the needless confusion that results [from] the double-barreled question.10

A researcher is well served to carefully examine any survey question that includes the words and or or. While sometimes words such as these may be used to reinforce or clarify a question, they are often a sign of a double-barreled question. If you have two (or three) questions, ask them separately, not all together.

Avoid Making Assumptions Consider the following question:

Should General Electric continue to pay its outstanding quarterly dividends?

□ Yes □ No

This question has a built-in assumption: that people believe the dividends paid by General Electric are outstanding. By answering “yes,” the respondent implies that the program is, in fact, outstanding and that things are fine just as they are. When a respondent answers “No,” he or she implies that GE should discontinue the dividends. The researchers should not place the respondent in that sort of bind by including an implicit assumption in the question.

Another frequent mistake is assuming that the respondent had previously thought about an issue. For example, the following question appeared in a survey concerning Jack-in-the-Box: “Do you think Jack-in-the-Box restaurants should consider changing their name?” Respondents have not likely thought about this question beforehand. Most respondents answered the question even though they had no prior opinion concerning the name change. Research that induces people to express attitudes on subjects they do not ordinarily think about is rather meaningless.

Avoid Burdensome Questions That May Tax the Respondent’s Memory A simple fact of human life is that people forget. Researchers writing questions about past behavior or events should recognize that certain questions may make serious demands on the respondent’s memory. Writing questions about prior events requires a conscientious attempt to minimize the problems associated with forgetting.

In many situations, respondents cannot recall the answer to a question. For example, a tele- phone survey conducted during the 24-hour period following the airing of the Super Bowl might establish whether the respondent watched the Super Bowl and then ask, “Do you recall any com- mercials on that program?” If the answer is positive, the interviewer might ask, “What brands were advertised?” These two questions measure unaided recall, because they give the respondent no clue as to the brand of interest.

If the researcher suspects that the respondent may have forgotten the answer to a question, he or she may rewrite the question in an aided-recall format—that is, in a format that provides a clue to help jog the respondent’s memory. For instance, the question about an advertised beer in an aided-recall format might be, “Do you recall whether there was a brand of beer advertised on that program?” or “I am going to read you a list of beer brand names. Can you pick out the name of the beer that was advertised on the program?” While aided recall is not as strong a test of attention or memory as unaided recall, it is less taxing to the respondent’s memory.

Telescoping and squishing are two additional consequences of respondents’ forgetting the exact details of their behavior. Telescoping error occurs when respondents believe that past events

‘‘“How am I to get in?” asked Alice again, in a louder tone. “Are you to get in at all?” said the Footman, “That’s the first question, you know.”’’—LEWIS CARROLL, ALICE’S ADVENTURES IN WONDERLAND

CHAPTER 15 Questionnaire Design • 345

happened more recently than they actually did. For instance, most people will estimate that they have changed the oil in their car more recently than they actually have. The opposite effect, squish- ing error, occurs when respondents think that recent events took place longer ago than they really did. A solution to this problem may be to refer to a specific event that is memorable—for example, “How often have you gone to a sporting event since the World Series?” Because forgetting tends to increase over time, the question may concern a recent period: “How often did you watch HBO on cable television last week?” During pretesting or the questionnaire editing stage, the most appropriate time period can be determined.

In situations in which “I don’t know” or “I can’t recall” is a meaningful answer, simply including a “don’t know” response category may solve the question writer’s problem.

Make Certain Questions Generate Variance We want our variables to vary! It is important that the response categories provided cover the breadth of possibilities (totally exhaustive), but also critical that they yield variance across respon- dents. In many ways, if all of the respondents check the same box, we have not generated usable information.

For example, the U.S. census uses the following age categories:

Under 5 years 5 to 9 years 10 to 14 years 15 to 19 years 20 to 24 years 25 to 29 years . . . . . . . . . . . . 95 to 99 years 100 years and over

While these five-year age categories do capture the range of ages and provide rather detailed census information regarding the general population, what would happen if they were used for a survey of undergraduate students? In many institutions, 95 percent or more of the respondents would fall into two groups. What might be more appropriate and provide better information in a study of undergraduates?

When we discussed measurement issues in Chapter 13, we noted that there were benefits from constructing scaled responses with a larger number of response categories rather than fewer. In general, this is a good rule, with seven- or ten-point scales likely providing greater variance than three- or four-point scales. In practice, it is also often better to use a scaled response than a dichotomous response form. For example, our earlier example of a simple-dichotomy (dichoto- mous) question asked:

Did you have any overnight travel for work-related activities last month?

□ Yes □ No

While the respondent could likely answer this question and we may simply desire to place respondents into either the “did travel” or “did not travel” category, we really do not gain much information from this question. It fails to discriminate at all between employees that travel once a month, twice a month, or were gone for 25 days last month. It is likely that these employees have different attitudes and needs regarding business travel. A better approach might be to create multiple categories (0, 1–5, 6–10, 11–15, 16–20, 21–25, 26+ nights) or ask for a specific number of nights away on business travel. From this, we could always recode the respondents into the nominal data categories of yes/no if needed. However, if we collect yes/no data to begin with, we cannot make more detailed distinctions later.

In other situations, we might need to change the wording of a question to increase variance. If we were using a Likert scale (Strongly Disagree to Strongly Agree), it might be better to ask the customer to respond to the statement “Edward Jones provides excellent advice for investors” rather

346 • PART FOUR Measurement Concepts

than “Edward Jones provides good advice for investors.” The point is not to generate a specific score, but to create variance that allows us to examine investors with different attitudes.

It is important for our questions to generate variance. In a perfect world, our questions would result in something close to a normal distribution.

What Is the Best Question Sequence? The order of questions, or the question sequence, may serve several functions for the researcher. If the opening questions are interesting, simple to comprehend, and easy to answer, respondents’ cooperation and involvement can be maintained throughout the questionnaire. Asking easy-to- answer questions teaches respondents their role and builds their confidence.

A mail survey among department store buyers drew an extremely poor return rate. A substan- tial improvement in response rate occurred, however, when researchers added some introductory questions seeking opinions on pending legislation of great importance to these buyers. Respon- dents continued on to complete all the questions, not only those in the opening section.

In their attempt to “warm up” respondents toward the questionnaire, student researchers frequently ask demographic or classification questions at the beginning of the survey. This gener- ally is not advisable, because asking for personal information such as income level or education may embarrass or threaten respondents. Asking these questions at the end of the questionnaire usually is better, after rapport has been established between respondent and interviewer.

Order bias can result from a particular answer’s position in a set of answers or from the sequenc- ing of questions. In political elections in which candidates lack high visibility, such as elections for county commissioners and judges, the first name listed on the ballot often receives the highest per- centage of votes. For this reason, many election boards print several ballots so that each candidate’s name appears in every possible position on the ballot.

Order bias can also distort survey results. For example, suppose a questionnaire’s purpose is to measure levels of awareness of several charitable organizations. If Big Brothers Big Sisters is always mentioned first, the American Red Cross second, and the American Cancer Society third, Big Brothers Big Sisters may receive an artificially high awareness rating because respondents are prone to yea-saying (by indicating awareness of the first item in the list).

Asking specific questions before asking about broader issues is a common cause of order bias. For example, people who are first asked, “Are you satisfied with your marriage?” will respond dif- ferently to a follow-up question that asks, “Are you satisfied with your life?” than if the questions are asked in the reverse order. Generally, researchers should ask general questions before specific questions. This procedure, known as the funnel technique, allows the researcher to understand the respondent’s frame of reference before asking more specific questions about the level of the respon- dent’s information and the intensity of his or her opinions.

Consider how later answers might be biased by previous questions in this questionnaire on environmental pollution:

Please consider each of the following issues. Circle the number for each that best indicates your feelings about the severity of that issue as an environmental problem:

order bias

Bias caused by the influence of earlier questions in a questionnaire or by an answer’s position in a set of answers.

funnel technique

Asking general questions before specific questions in order to obtain unbiased responses.

Issue Not At All A Problem Very Severe Problem

Air pollution from automobile exhausts 1 2 3 4 5

Air pollution from open burning 1 2 3 4 5

Air pollution from industrial smoke 1 2 3 4 5

Air pollution from foul odors 1 2 3 4 5

Noise pollution from airplanes 1 2 3 4 5

Noise pollution from cars, trucks, motorcycles 1 2 3 4 5

Noise pollution from industry 1 2 3 4 5

CHAPTER 15 Questionnaire Design • 347

348

filter question

A question that screens out respondents who are not

qualified to answer a second question.

pivot question

A filter question used to determine which version of a

second question will be asked.

RESEARCH SNAPSHOT

Climate change as a result of global warming and the role that human behavior may play in it is a topic that is frequently fea- tured in the news, especially in stories related to science and technology. Scientists at the Commonwealth Scientific and Industrial Research Organization (CSIRO), Climate Adaptation Research Program in Australia have dedicated themselves to researching the implications of climate change. As a result, the group conducted an online survey of 5,000 Australians, as part of an ongoing assessment of public opinion. Their questionnaire examined how Australians viewed the causes of climate change overall, what entities they believe they trust when it comes to information about climate change, and the degree to which they engaged in behaviors that are environmentally-friendly.

Despite the size of their study, CSIRO researchers found that there is considerable confusion about what Australians believe is the underlying causes of climate change. While there are a significant number of respondents that believe that climate change is a real problem, and governments and other global organizations are responsible for it, they found that many Australians are unsure about these relationships. Also, there appeared to be clear differences between those who believe

that climate change is happening, and is simply a naturally occurring cycle of the Earth’s climate, versus those that believe that climate change is human-induced.

The results of the study suggest that further research on human attitudes about climate change is necessary. Researchers should perhaps clarify the relationships between beliefs and attitudes towards climate change and subsequent behaviors, in an effort to link these conceptually. As a result, questionnaire items will have to be further developed to demonstrate empirically what is developed theoretically concerning this global phenomenon.

Source: Based on Leviston, Z. and I. A. Walker, “Baseline Survey of Australian Attitudes to Climate Change: Preliminary Report,” Commonwealth Scientific and Industrial Research Organisation (CSIRO) (January 2011).

What Citizens Think about Climate Change

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Not surprisingly, researchers found that the responses to the air pollution questions were highly correlated—in fact, almost identical. What if the first issue was foul odors instead of automobile exhausts? Do you think it would affect the remaining responses?

With attitude scales, there also may be an anchoring effect. The first concept measured tends to become a comparison point from which subsequent evaluations are made. Randomization of items on a questionnaire susceptible to the anchoring effect helps minimize order bias.

A related problem is bias caused by the order of alternatives on closed questions. To avoid this problem, the order of these choices should be rotated if producing alternative forms of the ques- tionnaire is possible. Unfortunately, business researchers rarely print alternative questionnaires to eliminate problems resulting from order bias. With Internet surveys, however, reducing order bias by having the computer randomly order questions and/or response alternatives is quite easy. With complete randomization, question order is random and respondents see response alternatives in different positions.

Asking a question that does not apply to the respondent or that the respondent is not qualified to answer may be irritating or cause a biased response because the respondent wishes to please the interviewer or to avoid embarrassment. Including a filter question minimizes the chance of asking questions that are inapplicable. Asking a human resource manager, “How would you rate the third party administrator (TPA) of your employee health plan?” may elicit a response even though the organization does not utilize a TPA. The respondent may wish to please the interviewer with an answer. A filter question such as, “Does your organization use a third party administrator (TPA) for your employee health plan?” followed by, “If you answered Yes to the previous question, how would you rate your TPA on … ?” would screen out the people who are not qualified to answer. If embedded in the questionnaire, this would create the need for a skip question for those that did not use a TPA as discussed below.

Another form of filter question, the pivot question, can be used to obtain income information and other data that respondents may be reluctant to provide. For example,

“Is your total family income over or under $50,000?” IF UNDER, ASK, “Is it over or under $25,000?” IF OVER, ASK, “Is it over or under $75,000?”

Under $25,000 $50,001–$75,000

$25,001–$50,000 Over $75,000

Exhibit 15.2 gives an example of a flowchart plan for a questionnaire. Structuring the order of the questions so that they are logical will help to ensure the respondent’s cooperation and eliminate confusion or indecision. The researcher maintains legitimacy by making sure that the respondent can comprehend the relationship between a given question (or section of the questionnaire) and the overall purpose of the study. Furthermore, a logical order may aid the individual’s memory. Informational and transitional comments explaining the logic of the questionnaire may ensure that the respondent continues. Here are two examples:

We have been talking so far about general shopping habits in this city. Now I’d like you to compare two types of grocery stores—regular supermarkets and grocery departments in wholesale club stores.

EXHIBIT 15.2

Flow of Questions to Determine the Role of Work-school Balance for University Students

Are you currently in school?

Are you currently employed?

Are you looking for work?

Yes

No Are you fulltime? Are you part-time?

Series of questions on what you are looking for in terms of work-school balance

Series of questions on your current experiences regarding work-school balance

End survey

End survey End survey

End surveyNo

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CHAPTER 15 Questionnaire Design • 349

So that I can combine your answers with those of other plant managers who are similar to you, I need some personal information about you. Your answers to these questions—just as all of the others you’ve answered—are confidential, and you will never be identified individually. Thanks for your help so far. If you’ll answer the remaining questions, it will help me analyze all your answers.

What Is the Best Layout? Good layout and physical attractiveness are crucial in mail, Internet, and other self-administered questionnaires. For different reasons, a good layout in questionnaires designed for personal and telephone interviews is also important.

Traditional Questionnaires Exhibit 15.3 shows a page from a telephone questionnaire. The layout is neat and organized, and the instructions for the interviewer (all boldface capital letters) are easy to follow. The responses, “It Depends,” “Refused,” and “Don’t Know” are enclosed in a box to indicate that these answers are acceptable but responses from the five-point scale are preferred.

Often rate of return can be increased by using money that might have been spent on an incen- tive to improve the attractiveness and quality of the questionnaire. Mail questionnaires should never be overcrowded. Margins should be of decent size, white space should be used to separate blocks of print, and the unavoidable columns of multiple boxes should be kept to a minimum. A question should not begin on one page and end on another page. Splitting questions may cause a respondent to read only part of a question, to pay less attention to answers on one of the pages, or to become confused.

Questionnaires should be designed to appear as short as possible. Sometimes it is advisable to use a booklet form of questionnaire rather than stapling a large number of pages together. In situ- ations in which it is necessary to conserve space on the questionnaire or to facilitate data entry or tabulation of the data, a multiple-grid layout may be used. The multiple-grid question presents several similar questions and corresponding response alternatives arranged in a grid format. For example,

Airlines often offer special fare promotions, but they may require connecting flights. On a vacation trip, how often would you take a connecting flight instead of a nonstop flight if you could save $100 a ticket, but the connecting flight was longer?

multiple-grid question

Several similar questions arranged in a grid format.

Never Rarely Sometimes Often Always

Complete trip is one hour longer? □ □ □ □ □

Complete trip is two hours longer? □ □ □ □ □

Complete trip is three hours longer? □ □ □ □ □

Experienced researchers have found that the title of a questionnaire should be phrased care- fully. In self-administered and mail questionnaires, a carefully constructed title may capture the respondent’s interest, underline the importance of the research (“Nationwide Study of Blood Donors”), emphasize the interesting nature of the study (“Study of Internet Usage”), appeal to the respondent’s ego (“Survey of Top Executives”), or emphasize the confidential nature of the study (“A Confidential Survey of Physicians”). At the same time, the researcher should take steps to ensure that the wording of the title will not bias the respondent in the same way that a leading question might.

By using several forms, special instructions, and other tricks of the trade, the researcher can design the questionnaire to facilitate the interviewer’s job of following interconnected questions.

350 • PART FOUR Measurement Concepts

EXHIBIT 15.3

Layout of a Page from a Telephone Questionnaire

5. Now I’m going to read you some types of professions. For each one, please tell me whether you think the work that profession does, on balance, has a very positive impact on society, a somewhat positive impact, a somewhat negative impact, a very negative impact, or not much impact either way on society. First … (START AT X’D ITEM. CONTINUE DOWN AND UP THE LIST UNTIL ALL ITEMS HAVE BEEN READ AND RATED.)

(DO NOT READ)

START HERE:

Very positive Impact

Some- what positive Impact

Some- what negative Impact

Very Negative Impact

Not Much Impact

It Depends Refused

Don’t Know

[ ] Members of Congress 1 2 3 4 5 0 X Y (24)

[X] Business executives 1 2 3 4 5 0 X Y (25)

[ ] Physicians 1 2 3 4 5 0 X Y (26)

[ ] Political pollsters—that is, people who conduct surveys for public officials or political candidates

1 2 3 4 5 0 X Y (27)

[ ] Researchers in the media— that is, people in media such as television, newspapers, magazines, and radio, who conduct surveys about issues later reported in the media

1 2 3 4 5 0 X Y (28)

[ ] Telemarketers—that is, people who sell products or services over the phone

1 2 3 4 5 0 X Y (29)

[ ] Used car salesmen 1 2 3 4 5 0 X Y (30)

[ ] Market researchers—that is, people who work for commercial research firms who conduct surveys to see what the public thinks about certain kinds of consumer products or services

1 2 3 4 5 0 X Y (31)

[ ] Biomedical researchers 1 2 3 4 5 0 X Y (32)

[ ] Public-opinion researchers— that is, people who work for commercial research firms who conduct surveys to see what the public thinks about important social issues

1 2 3 4 5 0 X Y (33)

[ ] College and university professors

1 2 3 4 5 0 X Y (34)

[ ] Attorneys 1 2 3 4 5 0 X Y (35)

[ ] Members of the clergy 1 2 3 4 5 0 X Y (36)

[ ] Journalists 1 2 3 4 5 0 X Y (37)

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CHAPTER 15 Questionnaire Design • 351

Exhibit 15.4 and Exhibit 15.5 illustrate portions of telephone and personal interview question- naires. Note how the layout and easy-to-follow instructions for interviewers in questions 1, 2, and 3 of Exhibit 15.4 help the interviewer follow the question sequence.

Instructions are often capitalized or printed in bold to alert the interviewer that it may be necessary to proceed in a certain way. For example, if a particular answer is given, the interviewer

EXHIBIT 15.4

Telephone Questionnaire with Skip Questions

1. Did you take the car you had checked to the Standard Auto Repair Center for repairs?

–1 Yes (SKIP TO Q. 3) –2 No

2. (IF NO, ASK:) Did you have the repair work done?

–1 Yes➔ –2 No➔ 1. Where was the repair work done?_______________________

_______________________________________________________ 1. Why didn’t you have the car repaired?

__________________________________________________________ __________________________________________________________ __________________________________________________________2. Why didn’t you have the repair work done

at the Standard Auto Repair Center?_____________________ ________________________________________________________

________________________________________________________ _______________________________________________________

3. (IF YES TO Q. 1, ASK:) How satisfied were you with the repair work? Were you …

–1 Very satisfied

–2 Somewhat satisfied

–3 Somewhat dissatisfied

–4 Very dissatisfied

(IF SOMEWHAT OR VERY DISSATISFIED:) In what way were you dissatisfied? _______________________________________________________________________________________________________________________ _______________________________________________________________________________________________________________________ _______________________________________________________________________________________________________________________

4. (ASK EVERYONE:) Do you ever buy gas at the 95th Street Standard Center?

–1 Yes –2 No (SKIP TO Q. 6)

5. (IF YES, ASK:) How often do you buy gas there?

–1 Always

–2 Almost always

–3 Most of the time

–4 Part of the time

–5 Hardly ever

6. Have you ever had your car washed there?

–1 Yes –2 No

7. Have you ever had an oil change or lubrication done there?

–1 Yes –2 No

Source: Reprinted with permission from the Council of American Survey Research, http://www.casro.org.

352 • PART FOUR Measurement Concepts

EXHIBIT 15.5

Personal Interview Questionnaire

“Hello, my name is ____ . I’m a Public Opinion Interviewer with Research Services, Inc. We’re making an opinion survey about banks and banking, and I’d like to ask you…”

1. What are the names of local banks you can think of offhand? (INTERVIEWER: List names in order mentioned.) a. __________________________________________________________________________________________________________________

b. __________________________________________________________________________________________________________________

c. __________________________________________________________________________________________________________________

d. __________________________________________________________________________________________________________________

e. __________________________________________________________________________________________________________________

f. __________________________________________________________________________________________________________________

g. __________________________________________________________________________________________________________________

2. Thinking now about the experiences you have had with the different banks here in Boulder, have you ever talked to or done business with …(INTERVIEWER: Insert name of bank checked in red below.) a. Are you personally acquainted with any of the employees or officers at ___________________________________________?

b. (If YES) Who is that ___________________________________________________________________________________________?

c. How long has it been since you have been inside _______________________________________________________________?

(INTERVIEWER: Now go back and repeat 2–2c for all other banks listed.)

(2) Talked

(2a and 2b) Known Employee

or Officer (2c)

Been in Bank in:

Yes No No Name Last Year 1-5 5-Plus No DK

Arapahoe National Bank 1 2 1 ________________ 1 2 3 4 5

First National Bank 1 2 1 ________________ 1 2 3 4 5

Boulder National Bank 1 2 1 ________________ 1 2 3 4 5

Security Bank 1 2 1 ________________ 1 2 3 4 5

United Bank of Boulder 1 2 1 ________________ 1 2 3 4 5

National State Bank 1 2 1 ________________ 1 2 3 4 5

3. (HAND BANK RATING CARD) On this card there are a number of contrasting phrases or state-ments—for example, “Large” and “Small.” We’d like to know how you rate (NAME OF BANK CHECKED IN RED BELOW) in terms of these statements or phrases. Just for example, let’s use the terms “fast service” and “slow service.” If you were to rate a bank #1 on this scale, it would mean you find their service “very fast.” On the other hand, a 7 rating would indicate you feel their service is “very slow,” whereas a 4 rating means you don’t think of them as being either “very fast” or “very slow.” Are you ready to go ahead? Good! Tell me then how you would rate (NAME OF BANK CHECKED IN RED) in terms of each of the phrases or statements on that card. How about (READ NEXT BANK NAME)? … (INTERVIEWER: Continue on until respondent has evaluated all six banks.)

Arapahoe National

First National

Boulder National

Security Bank

United Bank

National State

a. Service ____________ ____________ ____________ ____________ ____________ _____________ b. Size ____________ ____________ ____________ ____________ ____________ ____________ c. Business vs. Family ____________ ____________ ____________ ____________ ____________ ____________ d. Friendliness ____________ ____________ ____________ ____________ ____________ ____________ e. Big/Small Business ____________ ____________ ____________ ____________ ____________ ____________ f. Rate of Growth ____________ ____________ ____________ ____________ ____________ ____________ g. Modernness ____________ ____________ ____________ ____________ ____________ ____________ h. Leadership ____________ ____________ ____________ ____________ ____________ ____________ i. Loan Ease ____________ ____________ ____________ ____________ ____________ ____________ j. Location ____________ ____________ ____________ ____________ ____________ ____________ k. Hours ____________ ____________ ____________ ____________ ____________ ____________ l. Ownership ____________ ____________ ____________ ____________ ____________ ____________ m. Community

Involvement ____________ ____________ ____________ ____________ ____________ ____________

National State Bank 6 Other (Specify) ________________________________________ DK/Wouldn’t 9

(Continued )

CHAPTER 15 Questionnaire Design • 353

4. Suppose a friend of yours who has just moved to Boulder asked you to recommend a bank. Which local bank would you recommend? Why would you recommend that particular bank?

Arapahoe National 1 First National 2 Boulder National 3 Security Bank 4 United Bank of Boulder 5 National State Bank 6 Other (Specify) ___________________________ DK/Wouldn’t 9

5. Which of the local banks do you think of as: (INTERVIEWER: Read red-checked item first, then read each of the other five.)

the newcomer’s bank?__________________________________________________________________________________________________

the student’s bank?__________________________________________________________________________________________________________

the Personal Banker bank?_____________________________________________________________________________________________________

the bank where most C.U. faculty and staff bank?____________________________________________________________________________

the bank most interested in this community?_________________________________________________________________________________

the most progressive bank?__________________________________________________________________________________________________

6. Which of these financial institutions, if any, (HAND CARD 2) are you or any member of your immediate family who lives here in this home doing business with now?

Bank 1 Credit Union 2 Finance Company 3 Savings and Loan 4 Industrial Bank 5 None of these 6 DK/Not sure 7

(IF NONE, Skip to 19.)

7. If a friend asked you to recommend a place where he or she could get a loan with which to buy a home, which financial institution would you probably recommend? (INTERVIEWER: Probe for specific name.) Why would you recommend (INSTITUTION NAMED)?

Would Recommend: ____________________________________________________ Wouldn’t 0

DK/Not Sure 9

Source: Reprinted with permission from the Council of American Survey Research, http://www.casro.org.

or respondent may be instructed to skip certain questions or go to a special sequence of questions. To facilitate coding, question responses should be precoded when possible, as in Exhibit 15.4.

Exhibit 15.5 illustrates some other useful techniques that are possible with personal inter- views. Questions 3 and 6 instruct the interviewer to hand the respondent a card bearing a list of alternatives. Cards may help respondents grasp the intended meaning of the question and remem- ber all the brand names or other items they are being asked about. Also, questions 2, 3, and 6 instruct the interviewer that rating of the banks will start with the bank that has been checked in red pencil on the printed questionnaire. The name of the red-checked bank is not the same on every questionnaire. By rotating the order of the check marks, the researchers attempted to reduce order bias caused by respondents’ tendency to react more favorably to the first set of questions.

Exhibit 15.6 illustrates a series of questions that includes a skip question. Either skip instructions or an arrow drawn pointing to the next question informs the respondent which question comes next.

Layout is extremely important when questionnaires are long or require the respondent to fill in a large amount of information. In many circumstances, using headings or subtitles to indicate

EXHIBIT 15.5 (Continued)

Personal Interview Questionnaire

354 • PART FOUR Measurement Concepts

1. If you had to buy a computer tomorrow, which of the following three types of computers do you think you would buy?

1 Desktop—Go to Q. 3 2 Laptop—Go to Q. 3

3 Palm-sized (PDA)

2. (If “Palm-sized” on Q. 1, ask): What brand of computer do you think you would buy?

3. What is your age?

EXHIBIT 15.6

Example of a Skip Question

groups of questions will help the respondent grasp the scope or nature of the questions to be asked. Thus, at a glance, the respondent can follow the logic of the questionnaire.

Internet Questionnaires Layout is also an important issue for questionnaires appearing on the Internet. A questionnaire on a website should be easy to use, flow logically, and have a clean look and overall feel that motivate the respondent to cooperate from start to finish. Many of the guidelines for layout of paper ques- tionnaires apply to Internet questionnaires. There are, however, some important differences.

With graphical user interface (GUI) software, the researcher can exercise control over the background, colors, fonts, and other visual features displayed on the computer screen so as to create an attractive and easy-to-use interface between the computer user and the Internet survey. GUI software allows the researcher to design questionnaires in which respondents click on the appropri- ate answer rather than having to type answers or codes.

There are a large number of web publishing software packages (e.g., WebSurveyor, FrontPage, etc.) and web survey host sites (such as www.zoomerang.com and www.surveymonkey.com) to assist a researcher with Internet data collection. However, several features of a respondent’s computer may influence the appearance of an Internet questionnaire. For example, discrepancies between the design- er’s and the respondent’s computer settings for screen configuration (e.g., 1,024 × 768 pixels ver- sus 1,280 × 800 pixels) may result in questions not being fully visible on the respondent’s screen, misaligned text, or other visual problems. The possibility that the questionnaire the researcher/designer constructs on his or her com- puter may look different from the questionnaire that appears on the respondent’s computer should always be considered when design- ing Internet surveys. One sophisticated remedy is to use the first few questions on an Internet survey to ask about operating system, browser software, and other computer configuration issues so that the questionnaire that is delivered is as compatible as possible with the respondent’s computer. A simpler solution is to limit the hori- zontal width of the questions to 70 characters or less, to decrease the likelihood of wrap-around text.

Layout Issues Even if the questionnaire designer’s computer and the respon- dents’ computers are compatible, a web questionnaire designer should consider several layout issues. The first decision is whether the questionnaire will appear page by page, with individual ques- tions or groups of questions on separate screens (web pages), or on a scrolling basis, with the entire questionnaire appearing on a single web page that the respondent scrolls from top to bot- tom. The paging layout (going from screen to screen) greatly facilitates skip patterns. Based on a respondent’s answers to filter questions, the computer can automatically insert relevant ques- tions on subsequent pages. If the entire questionnaire appears on

Web-based software can generally adjust to a user’s browser and make for a neat appearance.

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CHAPTER 15 Questionnaire Design • 355

one page (the scrolling layout), the display should advance smoothly, as if it were a piece of paper being moved up or down. The scrolling layout gives the respondent the ability to read any por- tion of the questionnaire at any time, but the absence of page boundaries can cause problems. For example, suppose a Likert scale consists of 15 statements in a grid-format layout, with the response categories Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree at the beginning of the questionnaire. Once the respondent has scrolled down beyond the first few statements, he or she may not be able to see both the statements at the end of the list and the response categories at the top of the grid simultaneously. Thus, avoiding the problems associated with splitting ques- tions and response categories may be difficult with scrolling questionnaires.

When a scrolling questionnaire is long, category or section headings are helpful to respondents. It is also a good idea to provide links to the top and bottom parts of each section, so that users can navigate through the questionnaire without having to scroll through the entire document.11

Whether a web survey is page-by-page or scrolling format a push button with a label should clearly describe the actions to be taken. For example, if the respondent is to go to the next page, a large arrow labeled “NEXT” might appear in color at the bottom of the screen.

Decisions must be made about the use of color, graphics, animation, sound, and other special features that the Internet makes possible. One point to remember is that, although sophisticated graphics are not a problem for most people with powerful computers and high speed Internet, many respondents’ computers and/or Internet connections are not powerful enough to deliver complex graphics at a satisfactory speed.

With a paper questionnaire, the respondent knows how many questions he or she must answer. Because many Internet surveys offer no visual clues about the number of questions to be asked, it is important to provide a status bar or some other visual indicator of questionnaire length. For example, including a partially filled rectangular box as a visual symbol and a statement such as, “The status bar at top right indicates approximately what portion of the survey you have com- pleted” increases the likelihood that the respondent will finish the entire sequence of questions. Exhibit 15.7 shows a question from an online survey that uses a simple and motivating design. The survey presents one question at a time for simplicity. So that respondents can see their progress toward the end of the questionnaire, a gauge in the upper right corner fills from left to right as the respondent proceeds from Start to Finish.

An Internet questionnaire uses dialog boxes to display questions and record answers. Exhibit 15.8 portrays four common ways of displaying questions on a computer screen. Many Internet question- naires require the respondent to activate his or her answer by clicking on the radio button for a response. Radio buttons work like push buttons on automobile radios: clicking on an alternative response deacti- vates the first choice and replaces it with the new response. A drop-down box, such as the one shown in Exhibit 15.8, is a space-saving device that allows the researcher to provide a list of responses that are hidden from view until they are needed. A general statement, such as “Please select” or “Click here,” is shown initially. Clicking on the downward-facing arrow makes the full range of choices appear.

Checklist questions may be followed by checkboxes, several, none, or all of which may be checked by the respondent. Open-ended boxes are boxes in which respondents type their answers to open-ended questions. Open-ended boxes may be designed as one-line text boxes or scrolling text boxes, depending on the breadth of the expected answer. Of course, open-ended questions require that respondents have both the skill and the willingness to keyboard lengthy answers on

push button

In a dialog box on an Internet questionnaire, a small outlined

area, such as a rectangle or an arrow, that the respondent

clicks on to select an option or perform a function, such as

submit.

status bar

In an Internet questionnaire, a visual indicator that tells

the respondent what portion of the survey he or she has

completed.

radio button

In an Internet questionnaire, a circular icon, resembling a button, which activates one response choice and

deactivates others when a respondent clicks on it.

drop-down box

In an Internet questionnaire, a space-saving device that

reveals responses when they are needed but otherwise hides

them from view.

checkboxes

In an Internet questionnaire, small graphic boxes, next to answers, that a respondent

clicks on to choose an answer; typically, a check mark or an X

appears in the box when the respondent clicks on it.

open-ended boxes

In an Internet questionnaire, boxes where respondents can type in their own answers to

open-ended questions.

EXHIBIT 15.7

Question in an Online Screening Survey for Joining a Consumer Panel

Source: J. D. Power and Associates, “JDPower Panel,”

356 • PART FOUR Measurement Concepts

EXHIBIT 15.8

Alternative Ways of Displaying Internet Questions

Last month, did you purchase products or services over the Internet?

Yes

How familiar are you with Microsoft’s Xbox video game player? Know Know Know Know Never Extremely Fairly a Just Heard Well Well Little Name of

Click Here

What company do you think is the most visible sponsor of sports?

What can we do to improve our textbook?

In which country or region do you currently reside?

Click Here Click Here United States Asia/Pacific (excluding Hawaii) Africa Australia or New Zealand Canada Europe Latin America, South America, or Mexico Middle East Other

From which location(s) do you access the Internet? Select all that apply. Home Work Other Location

Please indicate which of the following websites you have ever visited or used. (CHOOSE ALL THAT APPLY.) E*Trade’s websites Waterhouse’s websites Merrill Lynch’s websites Fidelity’s websites Schwab’s websites Powerstreet Yahoo! Finance Quicken.com Lycos Investing AOL’s Personal Finance None of the above

Radio button

Drop-down box, closed position

Drop-down box, open position

Check box

Open-ended, one-line box

Open-ended, scrolling text box

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CHAPTER 15 Questionnaire Design • 357

the computer. Some open-ended boxes are designed so that respondents can enter numbers for frequency response, ranking, or rating questions. For example,

Below you will see a series of statements that might or might not describe how you feel about your career. Please rate each statement using a scale from 1 to 5, where 1 means “Totally Disagree,” 2 means “Somewhat Disagree,” 3 means “Neither Agree nor Disagree,” 4 means “Somewhat Agree,” and 5 means “Totally Agree.” Please enter your numeric answer in the box provided next to each statement. Would you say that …

A lack of business knowledge relevant to my field/career could hurt my career advancement.

My career life is an important part of how I define myself.

I am seriously considering a change in careers.

Pop-up boxes are message boxes that can be used to highlight important information. For example, pop-up boxes may be used to provide a privacy statement, such as the following:

IBM would like your help in making our website easier to use and more effective through an online survey. Your participation is voluntary and your responses are anonymous. If you choose not to participate, select “No thanks” and you will continue to the website.

Clicking on “Yes” opens the following pop-up box:

Survey Privacy Statement

This overall privacy statement verifies that IBM is a member of the TRUSTe program and is in compli- ance with TRUSTe principles. This survey is strictly for market research purposes. The information you provide will be used only to improve the overall content, navigation, and usability of ibm.com.

In some cases, respondents can learn more about how to use a particular scale or get a defini- tion of a term by clicking on a link, which generates a pop-up box. One of the most common reasons for using pop-up boxes is error trapping, a topic discussed in the next section.

Chapter 14 described graphic rating scales, which present respondents with a graphic contin- uum. On the Internet, researchers can take advantage of scroll bars or other GUI software features to make these scales easy to use. For example, the graphic continuum may be drawn as a measuring rod with a plus sign on one end and a minus sign on the other. The respondent then moves a small rectangle back and forth between the two ends of the scale to scroll to any point on the continuum. Scoring, as discussed in Chapter 14, is in terms of some measure of the length (millimeters) from one end of the graphic continuum to the point marked by the respondent.

Finally, researchers should include a customized thank-you page at the end of an Internet questionnaire, so that a brief thank-you note pops onto respondents’ screens when they click on the Submit push button.12

Software That Makes Questionnaires Interactive Computer code can be written to make Internet questionnaires interactive and less prone to errors. The writing of software programs is beyond the scope of this discussion. However, several of the interactive functions that software makes possible should be mentioned here.

Internet software allows the branching off of questioning into two or more different lines, depending on a particular respondent’s answer, and the skipping or filtering of questions. Questionnaire-writing software with skip and branching logic is readily available. Most of these programs have hidden skip logic so that respondents never see any evidence of skips. It is best if the questions the respondent sees flow in numerical sequence. However, some programs number all potential questions in numerical order, and the respondent sees only the numbers on the ques- tions he or she answers. Thus, a respondent may answer questions 1 through 11 and then next see a question numbered 15 because of the skip logic.

Software can systematically or randomly manipulate the questions a respondent sees. Variable piping software allows variables, such as answers from previous questions, to be inserted into unfolding questions. Other software can randomly rotate the order of questions, blocks of ques- tions, and response alternatives from respondent to respondent.

pop-up boxes

In an Internet questionnaire, boxes that appear at selected

points and contain information or instructions for respondents.

variable piping software

Software that allows variables to be inserted into an Internet questionnaire as a respondent

is completing it.

358 • PART FOUR Measurement Concepts

359

Researchers can also use software to control the flow of a questionnaire. Respondents can be blocked from backing up, or they can be allowed to stop in mid-questionnaire and come back later to finish. A questionnaire can be designed so that if the respondent fails to answer a ques- tion or answers it with an incorrect type of response, an immediate error message appears. This is called error trapping. With forced answering software, respondents cannot skip over questions as they do in mail surveys. The program will not let them continue if they fail to answer a question. The software may insert a boldfaced error message on the question screen or insert a pop-up box instructing the respondent how to continue. For example, if a respondent does not answer a ques- tion and tries to proceed to another screen, a pop-up box might present the following message:

You cannot leave a question blank. On questions without a “Not sure” or “Decline to answer” option, please choose the response that best represents your opinions or experiences.

The respondent must close the pop-up box and answer the question in order to proceed to the next screen.

Some designers include an interactive help desk in their web questionnaire so that respondents can solve problems they encounter in completing a questionnaire. A respondent might e-mail questions to the survey help desk or get live, interactive, real-time support via an online help desk.

Some respondents will leave the questionnaire website, prematurely terminating the survey. In many cases sending an e-mail message to these respondents at a later date, encouraging them to revisit the website, will persuade them to complete the questionnaire. Through the use of software and cookies, researchers can make sure that the respondent who revisits the website will be able to pick up at the point where he or she left off.

Once an Internet questionnaire has been designed, it is important to pretest it to ensure that it works with Internet Explorer, Firefox, Chrome, Safari, Opera, Maxthon, and other Internet browsers. Some general-purpose programming languages, such as Java, do not always work with all

error trapping

Using software to control the flow of an Internet questionnaire—for example, to prevent respondents from backing up or failing to answer a question.

forced answering software

Software that prevents respondents from continuing with an Internet questionnaire if they fail to answer a question.

interactive help desk

In an Internet questionnaire, a live, real-time support feature that solves problems or answers questions respondents may encounter in completing the questionnaire.

RESEARCH SNAPSHOT

The federal government’s Centers for Medicare and Medicaid Services (CMS) is supposed to make information about hos- pital performance available to the public so that patients can compare hospitals and make informed choices about health- care services. An important aspect of hospital performance is whether patients feel satisfied with the care they receive. Many hospitals have used surveys to measure patient satisfaction, but comparing hospitals requires that all facilities use the same survey. So, CMS has spent several years creating and modifying a questionnaire, the Consumer Assessment of Health Providers and Systems (CAHPS) Hospital Survey, and similar question- naires for other health-care providers.

Considering that the CAHPS Hospital Survey is being made available to all U.S. hospitals and the data will be made public, the researchers developing the survey have put it through exten- sive pretesting, with public comment invited at each stage of the process. The first version of the survey, consisting of 68 questions, was given to a sample of 18 individuals drawn from the general population, who were then interviewed to discuss how they inter- preted the questions. Based on their reactions, the researchers modified the survey to make it clearer and then tested it on 13 more people. Almost half the interviews were conducted in Span- ish. This process resulted in a draft survey with 66 items.

Next, the 66-item survey underwent pilot testing with almost 50,000 patients at hospitals in three states. Hospitals were selected to represent a cross section of hospital types in those states. The researchers verified that a representative sam- ple of the population completed the survey, and they analyzed the data to assess which questions best predicted satisfaction levels. Based on these analyses, the questionnaire was reduced to 32 items. That questionnaire was tested at several more hospitals and reviewed by the National Quality Forum. Based on this feedback, seven items were deleted and then two items were restored to the questionnaire. Finally, the resulting 27-item survey was ready for use nationwide.

Sources: Goldstein, Elizabeth, Marybeth Farquhar, Christine Crofton, Charles Darby, and Steven Garfinkel, “Measuring Hospital Care from the Patients’ Perspective: An Overview of the CAHPS Hospital Survey Development Process,” Health Services Research (December 2005); “CAHPS Surveys and Tools to Advance Patient-Centered Care,” U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality (AHRQ), http://www.cahps.ahrq.gov, accessed September 7, 2011.

Pretesting the CAHPS Hospital Survey

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browsers. While more compatible then ever, different browsers still have different peculiarities, thus a survey that works perfectly well with one may not function at all with another.13

How Much Pretesting and Revising Are Necessary? Many novelists write, rewrite, revise, and rewrite again certain chapters, paragraphs, or even sen- tences. The researcher works in a similar world. Rarely—if ever—does he or she write only a first draft of a questionnaire. Usually the questionnaire is written, revised, shared with others for feed- back, and then revised again. After that, it is tried out on a group, selected on a convenience basis, which is similar in makeup to the one that ultimately will be sampled. Although the researcher should not select a group too divergent from the target market—for example, selecting business students as surrogates for businesspeople—pretesting does not require a statistical sample. The pretesting process allows the researcher to determine whether respondents have any difficulty understanding the questionnaire and whether there are any ambiguous or biased questions. This process is exceedingly beneficial. Making a mistake with 25 or 50 subjects can avoid the potential disaster of administering an invalid questionnaire to several hundred individuals. For a question- naire investigating teaching-students’ experience with web-based instruction, the researcher had the questionnaire reviewed first by university faculty members to ensure the questions were valid, then asked 20 teaching students to try answering the questions and indicate any ambiguities they noticed. Their feedback prompted changes in the format and wording. Pretesting was especially helpful because the English-language questionnaire was used in a school in the United Arab Emir- ates, where English is spoken but is not the primary language.14

Tabulating the results of a pretest helps determine whether the questionnaire will meet the objectives of the research. A preliminary tabulation often illustrates that, although respondents can easily comprehend and answer a given question, that question is inappropriate because it does not provide relevant information to help solve the business problem. Consider the following example from a survey among distributors of power-actuated tools such as stud drivers concerning the percentage of sales to given industries:

Please estimate what percentage of your fastener and load sales go to the following industries:

__ % heating, plumbing, and air conditioning __ % carpentry __ % electrical __ % maintenance __ % other (please specify)

The researchers were fortunate to learn that asking the question in this manner made it virtu- ally impossible to obtain the information actually desired. The categories are rather vague, a high percentage may fall into the other category, and most respondents’ answers did not total 100 percent. As a result, the question had to be revised. In general, getting respondents to add everything cor- rectly is a difficult task and virtually impossible if they can not see all the categories (not a good idea for a telephone survey!). Pretesting difficult questions such as these is essential.

What administrative procedures should be implemented to maximize the value of a pretest? Administering a questionnaire exactly as planned in the actual study often is not possible. For example, mailing out a questionnaire is quite expensive and might require several weeks that sim- ply cannot be spared. Pretesting a questionnaire in this manner would provide important informa- tion on response rate, but may not point out why questions were skipped or what questions are ambiguous or confusing. Personal interviewers can record requests for additional explanation or comments that indicate respondents’ difficulty with question sequence or other factors. This is the primary reason why interviewers are often used for pretest work. Self-administered questionnaires are not reworded to be personal interviews, but interviewers are instructed to observe respondents and ask for their comments after they complete the questionnaire. When pretesting personal or

preliminary tabulation

A tabulation of the results of a pretest to help determine whether the questionnaire

will meet the objectives of the research.

360 • PART FOUR Measurement Concepts

telephone interviews, interviewers may test alternative wordings and question sequences to deter- mine which format best suits the intended respondents.

No matter how the pretest is conducted, the researcher should remember that its purpose is to uncover any problems that the questionnaire may cause. Thus, pretests typically are conducted to answer questions about the questionnaire such as the following:

■ Can the questionnaire format be followed by the interviewer? ■ Does the questionnaire flow naturally and conversationally? ■ Are the questions clear and easy to understand? ■ Can respondents answer the questions easily? ■ Which alternative forms of questions work best?

Pretests also provide means for testing the sampling procedure—to determine, for example, whether interviewers are following the sampling instructions properly and whether the procedure is efficient. Pretests also provide estimates of the response rates for mail surveys and the completion rates for telephone surveys.

Usually a questionnaire goes through several revisions. The exact number of revisions depends on the researcher’s and client’s judgment. The revision process usually ends when both agree that the desired information is being collected in an unbiased manner.

Designing Questionnaires for Global Markets Now that business research is being conducted around the globe, researchers must take cultural fac- tors into account when designing questionnaires. The most common problem involves translating a questionnaire into other languages. A questionnaire developed in one country may be difficult to translate because equivalent language concepts do not exist or because of differences in idiom and vernacular. Although Spanish is spoken in both Mexico and Venezuela, one researcher found out that the Spanish translation of the English term retail outlet works in Mexico but not in Venezuela. Venezuelans interpreted the translation to refer to an electrical outlet, an outlet of a river into an ocean, or the passageway onto a patio.

Counting on an international audience to speak a common language such as English does not necessarily bridge these gaps, even when the respondents actually do speak more than one language. Cultural differences incorporate many shades of meaning that may not be captured by a survey delivered in a language used primarily for, say, business transactions. In a test of this idea, undergraduate students in 24 countries completed questionnaires about attitudes toward school and career. Half received the questionnaire in English, and half in their native language. The results varied, with country-to-country differences being smaller when students completed the question- naire in English.15

International marketing researchers often have questionnaires back translated. Back translation is the process of taking a questionnaire that has previously been translated from one language to another and having it translated back again by a second, independent transla- tor. The back translator is often a person whose native tongue is the language that will be used for the questionnaire. This process can reveal inconsistencies between the English version and the translation. For example, when a soft-drink company translated its slogan, “Baby, it’s cold inside” into Cantonese for research in Hong Kong, the result read, “Small Mosquito, on the inside, it is very cold.” In Hong Kong, small mosquito is a colloquial expression for a small child. Obviously the intended meaning of the advertising message had been lost in the trans- lated questionnaire.16

As indicated in Chapter 8, literacy influences the designs of self-administered questionnaires and interviews. Knowledge of the literacy rates in foreign countries, especially those that are just developing modern economies, is vital.

back translation

Taking a questionnaire that has previously been translated into another language and having a second, independent translator translate it back to the original language.

CHAPTER 15 Questionnaire Design • 361

SUMMARY

1. Explain the significance of decisions about questionnaire design and wording. Good questionnaire design is a key to obtaining accurate survey results. The specific questions to be asked will be a function of the type of information needed to answer the manager’s questions and the communication medium of data collection. Relevance and accuracy are the basic criteria for judging questionnaire results. A questionnaire is relevant if no unnecessary information is collected and the information needed for solving the business problem is obtained. Accuracy means that the information is reliable and valid.

2. Define alternatives for wording open-ended and fixed-alternative questions. Knowing how each question should be phrased requires some knowledge of the different types of questions possible. Open-ended response questions pose some problem or question and ask the respondent to answer in his or her own words. Fixed-alternative questions require less interviewer skill, take less time to complete, and are easier to answer. In fixed-alternative questions the respondent is given specific limited alternative responses and asked to choose the one closest to his or her own viewpoint. Standardized responses are easier to code, tabulate, and interpret. Care must be taken to formulate the responses so that they do not overlap and cover all the possibilities. Respondents whose answers do not fit any of the fixed alternatives may be forced to select alternatives that do not communicate what they really mean. Open-ended response questions are especially useful in exploratory research or at the beginning or end of a questionnaire. They make a questionnaire more expensive to analyze because of the uniqueness of the answers. Also, interviewer bias can influence the responses to such questions.

3. Summarize guidelines for questions that avoid mistakes in questionnaire design. Some guidelines for questionnaire construction have emerged from research experience. The lan- guage should be simple to allow for variations in educational level. Researchers should avoid leading or loaded questions, which suggest answers to the respondents, as well as questions that induce them to give socially desirable answers. Respondents have a bias against ques- tions that suggest changes in the status quo. Their reluctance to answer personal questions can be reduced by explaining the need for the questions and by assuring respondents of the confidentiality of their replies. The researcher should carefully avoid ambiguity in questions. Another common problem is the double-barreled question, which asks two questions at once. Finally, researchers need to examine the question to ensure that it will provide vari- ance in responses.

4. Describe how the proper sequence of questions may improve a questionnaire. Question sequence can be very important to the success of a survey. The opening questions should be designed to capture respondents’ interest and keep them involved. General questions should pre- cede specific ones. In a series of attitude scales the first response may be used as an anchor for comparison with the other responses. The order of alternatives on closed questions can affect the results. Filter questions are useful for avoiding unnecessary questions that do not apply to a particu- lar respondent. Such questions may be put into a flowchart for personal or telephone interviewing. Personal questions, demographics, and categorical questions should be placed at the end of the questionnaire.

5. Discuss how to design a questionnaire layout. The layout of a mail or other self- administered questionnaire can affect its response rate. An attractive questionnaire encourages a response, as does a carefully phrased title. Internet questionnaires present unique design issues. Decisions must be made about the use of color, graphics, animation, sound, and other special layout effects that the Internet makes possible.

6. Describe criteria for pretesting and revising a questionnaire and for adapting it to global markets. Pretesting helps reveal errors while they can still be corrected easily. A preliminary tabulation may show that, even if respondents understand questions, the responses are not relevant to the business problem. Often, the most efficient way to conduct a pretest is with interviewers to generate quick feedback. International business researchers must take cultural factors into account when designing questionnaires. The most widespread problem involves translation into another language. International questionnaires are often back translated to insure the original concepts are correctly translated.

362 Part 4: Measurement Concepts

KEY TERMS AND CONCEPTS back translation, 361 checkboxes, 356 checklist question, 339 counterbiasing statement, 343 determinant-choice question, 339 double-barreled question, 344 drop-down box, 356 error trapping, 359 filter question, 348 fixed-alternative questions, 336 forced answering software, 359

frequency-determination question, 339

funnel technique, 347 interactive help desk, 359 leading question, 342 loaded question, 342 multiple-grid question, 350 mutually exclusive, 339 open-ended boxes, 356 open-ended response questions, 336 order bias, 347

pivot question, 348 pop-up boxes, 358 preliminary tabulation, 360 push button, 356 radio button, 356 simple-dichotomy (dichotomous)

question, 338 split-ballot technique, 343 status bar, 356 totally exhaustive, 339 variable piping software, 358

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Evaluate and comment on the following questions, taken from

several questionnaires. Do they follow the rules discussed in this chapter? a. A university computer center survey on SPSS usage:

How often do you use SPSS statistical software? Please check one.

□ Infrequently (once a semester) □ Occasionally (once a month) □ Frequently (once a week) □ All the time (daily)

b. A survey of advertising agencies:

Do you understand and like the Federal Trade Commission’s new corrective advertising policy?

______Yes ______No

c. A survey on a new, small electric car:

Assuming 90 percent of your driving is in town, would you buy this type of car?

______Yes ______No

If this type of electric car had the same initial cost as a current “Big 3” full-size, fully equipped car, but operated at one-half the cost over a five-year period, would you buy one?

______Yes ______No

d. A student survey:

Since the beginning of this semester, approximately what percentage of the time do you get to campus using each of the forms of trans- portation available to you per week?

______% Walk ______% Bicycle ______% Public transportation ______% Motor vehicle

e. A survey of motorcycle dealers:

Should the company continue its generous cooperative advertising program?

f. A government survey of gasoline retailers:

Suppose the full-service pump selling price for regular gasoline is $3.22 per gallon on the first day of the month. Suppose on the

10th of the month the price is raised to $3.34 per gallon, and on the 25th of the month it is reduced to $3.20 per gallon. In order to provide the required data you should list the accumula- tor reading on the full-service regular gasoline pump when the station opens on the 1st day, the 10th day, and the 25th day of the month and when the station closes on the last day of the month.

g. An anti-gun-control group’s survey:

Do you believe that private citizens should have the right to own firearms to defend themselves, their families, and their property from violent criminal attack?

______Yes ______No

h. A survey of the general public:

In the next year, after accounting for inflation, do you think your real personal income will go up or down?

1. Up 2. (Stay the same) 3. Down 4. (Don’t know)

i. ETHICS A survey of the general public:

Some people say that companies should be required by law to label all chemicals and substances that the government states are poten- tially harmful. The label would tell what the chemical or substance is, what dangers it might pose, and what safety procedures should be used in handling the substance. Other people say that such laws would be too strict. They say the law should require labels on only those chemicals and substances that the companies themselves decide are potentially harmful. Such a law, they say, would be less costly for the companies and would permit them to exclude those chemicals and substances they consider to be trade secrets. Which of these views is closest to your own?

1. Require labels on all chemicals and substances that the govern- ment states are potentially harmful.

2. (Don’t know) 3. Require labels on only those chemicals and substances that

companies decide are potentially harmful.

Chapter 15: Questionnaire Design 363

2. The following question was asked of a sample of television viewers:

We are going to ask you to classify the type of fan you consider yourself to be for different sports and sports programs.

■ Diehard Fan: Watch games, follow up on scores and sports news multiple times a day

■ Avid Fan: Watch games, follow up on scores and sports news once a day

■ Casual Fan: Watch games, follow up on scores and sports news occasionally

■ Championship Fan: Watch games, follow up on scores and sports news only during championships or playoffs

■ Non-Fan: Never watch games or follow up on scores ■ Anti-Fan: Dislike, oppose, or object to a certain sport

Does this question do a good job of avoiding ambiguity? 3. How might the wording of a question about income influence

respondents’ answers? 4. What is the difference between a leading question and a loaded

question? 5. Design one or more open-ended response questions to measure

reactions to a magazine ad for a Xerox photocopier. 6. Evaluate the layout of the filter question that follows:

Are you employed either full time or part time?

Mark (x) one. □ Yes □ No If yes: How many hours per week are you usually employed? Mark (x) one.

□ Less than 35 □ 35 or more What is the zip code at your usual place of work?

-------------------

7. Develop a checklist of things to consider in questionnaire construction.

8. It has been said that surveys show that consumers hate advertis- ing, but like specific ads. Comment.

9. Design a complete questionnaire: a. To evaluate a new fast-food fried chicken restaurant. b. To measure consumer satisfaction with an airline.

c. For your local Big Brothers Big Sisters organization to investigate awareness of and willingness to volunteer time to this organization.

d. For a bank located in a college town to investigate the potential for attracting college students as checking account customers.

10. The Apple Assistance Center is a hotline to solve problems for users of Macintosh computers and other Apple products. Design a short (postcard-size) consumer satisfaction/service quality questionnaire for the Apple Assistance Center.

11. ’NET Visit the following website: http://www.history.org. What type of questions might be asked in a survey to evaluate the effectiveness of this website in terms of being informative and in terms of being an effective sales medium?

12. A client tells a researcher that she wants a questionnaire that evaluates the importance of 30 product characteristics and rates her brand and 10 competing brands on these characteristics. The researcher believes that this questionnaire will induce respondent fatigue because it will be far too long. Should the researcher do exactly what the client says or risk losing the business by sug- gesting a different approach?

13. ETHICS A political action organization has on its website a series of short questionnaires that ask about your political position on an important topic. It also includes a “Support Reply Form,” a solicitation for donations. Is this approach ethical?

14. ’NET Visit Mister Poll at http://www.misterpoll.com, where you will find thousands of user-contributed polls on every imaginable topic from the controversial to the downright zany. What you find will depend on when you visit the site. However, you might find something such as a movie poll, where you pick your favorite film of the season. Evaluate the questions in the poll.

15. Try to find two friends that know the same foreign language. Write 10 Likert questions that measure how exciting a retail store environment is to shop in. Have one of your friends interpret the question into the foreign language. Have the other take the translation and state each question in English. How similar is the translated English to the original English? Comment.

RESEARCH ACTIVITY 1. Design eight questions that assess how effective an undergraduate

college business course has been.

Agency for Healthcare Research and Quality At the U.S. Department of Health and Human Services, the Agency for Healthcare Research and Quality (AHRQ) developed a survey to measure

hospital employees’ attitudes about patient safety in their facili- ties.17 The survey is designed to help hospitals ensure safety by creating an environment in which employees share information, improve safety when problems are identified, and if necessary, change the way employees deliver care. The AHRQ suggests that

hospitals use the survey to identify areas needing improvement and repeat its use to track changes over time.

The survey is shown in Case Exhibit 15.1-1.

Questions 1. Evaluate the questionnaire. Can you suggest any improvements? 2. Will this survey meet its objectives? Explain.

CASE 15.1

(Continued )

364 Part 4: Measurement Concepts

CASE EXHIBIT 15.1-1

AHRQ Hospital Questionnaire

(Continued )

INSTRUCTIONS

This survey asks for your opinions about patient safety issues, medical error, and event reporting in your hospital and will take about 10 to 15 minutes to complete.

• An “event” is defined as any type of error, mistake, incident, accident, or deviation, regardless of whether or not it results in patient harm.

• “Patient safety” is defined as the avoidance and prevention of patient injuries or adverse events resulting from the processes of health care delivery.

SECTION A: Your Work Area/Unit In this survey, think of your “unit” as the work area, department, or clinical area of the hospital where you spend most of your work time or provide most of your clinical services.

What is your primary work area or unit in this hospital? Mark ONE answer by filling in the circle. a. Many different hospital units/No specific unit

b . Medicine (non-surgical) g. Intensive care unit (any type) l. Radiology c . Surgery h . Psychiatry/mental health m. Anesthesiology d . Obstetrics i. Rehabilitation n. Other, please specify: e . Pediatrics j. Pharmacy f. Emergency department k . Laboratory

Please indicate your agreement or disagreement with the following statements about your work area/unit. Mark your answer by filling in the circle.

Think about your hospital work area/unit…

Strongly Disagree Disagree Neither Agree

Strongly Agree

1. People support one another in this unit..................................................

2. We have enough staff to handle the workload.........................................

3. When a lot of work needs to be done quickly, we work together as a team to get the work done.......................................................................

4. In this unit, people treat each other with respect.....................................

5. Staff in this unit work longer hours than is best for patient care............

6. We are actively doing things to improve patient safety............................

7. We use more agency/temporary staff than is best for patient care.........................................................................................................

8. Staff feel like their mistakes are held against them..................................

9. Mistakes have led to positive changes here.............................................

10. It is just by chance that more serious mistakes don’t happen around here.............................................................................................

11. When one area in this unit gets really busy, others help out...................

12. When an event is reported, it feels like the person is being written up, not the problem.......................................................................................

Case 15.1 (Continued )

Chapter 15: Questionnaire Design 365

CASE EXHIBIT 15.1-1 (Continued)

AHRQ Hospital Questionnaire

SECTION A: Your Work Area/Unit (continued)

Think about your hospital work area/unit…

Strongly Disagree Disagree Neither Agree

Strongly Agree

13. After we make changes to improve patient safety, we evaluate their effectiveness.............................................................................................

14. We work in “crisis mode” trying to do too much, too quickly....................

15. Patient safety is never sacrificed to get more work done...........................

16. Staff worry that mistakes they make are kept in their personnel file.............................................................................................................

17. We have patient safety problems in this unit............................................

18. Our procedures and systems are good at preventing errors from happening..................................................................................................

SECTION B: Your Supervisor/Manager Please indicate your agreement or disagreement with the following statements about your immediate supervisor/manager or person to whom you directly report. Mark your answer by filling in the circle.

Strongly Disagree Disagree Neither Agree

Strongly Agree

1. My supervisor/manager says a good word when he/she sees a job done according to established patient safety procedures..........................

2. My supervisor/manager seriously considers staff suggestions for improving patient safety............................................................................

3. Whenever pressure builds up, my supervisor/manager wants us to work faster, even if it means taking shortcuts...........................................

4. My supervisor/manager overlooks patient safety problems that happen over and over............................................................................................

SECTION C: Communications How often do the following things happen in your work area/unit? Mark your answer by filling in the circle.

Think about your hospital work area/unit… Never Rarely

Some- times

Most of the time Always

1. We are given feedback about changes put into place based on event reports..........................................................................................

2. Staff will freely speak up if they see something that may negatively affect patient care...................................................................................

4. Staff feel free to question the decisions or actions of those with more authority.......................................................................................

6. Staff are afraid to ask questions when something does not seem right.......................................................................................................

3. We are informed about errors that happen in this unit..........................

5. In this unit, we discuss ways to prevent errors from happening again........................................................................................................

(Continued )

366 Part 4: Measurement Concepts

CASE EXHIBIT 15.1-1 (Continued)

AHRQ Hospital Questionnaire

SECTION D: Frequency of Events Reported In your hospital work area/unit, when the following mistakes happen, how often are they reported? Mark your answer by filling in the circle.

Never Rarely Some- times

Most of the time Always

1. When a mistake is made, but is caught and corrected before affecting the patient, how often is this reported? .................................

2. When a mistake is made, but has no potential to harm the patient, how often is this reported? ..................................................................

3. When a mistake is made that could harm the patient, but does not, how often is this reported? ..................................................................

SECTION E: Patient Safety Grade Please give your work area/unit in this hospital an overall grade on patient safety. Mark ONE answer.

A Excellent

B Very Good

C Acceptable

D Poor

E Failing

SECTION F: Your Hospital Please indicate your agreement or disagreement with the following statements about your hospital. Mark your answer by filling in the circle.

Think about your hospital…

Strongly Disagree Disagree Neither Agree

Strongly Agree

1. Hospital management provides a work climate that promotes patient safety.............................................................................................

2. Hospital units do not coordinate well with each other...............................

3. Things “fall between the cracks” when transferring patients from one unit to another...........................................................................................

4. There is good cooperation among hospital units that need to work together ....................................................................................................

5. Important patient care information is often lost during shift changes .....................................................................................................

6. It is often unpleasant to work with staff from other hospital units ............

7. Problems often occur in the exchange of information across hospital units ..........................................................................................................

8. The actions of hospital management show that patient safety is a top priority.......................................................................................................

9. Hospital management seems interested in patient safety only after an adverse event happens.................................................................

10. Hospital units work well together to provide the best care for patients......................................................................................................

11. Shift changes are problematic for patients in this hospital ........................

SECTION G: Number of Events Reported In the past 12 months, how many event reports have you filled out and submitted? Mark ONE answer.

a. No event reports d. 6 to 10 event reports b. 1 to 2 event reports e. 11 to 20 event reports

c. 3 to 5 event reports f. 21 event reports or more

(Continued )

Chapter 15: Questionnaire Design 367

CASE EXHIBIT 15.1-1 (Continued)

AHRQ Hospital Questionnaire

SECTION H: Background Information This information will help in the analysis of the survey results. Mark ONE answer by filling in the circle.

1. How long have you worked in this hospital? a . Less than 1 year d. 11 to 15 years b. 1 to 5 years e. 16 to 20 years c. 6 to 10 years f. 21 years or more

2. How long have you worked in your current hospital work area/unit? a . Less than 1 year d. 11 to 15 years b. 1 to 5 years e. 16 to 20 years c. 6 to 10 years f. 21 years or more

3. Typically, how many hours per week do you work in this hospital? a. Less than 20 hours per week d. 60 to 79 hours per week b. 20 to 39 hours per week e. 80 to 99 hours per week c. 40 to 59 hours per week f. 100 hours per week or more

4. What is your staff position in this hospital? Mark ONE answer that best describes your staff position. a. Registered Nurse h. Dietician

b. Physician Assistant/Nurse Practitioner i. Unit Assistant/Clerk/Secretary c. LVN/LPN j. Respiratory Therapist

d. Patient Care Assistant/Hospital Aide/Care Partner k. Physical, Occupational, or Speech Therapist e. Attending/Staff Physician l. Technician (e.g., EKG, Lab, Radiology)

f. Resident Physician/Physician in Training m. Administration/Management g. Pharmacist n. Other, please specify:

5. In your staff position, do you typically have direct interaction or contact with patients? a. YES, I typically have direct interaction or contact with patients. b. NO, I typically do NOT have direct interaction or contact with patients.

6. How long have you worked in your current specialty or profession? a . Less than 1 year d. 11 to 15 years b. 1 to 5 years e. 16 to 20 years c. 6 to 10 years f. 21 years or more

SECTION I: Your Comments Please feel free to write any comments about patient safety, error, or event reporting in your hospital.

THANK YOU FOR COMPLETING THIS SURVEY.

Source: Agency for Healthcare Research and Quality, “Hospital Survey on Patient Safety Culture,” http://www.ahrq.gov/qual/patientsafetyculture/hospscanform.pdf.

368 Part 4: Measurement Concepts

Canterbury Travels Hometown, located in the north central United States, had a population of about fifty thousand. There were two travel agencies in Hometown before Canterbury

Travels opened its doors. Canterbury Travels was in its second month of operations. Owner

Roxanne Freeman had expected to have more business than she actually had. She decided that she needed to conduct a survey to determine how much business Hometown offered. She also wanted to learn whether people were aware of Canterbury Travels. She thought that this survey would determine the effectiveness of her advertising.

The questionnaire that Roxanne Freeman designed is shown in Case Exhibit 15.2-1.

Questions 1. Critically evaluate the questionnaire. 2. Will Canterbury Travels gain the information it needs from this

survey? 3. Design a questionnaire to satisfy Roxanne Freeman’s informa-

tion needs.

CASE 15.2

CASE EXHIBIT 15.2-1

Travel Questionnaire

The following questionnaire pertains to a project being conducted by a local travel agency. The intent of the study is to better understand the needs and attitudes of Hometown residents toward travel agencies. The questionnaire will take only 10 to 15 minutes to fill out at your convenience. Your name will in no way be connected with the questionnaire. 1. Have you traveled out of state? _______ Yes _______ No 2. If yes, do you travel for:

Business Both Pleasure

3. How often do you travel for the above? 0–1 times per month 0–1 times per year 2–3 times per month 2–3 times per year 4–5 times per month 4–5 times per year 6 or more times per month 6 or more times per year

4. How do you make your travel arrangements? Airline Travel agency Other (please specify) _________________________________________________________________

5. Did you know that travel agencies do not charge the customer for their services? ____ Yes ____No

6. Please rate the following qualities that would be most important to you in the selection of a travel agency:

Good Bad Free services (reservations, advice, and delivery of tickets and

literature)

__________ __________ __________ __________ __________

Convenient location __________ __________ __________ __________ __________

Knowledgeable personnel __________ __________ __________ __________ __________

Friendly personnel __________ __________ __________ __________ __________

Casual atmosphere __________ __________ __________ __________ __________

Revolving charge account __________ __________ __________ __________ __________

Reputation __________ __________ __________ __________ __________

Personal sales calls __________ __________ __________ __________ __________

7. Are you satisfied with your present travel agency? Very satisfied Very dissatisfied

Holiday Travel __________ __________ __________ __________ __________

Leisure Tours __________ __________ __________ __________ __________

Canterbury Travels __________ __________ __________ __________ __________

Other____________ __________ __________ __________ __________ __________

(Continued )

Chapter 15: Questionnaire Design 369

8. If not, what are you dissatisfied with about your travel agency? Good Bad

Free services (reservations, advice, and delivery of tickets and

literature)

__________ __________ __________ __________ __________

Convenient location __________ __________ __________ __________ __________

Knowledgeable personnel __________ __________ __________ __________ __________

Friendly personnel __________ __________ __________ __________ __________

Casual atmosphere __________ __________ __________ __________ __________

Revolving charge account __________ __________ __________ __________ __________

Reputation __________ __________ __________ __________ __________

Personal sales calls __________ __________ __________ __________ __________

9. Did you know that there is a new travel agency in Hometown? ____ Yes ____No

10. Can you list the travel agencies in Hometown and their locations? _____________________________________________________________________________________________________________________ _____________________________________________________________________________________________________________________ _____________________________________________________________________________________________________________________

11. Do you use the same travel agency repeatedly?

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Holiday Travel Leisure Tours Canterbury Travels Other (please specify)

12. Have you visited the new travel agency in Hometown? ____ Yes ____No

13. If yes, what is its name? _______________________________________________________________________________________________ 14. How do you pay for your travel expenses?

Cash Company charge Check Personal charge Credit card Other _______________________

15. Which of these have you seen advertising for? Holiday Travel Canterbury Travels Other___________________

16. Where have you seen or heard the advertisement you describe above? 17. Would you consider changing travel agencies?

____ Yes ____No

The following are some personal questions about you that will be used for statistical purposes only. Your answers will be held in the strictest confidence. 18. What is your age?

19–25 46–55 26–35 56–65 36–45 Over 65

19. What is your sex? Male Female

20. What is your marital status? Single Divorced Married Widowed

CASE EXHIBIT 15.2-1 (Continued )

Travel Questionnaire

(Continued )

370 Part 4: Measurement Concepts

CASE EXHIBIT 15.2-1 (Continued)

Travel Questionnaire

21. How long have you lived in Hometown? 0–6 months 5–10 years 7–12 months 11–15 years 1–4 years Over 15 years

22. What is your present occupation? Business and professional Laborer

Salaried and semiprofessional Student

Skilled worker

23. What is the highest level of education you have completed? Elementary school 1–2 years of college

Junior high school 3–4 years of college

Senior high school More than 4 years of college

Trade or vocational school

24. What is your yearly household income? $0–$5,000 $25,001–$40,000

$5,001–$10,000 $40,001–$60,000

$10,001–$15,000 $60,000 and above

$15,001–$25,000

The questions in Case Exhibit 15.3-1, about a visit to McDonald’s, originally appeared in Spanish and were translated into English.

Questions 1. What is the typical process for developing questionnaires for

markets where consumers speak a language other than English?

2. Find someone who speaks Spanish and have him or her back translate the questions that appear in Case Exhibit 15.3-1. Are these Spanish-language questions adequate?

CASE 15.3

CASE EXHIBIT 15.3-1

McDonald’s Questionnaire

AQUI SE EMPIEZA

1. En general, ¿qué tan satisfecho/a quedó con su visita a este McDonald's hoy?

2. Su visita fue....... Adentro (A) o en el Drive-thru (DT) 3. Su visita fue....... Durante el Desayuno (D), Almuerzo (A), Cena (C) 4. Su visita fue....... Entre semana (E) Fin de semana (F)

1 2 3 4 5

NADA SATISFECHO/A

MUY SATISFECHO/A

Adentro

Desayuno

Entre semana

Drive-thru

Almuerzo A Cena

Fin de semana

COMIDA 5. ¿Quedó satisfecho/a con la comida que recibio hoy? Si NO, ¿cuál fue el problema?

Favor de rellenar el(los) círculo(s) apropiado(s).

Sandwich / platillo frío Apariencia desagradable Mal sabor de la comida Pocas papas en la bolsa / caja Papas / tortitas de papa frías Papas no bien saladas Bebida aguada / de mal sabor

S Si N No

...........

McDonald’s Spanish Language Questionnaire

Chapter 15: Questionnaire Design 371

Schönbrunn Palace in Vienna The Schönbrunn Palace in Vienna was constructed in the eighteenth century during the reign of the Hapsburgs. Today this former summer residence of the

imperial family is one of Austria’s top tourist attractions. The questions in Case Exhibit 15.4-1, about a visit to the

Schönbrunn Palace, originally appeared in German and were translated into English.

Questions 1. What is the typical process for developing questionnaires for

markets where consumers speak a different language? 2. Find someone who speaks German and have him or her back

translate the questions that appear in Case Exhibit 15.4-1. Are these German questions adequate?

CASE 15.4

CASE EXHIBIT 15.4-1

Schönbrunn Palace Questionnaire

372 Part 4: Measurement Concepts

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APPENDIX 15A

As Chapters 13, 14, and 15 explain, problem definitions and research objectives determine the nature of the questions to be asked. In most cases researchers construct custom questions for their specific projects. However, in many instances different research projects have some common research objectives. This appendix compiles question wordings and measurement scales frequently used by business researchers. It is by no means exhaustive. It does not repeat every question already discussed in the text. For example, it does not include the hundreds of possible semantic differential items or Likert scale items discussed in Chapter 14.

The purpose of this appendix is to provide a bank of questions and scales for easy reference. It can be used when business research objectives dictate investigation of commonly researched issues.

Questions about Advertising Awareness Have you ever seen any advertising for (brand name)?

□ Yes □ No

Are you aware of (brand name)?

□ Yes □ No

If yes, how did you first become aware of (brand name)?

■ In-flight airline magazine ■ Poster or billboard at airport ■ Television at airport ■ Card in the seatback pocket ■ Other (please specify) __________

Question Wording and Measurement Scales for

Commonly Researched Topics

Unaided Recall/Top of the Mind Recall Can you tell me the names of any brands of (product category) for which you have seen or heard any advertising recently? __________

__________

__________ (After reading a magazine or viewing a TV program with commercials) Please try to recall all the brands you saw advertised on/in (name of program or magazine). (DO NOT PROBE. WRITE BRAND NAMES IN ORDER MENTIONED BY RESPONDENT.)

(After establishing that the respondent watched a certain television program) Do you recall seeing a commercial for any (product category)? (IF YES) What brand of (product category) was advertised?

Aided Recall (After establishing that the respondent watched a certain television program or read a certain magazine) Now, I’m going to read you a list of brands. Some of them were advertised on/in (name of program or magazine); others were not. Please tell me which ones you remember seeing, even if you mentioned them before.

Brand A (Advertised) Brand B (Not advertised) Brand C (Advertised)

Do you remember seeing a commercial for (specific brand name)?

□ Yes □ No

Recognition (Show advertisement to respondent) Did you see or read any part of this advertisement?

□ Yes □ No

Message Communication/Playback (Sales Point Playback) These questions require that the researcher first qualify awareness with a question such as, “Have you ever seen any advertising for (brand name)?” The interviewer then asks message playback questions.

(If yes) What did the advertising tell you about (brand name or product category)? ____________________________________________________________________________ ____________________________________________________________________________

Other than trying to sell you the product, what do you think was the main idea in the description you just read (commercial you just saw)? ____________________________________________________________________________ ____________________________________________________________________________

What was the main thing it was trying to communicate about the product? What did the advertising for (brand name) say about the product? ____________________________________________________________________________ ____________________________________________________________________________

What did you learn about (brand name) from this advertisement? ____________________________________________________________________________ ____________________________________________________________________________ ____________________________________________________________________________

374 Part 4: Measurement Concepts

Attitude toward the Advertisement Please choose the statement below that best describes your feelings about the commercial you just saw.

□ I liked it very much. □ I liked it. □ I neither liked nor disliked it. □ I disliked it. □ I disliked it very much. Was there anything in the commercial you just saw that you found hard to believe?

□ Yes □ No

What thoughts or feelings went through your mind as you watched the advertisement?

Attitude toward Advertised Brand (Persuasion) Based on what you’ve seen in this commercial, how interested would you be in trying the product?

□ Extremely interested □ Very interested □ Somewhat interested □ Not very interested □ Not at all interested

The advertisement tried to increase your interest in (brand). How was your buying interest affected?

□ Increased considerably □ Increased somewhat □ Not affected □ Decreased somewhat □ Decreased considerably

Based on what you’ve just seen in this commercial, how do you think (brand name) might compare to other brands you’ve seen or heard about?

□ Better □ As good as □ Not as good as

Readership/Viewership Have you ever read (seen) a copy of (advertising medium)?

□ Yes □ No

How frequently do you (watch the evening news on channel X)?

□ Every day □ 5–6 times a week □ 2–4 times a week □ Once a week □ Less than once a week □ Never

Several of the questions about products or brands in the following section are also used to assess attitudes toward advertised brands.

Chapter 15: Questionnaire Design 375

Questions about Ownership and Product Usage Ownership Do you own a (product category)?

□ Yes □ No

Purchase Behavior Have you ever purchased a (product category or brand name)?

□ Yes □ No

Regular Usage Which brands of (product category) do you regularly use?

□ Brand A □ Brand B □ Brand C □ Do not use __________

Which brands of (product category) have you used in the past month?

□ Brand A □ Brand B □ Brand C □ Do not use __________

In an average month, how often do you buy (product category or brand name)?

Record number of times per month __________

How frequently do you buy (product category or brand name)?

□ Every day □ 5–6 times a week □ 2–4 times a week □ Once a week □ Less than once a week □ Never

Would you say you purchase (product category or brand name) more often than you did a year ago, about the same as a year ago, or less than a year ago?

□ More often than a year ago □ About the same as a year ago □ Less than a year ago

Questions about Goods and Services Ease of Use How easy do you find using (brand name)?

□ Very easy □ Easy

376 Part 4: Measurement Concepts

□ Neither easy nor difficult □ Difficult □ Very difficult

Uniqueness How different is this brand from other brands of (product category)?

□ Very different □ Somewhat different □ Slightly different □ Not at all different

How would you rate this product (brand name) on uniqueness?

□ Extremely unique □ Very unique □ Somewhat unique □ Slightly unique □ Not at all unique

Please form several piles of cards so that statements that are similar to each other or say similar things are in the same pile. You may form as many piles as you like, and you may put as many or as few cards as you want in a pile. You can set aside any statements that you feel are unique or different and are not similar to any of the other statements.

Attribute Ratings/Importance of Characteristics Measurement scales such as the semantic differential and Likert scales are frequently used to assess product attributes, especially when measuring brand image or store image. See Chapter 15.

How important is (specific attribute), as far as you are concerned?

□ Very important □ Of some importance □ Of little importance □ Of absolutely no importance

We would like you to rate (brand name or product category) on several different characteristics. (For concept tests, add: Since you may not have used this product before, please base your answers on your impressions from what you’ve just read.)

Characteristic A

□ Excellent □ Good □ Fair □ Poor

Interest In general, how interested are you in trying a new brand of (product category)?

□ Very interested □ Somewhat interested □ Not too interested □ Not at all interested

Chapter 15: Questionnaire Design 377

Like/Dislike What do you like about (brand name)? ____________________________________________________________________________ ____________________________________________________________________________

What do you dislike about (brand name)? ____________________________________________________________________________ ____________________________________________________________________________ How do you like the taste of (brand name)?

□ Like it very much □ Like it □ Neither like nor dislike it □ Dislike it □ Strongly dislike it

Preference Which credit card do you prefer to use?

□ American Express □ MasterCard □ Visa □ No preference

Expectations How would you compare the way (company’s) service was actually delivered with the way you had anticipated that (company) would provide the service?

□ Much better than expected □ Somewhat better than expected □ About the same as expected □ Somewhat worse than expected □ Much worse than expected

Satisfaction How satisfied were you with (brand name)?

□ Very satisfied □ Somewhat satisfied □ Very dissatisfied

How satisfied were you with (brand name)?

□ Very satisfied □ Very dissatisfied □ Somewhere in between

(If somewhere in between) On balance, would you describe yourself as leaning toward being more satisfied or more dissatisfied with (brand name) than with the brand you normally use?

□ Satisfied □ Dissatisfied

Now that you have owned (brand name) for 6 months, please tell us how satisfied you are with it.

□ Completely satisfied □ Very satisfied

378 Part 4: Measurement Concepts

□ Fairly well satisfied □ Somewhat dissatisfied □ Very dissatisfied

Quality How would you rate the quality of (brand name)?

□ Excellent □ Good □ Fair □ Poor

Please indicate how the quality of (Brand A) compares with the quality of (Brand B).

□ Better □ About the same □ Worse

Problems Have you experienced problems with (company’s) service?

□ Yes □ No

When attempting to contact (company’s) representative, how much of a problem, if any, was each of the following:

Phones busy

□ No problem at all □ Slight problem □ Somewhat of a problem □ Major problem

Put on hold too long or too often

□ No problem at all □ Slight problem □ Somewhat of a problem □ Major problem

What are the major shortcomings of (brand name)? (PROBE: What other shortcomings are there?)

Benefits Do you think (product concept) would have major benefits, minor benefits, or no benefits at all?

□ Major benefits □ Minor benefits □ No benefits at all

Improvements In what ways, if any, could (brand name) be changed or improved? We would like you to tell us anything you can think of, no matter how minor it seems.

Buying Intentions for Existing Products Do you intend to buy a (brand name or product category) in the next month (3 months, year, etc.)?

□ Yes □ No

If a free (product category) were offered to you, which would you select?

□ Brand A □ Brand B □ Brand C □ Do not use

Chapter 15: Questionnaire Design 379

Buying Intentions Based on Product Concept (Respondent is shown a prototype or asked to read a concept statement.) Now that you have read about (prod- uct concept), if this product were available at your local store, how likely would you be to buy it?

□ Would definitely buy it □ Would probably buy it □ Might or might not buy it □ Would probably not buy it □ Would definitely not buy it

(Hand response card to respondent.) Which phrase on this card indicates how likely you would be to buy this product the next time you go shopping for a product of this type?

□ Would definitely buy it □ Would probably buy it □ Might or might not buy it □ Would probably not buy it □ Would definitely not buy it

Now that you have read about (product concept), if this product were available at your local store for (price), how likely would you be to buy it?

□ Would definitely buy it □ Would probably buy it □ Might or might not buy it □ Would probably not buy it □ Would definitely not buy it

How often, if ever, would you buy (product concept)?

□ Once a week or more □ Once every 2 to 3 weeks □ Once a month/every 4 weeks □ Once every 2 to 3 months □ Once every 4 to 6 months □ Less than once a year □ Never

Based on your experience, would you recommend (company) to a friend who wanted to purchase (product concept)?

□ Recommend that the friend buy from (company) □ Recommend that the friend not buy from (company) □ Offer no opinion either way

Reason for Buying Intention Why do you say that you would (would not) buy (brand name)? (PROBE: What other reason do you have for feeling this way?)

Questions about Demographics Age What is your age, please?

What year were you born?

380 Part 4: Measurement Concepts

Education What is your level of education?

□ Some high school or less □ Completed high school □ Some college □ Completed college □ Some graduate school □ Completed graduate school

What is the highest level of education you have obtained?

□ Some high school or less □ High school graduate □ Some college □ College graduate □ Postgraduate school □ Completed graduate school

Marital Status What is your marital status?

□ Married □ Divorced/separated □ Widowed □ Never married/single

Children Are there any children under the age of 6 living in your household?

□ Yes □ No

If yes, how many?

Income Which group describes your annual family income?

□ Under $20,000 □ $20,000 –$39,999 □ $40,000 –$59,999 □ $60,000 –$79,999 □ $80,000 –$99,999 □ $100,000 –$149,999 □ $150,000 or more

Please check the box that describes your total household income before taxes in (year). Include income for your- self as well as for all other persons who live in your household.

□ Less than $10,000 □ $35,000 –$39,999 □ $10,000 –$14,999 □ $40,000 –$49,999

Chapter 15: Questionnaire Design 381

□ $15,000 –$19,999 □ $50,000 –$59,999 □ $20,000 –$24,999 □ $60,000 –$74,999 □ $25,000 –$29,999 □ $75,000 or more □ $30,000 –$34,999

Occupation What is your occupation?

□ Professional □ Technical □ Executive □ Labor □ Managerial □ Secretarial □ Administrative □ Clerical □ Sales □ Other

What is your occupation?

□ Homemaker □ Clerical or service worker □ Professional/technical □ Tradesperson/machine operator □ Upper management/executive □ Laborer □ Middle management □ Retired □ Sales/marketing □ Student

382 Part 4: Measurement Concepts

P a r t F I V E

Sampling and Fieldwork

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CHAPTER 16 Sampling Designs and Sampling Procedures

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory

CHAPTER 18 Fieldwork

After studying this chapter, you should be able to

1. Explain reasons for taking a sample rather than a complete census

2. Describe the process of identifying a target population and selecting a sampling frame

3. Compare random sampling and systematic (nonsampling) errors

4. Identify the types of nonprobability sampling, including their advantages and disadvantages

5. Summarize the advantages and disadvantages of the various types of probability samples

6. Discuss how to choose an appropriate sample design, as well as challenges for Internet sampling

C H A P T E R

I t is easy to ask people what they consider to be the most pressing financial problems they face. From low wages, to rising health care and housing costs, to a concern for too much debt, these problems are

constantly on the minds of many families today. When pressed about which financial problem is most important, some inter- esting trends occur. These trends could not have been captured if not for the work of large-scale sampling of populations.

Each quarter, the Gallup Corporation develops a representa- tive sample of approximately 1,000 U.S. adults, aged 18 and older, to capture public perceptions on a variety of relevant topics, including financial concerns of the family. Since the sam- ple is developed and obtained carefully, it serves as a represen- tation of the population of adults in the U.S. who are 18 years or older. As a result of this sampling technique, researchers can be 95 percent confident that the responses of the sample are reflective of this national population, with a sampling error of less than 3 percent. Using telephone-based interviews, the Gallup Corporation asks the respondent to describe “the most important financial problem facing your family today.” Responses are open-ended, and are then coded based upon the theme of the response.

Interestingly, trends suggest that the most important financial problem facing families can often change over time, and may be reflective of the respondent’s current awareness of the financial challenges of the day. For example, when energy and gas prices were at their highest during the summer of 2008, almost one-third (29 percent) of the July 2008 Gallup respondents listed energy and gas prices as their most important problem. However, in less than six months (January 2009), energy and gas prices were mentioned

LEARNING OUTCOMES

Sampling Designs and Sampling Procedures

Chapter Vignette:

Changing Pocketbook Problems for Today’s Families

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by only 3 percent. While health care costs was mentioned by 19 percent of families in October 2007, only 9 percent mentioned health care a year later. Following the financial crisis, another shift has occurred. In July 2011, the most frequently mentioned financial problem was low wages, mentioned by 17 percent of the respondents, while health care costs (12 percent) were second and 7 percent mentioned energy and gas prices.

The implication of these types of changing trends is that financial problems facing families evolve over time. And, families often look no further than their own pocketbook (or credit card statement) when they consider their great- est financial challenges. The use of large-scale representa- tive samples by the Gallup Corporation helped reveal these interesting trends.1

Introduction Sampling is a familiar part of daily life. A customer in a bookstore picks up a book, looks at the cover, and skims a few pages to get a sense of the writing style and content before deciding whether to buy. A high school student visits a college classroom to listen to a professor’s lecture. Selecting a university on the basis of one classroom visit may not be scientific sampling, but in a personal situation, it may be a practical sampling experience. When measuring every item in a population is impossible, inconvenient, or too expensive, we intuitively take a sample.

Although sampling is commonplace in daily activities, these familiar samples are seldom sci- entific. For researchers, the process of sampling can be quite complex. Sampling is a central aspect of business research, requiring in-depth examination. This chapter explains the nature of sampling and ways to determine the appropriate sample design.

Sampling Terminology A population (universe) is any complete group—for example, of people, sales territories, stores, or college students—that shares some common set of characteristics. The term population element refers to an individual member of the population.

Researchers could study every element of a population to draw some conclusion. A census is an investigation of all the individual elements that make up the population—a total enumeration of every element of interest. Thus, if we wished to know whether more adult Texans drive pickup trucks than sedans, we could contact every adult Texan and find out whether or not they drive a pickup truck or a sedan. We would then know the answer to this question definitively.

Typically, business researchers do not conduct a census of the population, but rather select a smaller number of population elements, a sample. A sample is a subset, or some part, of a larger population. The purpose of sampling is to estimate an unknown characteristic of a population. As seen in the chapter vignette above, the process of sampling involves using a portion of a population to make conclusions about the whole population.

Why Sample? At a wine tasting, guests sample wine by having a small taste from each of a number of different wines. From this, the taster decides if he or she likes a particular wine and if it is judged to be of low or high quality. If an entire bottle were consumed to decide, the taster may end up not caring much about the next bottle! However, in a scientific study in which the objective is to determine an unknown population value, why should a sample rather than a complete census be taken?

Pragmatic Reasons Applied business research projects usually have budget and time constraints. If Ford Motor Corporation wished to take a census of past purchasers’ reactions to the company’s recalls of

population (universe)

Any complete group of entities that share some common set of characteristics.

population element

An individual member of a population.

census

An investigation of all the individual elements that make up a population.

sample

A subset, or some part, of a larger population.

CHAPTER 16 Sampling Designs and Sampling Procedures • 385

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defective models, the researchers would have to contact millions of automobile buyers. Some of them would be inaccessible (for example, out of the country), and it would be impossible to con- tact all these people within a short time period.

A researcher who wants to investigate a population with an extremely small number of popu- lation elements may elect to conduct a census rather than a sample because the cost, labor, and time drawbacks would be relatively insignificant. For a company that wants to assess salespersons’ satis- faction with its computer networking system, circulating a questionnaire to all 25 of its employees is practical. In most situations, however, many practical reasons favor sampling. Sampling cuts costs, reduces labor requirements, and gathers vital information quickly. These advantages may be suf- ficient in themselves for using a sample rather than a census, but there are other reasons.

Accurate and Reliable Results As seen in the Research Snapshot “Finding Out about Work Is a Lot of Work!”, another major reason for sampling is that most properly selected samples give results that are quite accurate. This is particularly true if the elements of a population are similar. In this situation, only a small sample is necessary to accurately portray the characteristic of interest. Thus, a population consisting of 10,000 eleventh grade students in all-boys Catholic high schools will require a smaller sample than a broader population consisting of 10,000 high school students from coeducational secondary schools.

While not common, in some situations a sample may be more accurate than a census. Inter- viewer mistakes, tabulation errors, and other nonsampling errors may increase during a census because of the increased volume of work. In a sample, increased accuracy may sometimes be pos- sible because the fieldwork and tabulation of data can be more closely supervised. In a field survey, a small, well-trained, closely supervised group may do a more careful and accurate job of collecting information than a large group of nonprofessional interviewers who try to contact everyone. An interesting case in point is the use of samples by the U.S. Census Bureau to check the accuracy of the U.S. Census. If the sample indicates a possible source of error, the census is redone.

Destruction of Test Units Many research projects, especially those in quality-control testing, require the destruction of the items being tested. If a manufacturer of firecrackers wished to find out whether each unit met a specific production standard, no product would be left after the testing. This is the exact situation

SURVEY

THIS! The data gathered in conjunction with the BRM Survey asks students questions related to job preferences. These data may well be of interest to prospective employers looking to hire qualified businesspeople.

1. How well do you think the results collected in this sur- vey represent the population of entry-level, business- oriented, recent college graduates?

2. If question one shown in the screenshot does not describe the population to which this survey pertains, describe one that you believe is better represented by this data. In other words, work backwards from the data characteristics to infer a population that is well represented.

3. Can the data be stratified in a way that would allow it to represent more specific populations? Explain your answer. 4. Take a careful look at the choices indicated in the responses shown. Does this particular respondent neatly represent a

common population? Comment.

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in many research strategy experiments. For example, if an experimental sales presentation were presented to every potential customer, no prospects would remain to be contacted after the experi- ment. In other words, if there is a finite population and everyone in the population participates in the research and cannot be replaced, no population elements remain to be selected as sampling units. The test units have been destroyed or ruined for the purpose of the research project.

Practical Sampling Concepts Before taking a sample, researchers must make several decisions. Exhibit 16.1 presents these deci- sions as a series of sequential stages, but the order of the decisions does not always follow this sequence. These decisions are highly interrelated. The issues associated with each of these stages, except for fieldwork, are discussed in this chapter and Chapter 17. Fieldwork is examined in Chapter 18.

Defining the Target Population Once the decision to sample has been made, the first question concerns identifying the target population. What is the relevant population? In many cases this question is easy to answer. Registered voters may be clearly identifiable. Likewise, if a company’s 106-person sales force is the population of concern, there are few definitional problems. In other cases the decision may be difficult. One survey concerning organizational buyer behavior incorrectly defined the population as purchasing agents whom sales representatives regularly contacted. After the survey, investigators discovered that industrial engineers within the customer companies rarely talked with the salespeople but substantially affected buying decisions. For consumer-related research, the appropriate population element frequently is the household rather than an individual member of the household. This presents some problems if household lists are not available.

At the outset of the sampling process, the target population must be carefully defined so that the proper sources from which the data are to be collected can be identified. The usual technique

RESEARCH SNaPSHOt

What do people do for work? How long does it take them to get there? What do they earn? These and many other ques- tions are critically important for United States economists and social scientists. The U.S. Census Bureau and the Bureau of Labor Statistics have jointly asked these questions, every month, for almost 70 years.

The work of these two Bureaus is captured by the Current Population Survey (CPS). The CPS uses a scientifi- cally derived panel sample of 60,000 occupied households. The participating households are surveyed for four months out of the sample of eight months, and then are sampled again for four more months before they are removed from the panel. Moreover, the sample is surveyed for each month on a week that contains the 19th of that month. Not surprisingly, the cost of conducting the CPS is measured in the millions of dollars.

The sophistication and detail of the CPS is required to ensure that accurate national statistics are captured on a monthly basis. As a result, the CPS is considered one of the standards by which other household surveys are conducted. The cost of the CPS, as well as the need for extensive telephone and field staff, really does represent a lot of “work”!

Source: U.S. Department of Labor, Bureau of Labor Statistics, and U.S. Department of Commerce, U.S. Census Bureau, Current Population Survey: Design and Methodology, Technical Paper 66 (2006).

Finding Out about Work Is a Lot of Work!

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for defining the target population is to answer questions about the crucial characteristics of the population. Does the term comic book reader include children under six years of age who do not actually read the words? Does all persons west of the Mississippi include people in east bank towns that border the river, such as East St. Louis, Illinois? The question to answer is, “Whom do we want to talk to?” The answer may be users, nonusers, recent adopters, or brand switchers.

To implement the sample in the field, tangible characteristics should be used to define the population. A baby food manufacturer might define the population as all women still capable of bearing children. However, a more specific operational definition would be women between the ages of 12 and 50. While this definition by age may exclude a few women who are capable of childbearing and include some who are not, it is still more explicit and provides a manageable basis for the sample design.

The Sampling Frame A list of elements from which the sample may be drawn is called a sampling frame. The sampling frame is also called the working population because these units will eventually provide units involved in analysis. A simple example of a sampling frame would be a list of all members of the American Medical Association or the list of students from the registrar’s office at a university.

Some firms, called sampling services or list brokers, specialize in providing lists or databases that include the names, addresses, phone numbers, and e-mail addresses of specific populations. Exhibit 16.2 shows a page from a mailing list company’s offerings. Lists offered by companies such as this are compiled from subscriptions to professional journals, credit card applications, war- ranty card registrations, and a variety of other sources. One sampling service obtained its listing

sampling frame

A list of elements from which a sample may be drawn; also

called working population.

EXHIBIT 16.1

Stages in the Selection of a Sample

Select a sampling frame

Determine if a probability or nonprobability sampling method will be chosen

Plan procedure for selecting sampling units

Determine sample size

Select actual sampling units

Conduct fieldwork

Define the target population

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388 • PART FIVE Sampling and Fieldwork

EXHIBIT 16.2

Mailing List Directory Page

of households with children from an ice cream retailer who gave away free ice cream cones on children’s birthdays. The children filled out cards with their names, addresses, and birthdays, which the retailer then sold to the mailing list company.

A valuable source of names are city directories, such as City Town Info (http://www . citytowninfo.com) and City Directories (http://www.uscitydirectories.com). Directories such as these provide complete, comprehensive, and accurate business and residential information. The reverse directory pages offer a unique benefit. A reverse directory provides, in a different format, the same information contained in a telephone directory. Listings may be by city and street address or by phone number, rather than alphabetical by last name. Such a directory is particularly useful when a research wishes to survey only a certain geographical area of a city or when census tracts are to be selected on the basis of income or another demographic criterion.

In practice, almost every list excludes some members of the population. For example, would a university e-mail directory provide an accurate sampling frame for a given university’s student population? Perhaps the sampling frame excludes students who registered late and includes stu- dents who have resigned from the university. The e-mail directory also will likely list only the student’s official university e-mail address. However, many students may not ever use this address, opting to use a private e-mail account instead. Thus, the university e-mail directory could not be expected to perfectly represent the student population. However, a perfect representation isn’t always possible or needed.

reverse directory

A directory similar to a telephone directory except that listings are by city and street address or by phone number rather than alphabetical by last name.

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CHAPTER 16 Sampling Designs and Sampling Procedures • 389

A sampling frame error occurs when certain sample elements are excluded or when the entire population is not accurately represented in the sampling frame. Election polling that used a tele- phone directory as a sampling frame would be contacting households with listed phone numbers, not households whose members are likely to vote. A better sampling frame might be voter registra- tion records. Another potential sampling frame error involving phone records is the possibility that a phone survey could underrepresent people with disabilities. Some disabilities, such as hearing and speech impairments, might make telephone use impossible. However, when researchers in Wash- ington State tested for this possible sampling frame error by comparing Census Bureau data on the prevalence of disability with the responses to a telephone survey, they found the opposite effect. The reported prevalence of a disability was actually higher in the phone survey.2 These findings could be relevant for research into a community’s health status or the level of demand for services for disabled persons.

As in this example, population elements can be either under- or overrepresented in a sam- pling frame. A savings and loan defined its population as all individuals who had savings accounts. However, when it drew a sample from the list of accounts rather than from the list of names of individuals, individuals who had multiple accounts were overrepresented in the sample.

Sampling Frames for International Research The availability of sampling frames around the globe varies dramatically. Not every country’s government conducts a census of population. In some countries telephone directories are incom- plete, no voter registration lists exist, and accurate maps of urban areas are unobtainable. However, in Taiwan, Japan, and other Asian countries, a researcher can build a sampling frame relatively easily because those governments release some census information. If a family changes households, updated census information must be reported to a centralized government agency before com- munal services (water, gas, electricity, education, and so on) are made available.3 This information is then easily accessible in the local Register of Inhabitants.

Sampling Units During the actual sampling process, the elements of the population must be selected according to a certain procedure. The sampling unit is a single element or group of elements subject to selection in the sample. For example, if an airline wishes to conduct a study of passenger satisfaction, it may take every twenty-fifth name on a complete list of all passengers. In this case the sampling unit would be individual passengers, the same as the element. Alternatively, the airline could first select certain flights as the sampling unit and then select either all of the passengers or certain passengers on each selected flight. In this case the sampling unit would be flights rather than individual passengers.

If the target population has first been divided into units, such as airline flights, additional ter- minology must be used. A unit selected in the first stage of sampling is called a primary sampling unit (PSU). A unit selected in a successive stage of sampling is called a secondary sampling unit or (if three stages are necessary) tertiary sampling unit. When there is no list of population elements, the sampling unit generally is something other than the population element. In a random-digit dialing study, the sampling unit will be telephone numbers.

Random Sampling and Nonsampling Errors An advertising agency sampled a small number of shoppers in grocery stores that used Shopper’s Video, an in-store advertising network. The agency hoped to measure brand awareness and pur- chase intentions. Investigators expected this sample to be representative of the grocery-shopping population. However, if a difference exists between the value of a sample statistic of interest (for example, the sample group’s average willingness to buy the advertised brand) and the value of the corresponding population parameter (the population’s average willingness to buy), a statistical error

sampling frame error

An error that occurs when certain sample elements are not listed or are not

accurately represented in a sampling frame.

sampling unit

A single element or group of elements subject to selection in

the sample.

primary sampling unit (PSU)

A term used to designate a unit selected in the first stage of

sampling.

secondary sampling unit

A term used to designate a unit selected in the second stage of

sampling.

tertiary sampling unit

A term used to designate a unit selected in the third stage of

sampling.

390 • PART FIVE Sampling and Fieldwork

has occurred. Two basic causes of differences between statistics and parameters were introduced in an earlier chapter and are described below:

1. random sampling errors 2. systematic (nonsampling) error

Random Sampling Error Random sampling error is the difference between the sample result and the result of a census con- ducted using identical procedures. Random sampling error occurs because of chance variation in the scientific selection of sampling units. The sampling units, even if properly selected according to sampling theory, are not likely to perfectly represent the population, but generally they are reliable estimates. Our discussion on the process of randomization (a procedure designed to give everyone in the population an equal chance of being selected as a sample member) will show that, because random sampling errors follow chance variations, they tend to cancel one another out when aver- aged. This means that properly selected samples, while perhaps not perfect, are generally good approximations of the population. Still, the true population value almost always differs slightly from the sample value, causing a small random sampling error. Every once in a while, an unusual sample is selected because too many atypical people were included in the sample and a large random sam- pling error occurred.

Random sampling error is a function of sample size. As sample size increases, random sampling error decreases. It is possible to estimate the random sampling error that may be expected with various sample sizes. Suppose a survey of approximately 1,000 people has been taken in Fresno to determine the feasibility of a new soccer franchise. Assume that 30 percent of the respondents favor the idea of a new professional sport in town. The researcher will know, based on the laws of probability, that 95 percent of the time a survey of slightly fewer than 900 people will produce results with an error of approximately plus or minus 3 percent. If the survey were conducted with only 325 people, the margin of error would increase to approximately plus or minus 5 percentage points. This example illustrates random sampling errors.

Systematic Sampling Error Systematic (nonsampling) errors result from nonsampling factors, primarily the nature of a study’s design and the correctness of execution. These errors are not due to chance fluctuations. For example, highly educated respondents are more likely to cooperate with mail surveys than poorly educated ones, for whom filling out forms is more difficult and intimidating. Sample biases such as these account for a large portion of errors in business research. The term sample bias is somewhat unfortunate, because many forms of bias are not related to the selection of the sample.

We discussed nonsampling errors in Chapter 8. Errors due to sample selection problems, such as sampling frame errors, are systematic (nonsampling) errors and should not be classified as ran- dom sampling errors.

Less Than Perfectly Representative Samples Random sampling errors and systematic errors associated with the sampling process may com- bine to yield a sample that is less than perfectly representative of the population. Exhibit 16.3 illustrates two nonsampling errors (sampling frame error and nonresponse error) related to sample design. The total population is represented by the area of the largest square. Sampling frame errors eliminate some potential respondents. Random sampling error (due exclusively to random, chance fluctuation) may cause an imbalance in the representativeness of the group. Additional errors will occur if individuals refuse to be interviewed or cannot be contacted. Such nonresponse error may also cause the sample to be less than perfectly representative. Thus, the actual sample is drawn from a population different from (or smaller than) the ideal.

random sampling error

The difference between the sample result and the result of a census conducted using identical procedures.

CHAPTER 16 Sampling Designs and Sampling Procedures • 391

Probability versus Nonprobability Sampling Several alternative ways to take a sample are available. The main alternative sampling plans may be grouped into two categories: probability techniques and nonprobability techniques.

In probability sampling, every element in the population has a known, nonzero probability of selection. In addition, a probability sample has an element of true randomness in the selection process. The simple random sample, in which each member of the population has an equal prob- ability of being selected, is the best-known probability sample.

In nonprobability sampling, the probability of any particular member of the population being chosen is unknown. The selection of sampling units in nonprobability sampling is quite arbitrary, as researchers rely heavily on personal judgment. Technically, no appropriate statistical techniques exist for measuring random sampling error from a nonprobability sample. Therefore, projecting the data beyond the sample is, technically speaking, statistically inappropriate. Nevertheless, as the “How Much Does Your Prescription Cost? It Depends on Who You Buy It From” Research Snapshot shows on prescrip- tion drug costs shows, researchers sometimes find nonprobability samples suitable for a specific researcher purpose. As a result, nonprobability samples are pragmatic and are used in business research.

Nonprobability Sampling Although probability sampling is preferred, we will discuss nonprobability sampling first to illus- trate some potential sources of error and other weaknesses in sampling.

Convenience Sampling As the name suggests, convenience sampling refers to sampling by obtaining people or units that are conveniently available. A research team may determine that the most convenient and economi- cal method is to set up an interviewing booth from which to intercept consumers at a shopping center. Just before elections, television stations often present person-on-the-street interviews that

probability sampling

A sampling technique in which every member of the

population has a known, nonzero probability of

selection.

nonprobability sampling

A sampling technique in which units of the sample are selected

on the basis of personal judgment or convenience; the

probability of any particular member of the population being chosen is unknown.

convenience sampling

The sampling procedure of obtaining those people or units

that are most conveniently available.

EXHIBIT 16.3

Errors Associated with Sampling

Total population Sampling frame Planned sample Respondents (actual sample)

Sampling frame error

Random sampling error

Nonresponse error

Source: Adapted from Cox, Keith K. and Ben M. Enis, The Marketing Research Process (Pacific Palisades, CA: Goodyear, 1972); and Bellenger, Danny N. and Barnet A. Greenberg, Marketing Research: A Management Information Approach (Homewood, IL: Richard D. Irwin, 1978), pp. 154–155.

392 • PART FIVE Sampling and Fieldwork

393

‘‘A straw vote only shows which way the hot air blows.’’ —O. HENRY

are presumed to reflect public opinion. (Of course, the television station generally warns that the survey was “unscientific and random” [sic].) The college professor who uses his or her students has a captive sample—convenient, but perhaps not so representative.

Researchers generally use convenience samples to obtain a large number of completed ques- tionnaires quickly and economically, or when obtaining a sample through other means is imprac- tical. For example, many Internet surveys are conducted with volunteer respondents who, either intentionally or by happenstance, visit an organization’s website. Although this method produces a large number of responses quickly and at a low cost, selecting all visitors to a website is clearly convenience sampling. Respondents may not be representative because of the haphazard manner by which many of them arrived at the website or because of self-selection bias.

Similarly, research looking for cross-cultural differences in organizational or consumer behav- ior typically uses convenience samples. Rather than selecting cultures with characteristics relevant to the hypothesis being tested, the researchers conducting these studies often choose cultures to which they have access (for example, because they speak the language or have contacts in that cul- ture’s organizations). Further adding to the convenience, cross-cultural research often defines “cul- ture” in terms of nations, which are easier to identify and obtain statistics for, even though many nations include several cultures and some people in a given nation may be more involved with the international business or academic community than with a particular ethnic culture.4 Here again, the use of convenience sampling limits how well the research represents the intended population.

The user of research based on a convenience sample should remember that projecting the results beyond the specific sample is inappropriate. Convenience samples are best used for explor- atory research when additional research will subsequently be conducted with a probability sample.

Judgment Sampling Judgment (purposive) sampling is a nonprobability sampling technique in which an experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member. Researchers select samples that satisfy their specific purposes, even if they are not fully representative. The consumer price index (CPI) is based on a judgment sample of market-basket items, housing costs, and other selected goods and services expected to reflect a representative sample of items consumed by most Americans. Test-market cities often are selected because they are viewed as typical cities whose demographic profiles closely match the national profile. A fashion manufacturer regularly selects a sample of key accounts that it believes are capable of providing information needed to predict what may sell in the fall. Thus, the sample is selected to achieve this specific objective.

judgment (purposive) sampling

A nonprobability sampling technique in which an experienced individual selects the sample based on personal judgment about some appropriate characteristic of the sample member.

RESEARCH SNaPSHOt

Many people are sensitive to the costs of their prescription drugs. For some drugs, these costs can make up a significant part of a person’s monthly or yearly budget. Generally speak- ing, however, most people would expect that their prescriptions would cost about the same, no matter where they buy them. After a number of complaints to the contrary, the state of Michigan sought to answer that very question.

The attorney general of the state of Michigan commis- sioned a targeted survey of 200 pharmacies to capture drug prescription costs for 11 common drugs used by people within the state. The survey was further focused on 10 specific communities, to include Detroit and Grand Rapids, as well as the Upper Peninsula of the State of Michigan.

Since the sample was drawn purposely, there was confidence that the survey would lead to some fruitful insights. Not sur- prisingly, the results confirmed the complaints of customers to the attorney general. Prices for the same prescription could vary as much as $100, and the variation may exist even though pharmacies were quite literally “down the block.” Long term, the use of a carefully drawn sample led to a consumer alert from the attorney general’s office— encouraging customers to shop carefully for their prescription drugs in the state.

Source: May 2007 Prescription Drug Survey Summary, Office of the Attorney General, State of Michigan (May 2007).

How Much Does Your Prescription Cost? It Depends on Who You Buy It From

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Judgment sampling often is used in attempts to forecast election results. People frequently wonder how a television network can predict the results of an election with only 2 percent of the votes reported. Political and sampling experts judge which small voting districts approximate overall state returns from previous election years; then these bellwether precincts are selected as the sampling units. Of course, the assumption is that the past voting records of these districts are still representative of the political behavior of the state’s population.

Quota Sampling Suppose your university administration wants to investigate the experiences of the undergraduate student body. While 95% of the students are full-time, the administration may wish to ensure that both full-time and part-time students are included in the sample. In this case, the researcher may decide to have the sample of 200 students consist of 150 full-time and 50 part-time students. This quota would insure that the part-time students are well represented, while strict probability sam- pling procedures might not include a sufficient number of those students (10).

As seen in the Research Snapshot “American Kennel Club Tries to Keep Pet Owners out of the Doghouse”, the purpose of quota sampling is to ensure that the various subgroups in a popula- tion are represented on pertinent sample characteristics to the exact extent that the investigators desire. Stratified sampling, a probability sampling procedure described in the next section, also has this objective, but it should not be confused with quota sampling. In quota sampling, the inter- viewer has a quota to achieve. For example, the interviewer may be assigned 100 interviews, 75 with full-time students and 25 with part-time students. The interviewer is responsible for finding enough people to meet the quota. Aggregating the various interview quotas yields a sample that represents the desired proportion of each subgroup.

Possible Sources of Bias The logic of classifying the population by pertinent subgroups is essentially sound. However, because respondents are selected according to a convenience sampling procedure rather than on a

quota sampling

A nonprobability sampling procedure that ensures

that various subgroups of a population will be represented

on pertinent characteristics to the exact extent that the

investigator desires.

The American Kennel Club (AKC) is an organization dedicated to promoting purebred dogs and their health and well-being as family companions. So the organization commissioned a study to investigate dog ownership and the acceptance of dogs in their neighborhoods. The AKC used quota sampling in its recent Dog Ownership Study, which set out to compare atti- tudes of dog owners and nonowners, based on a sample of one thousand people. In such a small sample of the U.S. population, some groups might not be represented, so the study design set quotas for completed interviews in age, sex, and geographic categories. The primary sampling units for this phone survey were selected with random-digit dialing. In the next phase of selection, the researchers ensured that respondents filled the quotas for each group. They further screened respondents so that half owned dogs and half did not.

An objective of the survey was to help dog owners under- stand concerns of their neighbors so that the AKC can provide better education in responsible dog ownership, contributing to greater community harmony. The study found that people

without dogs tended to be most concerned about dogs jumping and barking and owners not “picking up after their dogs.” Lisa Peterson, director of club commu- nications for AKC, commented, “Anyone considering bringing a dog home should realize that it’s a 10- to 15-year commitment of time, money, and love that should not be taken lightly.”

The study addressed the pleasures of a pet’s companion- ship, as well as the duties. A benefit of ownership was that dog owners were somewhat more likely than nonowners to describe themselves as laid-back and happy.

Sources: “AKC Mission Statement” and “History of the American Kennel Club,” American Kennel Club, http://www.akc.org, accessed August 12, 2011; “AKC Responsible Dog Ownership Day Survey Reveals Rift between Dog and Non-Dog Owners,” American Kennel Club news release (September 7, 2005), http://www.akc.org/news/index.cfm?article_id=2607, accessed August 12, 2011.

American Kennel Club Tries to Keep Pet Owners out of the Doghouse

RESEARCH SNaPSHOt

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probability basis (as in stratified sampling), the haphazard selection of subjects may introduce bias. For example, a college professor hired some of his students to conduct a quota sample based on age. When analyzing the data, the professor discovered that almost all the people in the “under 25 years” category were college-educated. Interviewers, being human, tend to prefer to interview people who are similar to themselves.

Quota samples tend to include people who are easily found, willing to be interviewed, and middle class. Fieldworkers are given considerable leeway to exercise their judgment concerning selection of actual respondents. Interviewers often concentrate their interviewing in areas with heavy pedestrian traffic such as downtowns, shopping malls, and college campuses. Those who interview door-to-door learn quickly that quota requirements are difficult to meet by interviewing whoever happens to appear at the door. People who are more likely to stay at home generally share a less active lifestyle and are less likely to be meaningfully employed. One interviewer related a story of working in an upper- middle-class neighborhood. After a few blocks, he arrived in a neighborhood of mansions. Feeling that most of the would-be respondents were above his station, the interviewer skipped these houses because he felt uncomfortable knocking on doors that would be answered by these people or their hired help.

Advantages of Quota Sampling The major advantages of quota sampling over probability sampling are speed of data collection, lower costs, and convenience. Although quota sampling has many problems, carefully supervised data collection may provide a representative sample of the various subgroups within a popula- tion. Quota sampling may be appropriate when the researcher knows that a certain demographic group is underrepresented in the population or more likely to refuse to cooperate with a survey. For instance, if older men are more likely to refuse, a higher quota can be set for this group so that the proportion of each demographic category will be similar to the proportions in the population. A number of laboratory experiments also rely on quota sampling because it is difficult to find a sample of the general population willing to visit a laboratory to participate in an experiment.

Snowball Sampling A variety of procedures known as snowball sampling involve using probability methods for an initial selection of respondents and then obtaining additional respondents through information provided by the initial respondents. This technique is best used to locate members of rare populations by referrals. Suppose a manufacturer of sports equipment is considering marketing a mahogany croquet set for serious adult croquet players. This market is certainly small. An extremely large sample would be necessary to find 100 serious adult croquet players. It would be much more economical to survey, say, 300 people, find perhaps two croquet players, and ask them for the names of other players.

Reduced sample sizes and costs are clear-cut advantages of snowball sampling. However, bias is likely to enter into the study because a person suggested by someone also in the sample has a higher probability of being similar to the first person. If there are major differences between those who are widely known by others and those who are not, this technique may present some seri- ous problems. However, snowball sampling may be used to locate and recruit heavy users, such as consumers who download more than 500 songs per year, for focus groups. As the focus group is not expected to be a generalized sample, snowball sampling may be appropriate.

Probability Sampling All probability sampling techniques are based on chance selection procedures. Because the proba- bility sampling process includes an element of true randomness, the bias inherent in nonprobability sampling procedures is eliminated. Note that the term random refers to the procedure for select- ing the sample; it does not describe the data in the sample. Randomness characterizes a procedure whose outcome cannot be predicted because it depends on chance. Randomness should not be thought of as unplanned or unscientific—it is the basis of all probability sampling techniques. This section will examine the various probability sampling methods.

snowball sampling

A sampling procedure in which initial respondents are selected by probability methods and additional respondents are obtained from information provided by the initial respondents.

‘‘Make everything as simple as possible, but not simpler.’’—ALBERT EINSTEIN

CHAPTER 16 Sampling Designs and Sampling Procedures • 395

Simple Random Sampling The sampling procedure that ensures each element in the population will have an equal chance of being included in the sample is called simple random sampling. The simplest examples include drawing names from a hat and selecting the winning raffle ticket from a large drum. If the names or raffle tickets are thoroughly stirred and on the same size piece of paper, each person or ticket should have an equal chance of being selected. In contrast to other, more complex types of prob- ability sampling, this process is simple because it requires only one stage of sample selection.

Although drawing names or numbers out of a fishbowl, using a spinner, rolling dice, or turn- ing a roulette wheel may be an appropriate way to draw a sample from a small population, when populations consist of large numbers of elements, sample selection is based on tables of random numbers (see Table A.1 in the online Appendix) or computer-generated random numbers.

Suppose a researcher is interested in selecting a simple random sample of all the Honda dealers in California, New Mexico, Arizona, and Nevada. Each dealer’s name is assigned a number from 1 to 105. The numbers can be written on paper slips, and all the slips can be placed in a bowl. After the slips of paper have been thoroughly mixed, one is selected for each sampling unit. Thus, if the sample size is 35, the selection procedure must be repeated 34 times after the first slip has been selected. Mixing the slips after each selection will ensure that those at the bottom of the bowl will continue to have an equal chance of being selected in the sample.

To use a table of random numbers, a serial number is first assigned to each element of the population. Assuming the population is 99,999 or fewer, five-digit numbers may be selected from the table of random numbers merely by reading the numbers in any column or row, moving up, down, left, or right. A random starting point should be selected at the outset. For convenience, we will assume that we have randomly selected as our starting point the first five digits in columns 1 through 5, row 1, of Table A.1 in the online Appendix. The first number in our sample would be 37751; moving down, the next numbers would be 50915, 99142, and so on.

The random-digit dialing technique of sample selection requires that the researcher identify the exchange or exchanges of interest (the first three numbers) and then use a table of numbers to select the next four numbers. In practice, the exchanges are not always selected randomly. Researchers who wanted to find out whether Americans of African descent prefer being called “black” or “African-American” narrowed their sampling frame by selecting exchanges associated with geographic areas where the proportion of the population (African-Americans/blacks) was at least 30 percent. The reasoning was that this made the survey procedure far more efficient, considering that the researchers were trying to contact a group representing less than 13 percent of U.S. households. This initial judgment sampling raises the same issues we discussed regarding nonprobability sampling. In this study, the researchers found that respondents were most likely to prefer the term black if they had attended schools that were about half black and half white.5 If such experiences influence the answers to the question of interest to the researchers, the fact that

blacks who live in predominantly white communi- ties are underrepresented may introduce bias into the results.

Systematic Sampling Suppose a researcher wants to take a sample of 1,000 from a list of 200,000 names. With systematic sampling, every 200th name from the list would be drawn. The procedure is extremely simple. A starting point is selected by a random process; then every nth number on the list is selected. To take a sample of consumers from a rural telephone direc- tory that does not separate business from residential listings, every 23rd name might be selected as the sampling interval. In the process, Harvest Cafe might be selected. This unit is inappropriate because

simple random sampling

A sampling procedure that assures each element in the

population of an equal chance of being included in the

sample.

systematic sampling

A sampling procedure in which a starting point is selected by

a random process and then every nth number on the list is

selected.

Random number tables are also found on the Internet. This is just one example.

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it is a business listing rather than a consumer listing, so the next eligible name would be selected as the sampling unit, and the systematic process would continue.

While systematic sampling is not actually a random selection procedure, it does yield random results if the arrangement of the items in the list is random in character. The problem of periodicity occurs if a list has a systematic pattern—that is, if it is not random in character. Collecting retail sales information every seventh day would result in a distorted sample because there would be a sys- tematic pattern of selecting sampling units—sales for only one day of the week (perhaps Monday) would be sampled. If the first 50 names on a list of contributors to a charity were extremely large donors, periodicity bias might occur. Similarly, if the list of customers is based on a customer number that was assigned when the business relationship began, the oldest customers would have the lowest numbers. It is important to select a sampling interval that allows the researcher to go through the entire sampling frame to minimize the potential impact of periodicity.

Stratified Sampling The usefulness of dividing the population into subgroups, or strata, whose members are more or less equal with respect to some characteristic was illustrated in our discussion of quota sampling. The first step is the same for both stratified and quota sampling: choosing strata on the basis of existing information—for example, classifying retail outlets based on annual sales volume. However, the process of selecting sampling units within the strata differs substantially. In stratified sampling, a subsample is drawn using simple random sampling within each stratum. This is not true of quota sampling.

The reason for taking a stratified sample is to obtain a more efficient sample than would be possible with simple random sampling. Suppose, for example, that urban and rural groups have widely different attitudes toward energy conservation, but members within each group hold very similar attitudes. Random sampling error will be reduced with the use of stratified sampling, because each group is internally homogeneous but there are comparative differences between groups. More technically, a smaller standard error may result from this stratified sampling because the groups will be adequately represented when strata are combined.

Another reason for selecting a stratified sample is to ensure that the sample will accurately reflect the population on the basis of the criterion or criteria used for stratification. This is a con- cern because occasionally simple random sampling yields a disproportionate number of one group or another and the sample ends up being less representative than it could be.

A researcher can select a stratified sample as follows. First, a variable (sometimes several variables) is identified as an efficient basis for stratification. A stratification variable must be a characteristic of the population elements known to be related to the dependent variable or other variables of interest. The variable chosen should increase homogeneity within each stratum and increase heterogeneity between strata. In other words, we want the elements within each strata to be similar, but differences to exist between the strata. The stratification variable usually is a categorical variable or one easily converted into categories (that is, subgroups). For example, a pharmaceutical company interested in measuring how often physicians prescribe a certain drug might choose physicians’ training as a basis for stratification. In this example the mutually exclusive strata are MDs (medical doctors) and ODs (osteopathic doctors).

Next, for each separate subgroup or stratum, a list of population elements must be obtained. (If such lists are not available, they can be costly to prepare, and if a complete listing is not available, a true strati- fied probability sample cannot be selected.) Using a table of random numbers or some other device, a separate simple random sample is then taken within each stratum. Of course, the researcher must determine how large a sample to draw for each stratum. This issue is discussed in the following section.

Proportional versus Disproportional Sampling If the number of sampling units drawn from each stratum is in proportion to the relative population size of the stratum, the sample is a proportional stratified sample. Consider the percentages of retail outlets presented in Exhibit 16.4. A proportional sample would have the same percentages as in the population.

In a disproportional stratified sample the sample size for each stratum is not allocated in propor- tion to the population size but is dictated by analytical considerations, such as variability in store sales

stratified sampling

A probability sampling procedure in which simple random subsamples that are more or less equal on some characteristic are drawn from within each stratum of the population.

proportional stratified sample

A stratified sample in which the number of sampling units drawn from each stratum is in proportion to the population size of that stratum.

disproportional stratified sample

A stratified sample in which the sample size for each stra- tum is allocated according to analytical considerations.

CHAPTER 16 Sampling Designs and Sampling Procedures • 397

volume. For example, although the percentage of warehouse club stores is small, the average dollar sales volume for the warehouse club store stratum is quite large and varies substantially from the average sales volume for the smaller independent stores. To avoid overrepresenting the chain stores and inde- pendent stores (with smaller sales volume) in the sample, a disproportional sample could be taken.

A disproportional stratified sample could also be selected to ensure an adequate number of sam- pling units in every stratum. Sampling more heavily in a given stratum than its relative population size is not a problem if the primary purpose of the research is to estimate some characteristic separately for each stratum and if researchers are concerned about assessing the differences among strata.

Cluster Sampling The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. Consider a researcher who must conduct five hundred personal interviews with consumers scattered throughout the United States. Travel costs are likely to be enormous because the amount of time spent traveling will be substantially greater than the time spent in the interviewing process. If an aspirin marketer can assume the product will be equally successful in Phoenix and Baltimore, or if a frozen pizza manufacturer assumes its product will suit the tastes of Texans equally as well as Oregonians, cluster sampling may be used to represent the United States.

In a cluster sample, the primary sampling unit is no longer the individual element in the popula- tion (for example, grocery stores) but a larger cluster of elements located in proximity to one another (for example, cities). The area sample is the most popular type of cluster sample. A grocery store researcher, for example, may randomly choose several geographic areas as primary sampling units and then interview all or a sample of grocery stores within the geographic clusters. Interviews are confined to these clusters only. No interviews occur in other clusters. Cluster sampling is classified as a probability sampling technique because of either the random selection of clusters or the random selection of elements within each cluster. Some examples of clusters appear in Exhibit 16.5.

Cluster samples frequently are used when lists of the sample population are not available. For example, when researchers investigating employees and self-employed workers for a down- town revitalization project found that a comprehensive list of these people was not available, they decided to take a cluster sample, selecting organizations (business and government) as the clusters. A sample of firms within the central business district was developed, using stratified probability sampling to identify clusters. Next, individual workers within the firms (clusters) were randomly selected and interviewed concerning the central business district.

Ideally a cluster should be as heterogeneous as the population itself—a mirror image of the population. A problem may arise with cluster sampling if the characteristics and attitudes of the elements within the cluster are too similar. For example, geographic neighborhoods tend to have residents of the same socioeconomic status. Students at a university tend to share similar beliefs. This problem may be mitigated by constructing clusters composed of diverse elements and by selecting a large number of sampled clusters.

cluster sampling

An economically efficient sampling technique in which the primary sampling unit is

not the individual element in the population but a large

cluster of elements; clusters are selected randomly.

EXHIBIT 16.4

Disproportional Sampling: Hypothetical Example

Percentage in Population

Warehouse Clubs

Chain Stores

Small Independents

20%

57%

23%

Proportional Sample

20%

57%

23%

Disproportional Sample

50%

12%

38%

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398 • PART FIVE Sampling and Fieldwork

399

RESEARCH SNaPSHOt

A carefully planned telephone survey often involves multistage sampling. First the researchers select a sample of households to call, and then they select someone within each household to interview—not necessarily whoever answers the phone. Cecilie Gaziano, a researcher with Research Solutions in Minneapolis, conducted an analysis of various selection procedures used in prior research, looking for the methods that performed best in terms of generating a representative sample, achieving respondent cooperation, and minimizing costs.

Gaziano found several methods worth further consider- ation. One of these was full enumeration, in which the inter- viewer requests a list of all the adults living in the household, generates a random number, uses the number to select a name from that list, and asks to speak with that person. In a varia- tion of this approach, called the Kish method, the interviewer requests the number of males by age and the number of females by age, and then uses some form of randomization to select either a male or a female and a number—say, the oldest male or the third oldest female. A third method is to interview the person who last had a birthday.

In the studies Gaziano examined, the Kish method did not seem to discour- age respondents by being too intrusive. That method was popular because it came close to being random. The last-birthday method generated somewhat better cooperation rates, which may have made that method more efficient in terms of costs. However, some question whether the person on the phone accurately knows the birthdays of every household member, especially in households with several adults. Methods that request the gender of household members also address a challenge of getting a representative phone survey sample: females tend to answer the phone more often than males.

Sources: Gaziano, Cecilie, “Comparative Analysis of Within-Household Respondent Selection Techniques,” Public Opinion Quarterly 69 (Spring 2005), 124–157; Braman, S. (Ed.) (2003). Communication Researchers and Policy-Making. Cambridge, MA: MIT Press Sourcebooks.

Who’s at Home? Different Ways to Select Respondents

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EXHIBIT 16.5

Examples of ClustersPopulation Element Possible Clusters in the United States

U.S. Adult Population States Counties Metropolitan Statistical Areas Census Tracts Blocks Households

College Seniors Colleges

Manufacturing Firms Counties Metropolitan Statistical Areas Localities Plants

Airline Travelers Airports Planes

Sports Fans Football Stadiums Basketball Arenas Baseball Parks

Multistage Area Sampling So far we have described two-stage cluster sampling. Multistage area sampling involves two or more steps that combine some of the probability techniques already described. Typically, geo- graphic areas are randomly selected in progressively smaller (lower-population) units. For example, a political pollster investigating an election in Arizona might first choose counties within the state to ensure that the different areas are represented in the sample. In the second step, precincts within

multistage area sampling

Sampling that involves using a combination of two or more probability sampling techniques.

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the selected counties may be chosen. As a final step, the pollster may select blocks (or households) within the precincts, and then interview all the blocks (or households) within the geographic area. Researchers may take as many steps as necessary to achieve a representative sample. Exhibit 16.6 graphically portrays a multistage area sampling process in the United States frequently used by a major academic research center. Progressively smaller geographic areas are chosen until a single housing unit is selected for interviewing.

The U.S. Census Bureau provides maps, population information, demographic characteris- tics for population statistics, and so on, by several small geographical areas; these may be useful in sampling. Census classifications of small geographical areas vary, depending on the extent of urbanization within Metropolitan Statistical Areas (MSAs) or counties. Exhibit 16.7 illustrates the geographic hierarchy inside urbanized areas.

EXHIBIT 16.6

Illustration of Multistage Area Sampling in the United States

Twp 1 Twp 2 Twp 3

Twp 4 Twp 6

Twp 7 Twp 9

Twp 10

CITY

Twp 5

Lifland Ave.

W al

to n

St .

Qu ic

a Bl

vd .

Wilhelm Way

Alley

W al

to n

St .

Alley

Town Town

Twp 11 Twp 12

Town Town

Segment

Chunk Sample Location

1 Primary Area

5 Housing Unit

Wilhelm Way

Source: From Interviewer’s Manual, Revised Edition (Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan, 1976), p. 36. Reprinted with permission.

400 • PART FIVE Sampling and Fieldwork

What Is the Appropriate Sample Design? A researcher who must decide on the most appropriate sample design for a specific project will identify a number of sampling criteria and evaluate the relative importance of each criterion before selecting a sampling design. This section outlines and briefly discusses the most common criteria. Exhibit 16.8 summarizes the advantages and disadvantages of each nonprobability sampling tech- nique, and Exhibit 16.9 does the same for the probability sampling techniques.

EXHIBIT 16.7

Geographic Hierarchy inside Urbanized Areas

Central City

County A

County C

County B County D

Elm St.

Maple St.

16 th

A ve

17 th

A ve

.Urbanized Area

Central City

Census Tract

Block Numbering

Area

Block

MSA Urbanized Area

Village

City

Source: From U.S. Bureau of the Census, “Geography—Concepts and Products,” Washington, DC, August 1985, p. 3.

EXHIBIT 16.8

Comparison of Sampling Techniques: Nonprobability Samples

Nonprobability Samples

Description Cost and Degree of Use Advantages Disadvantages

1. Convenience: The researcher uses the most convenient sample or economical sample units.

Very low cost, extensively used

No need for list of population

Unrepresentative samples likely; random sampling error estimates cannot be made; projecting data beyond sample is relatively risky

2. Judgment: An expert or experienced researcher selects the sample to fulfill a purpose, such as ensuring that all members have a certain characteristic.

Moderate cost, average use

Useful for certain types of forecasting; sample guaranteed to meet a specific objective

Bias due to expert’s beliefs may make sample unrepresentative; projecting data beyond sample is risky

3. Quota: The researcher classifies the population by pertinent properties, determines the desired proportion to sample from each class, and fixes quotas for each interviewer.

Moderate cost, very extensively used

Introduces some stratification of population; requires no list of population

Introduces bias in researcher’s classification of subjects; nonrandom selection within classes means error from population cannot be estimated; projecting data beyond sample is risky

4. Snowball: Initial respondents are selected by probability samples; additional respondents are obtained by referral from initial respondents.

Low cost, used in special situations

Useful in locating members of rare populations

High bias because sample units are not independent; projecting data beyond sample is risky

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CHAPTER 16 Sampling Designs and Sampling Procedures • 401

EXHIBIT 16.9

Comparison of Sampling Techniques: Probability Samples

Probability Samples

Description Cost and Degree of Use Advantages Disadvantages

1. Simple random: The researcher assigns each member of the sampling frame a number, and then selects sample units by random method.

High cost, moderately used in practice (most common in random digit dialing and with computerized sampling frames)

Only minimal advance knowledge of population needed; easy to analyze data and compute error

Requires sampling frame to work from; does not use knowledge of population that researcher may have; larger errors for same sampling size than in stratified sampling; respondents may be widely dispersed, hence cost may be higher

2. Systematic: The researcher uses natural ordering or the order of the sampling frame, selects an arbitrary starting point, and then selects items at a preselected interval.

Moderate cost, moderately used

Simple to draw sample; easy to check

If sampling interval is related to periodic ordering of the population, may introduce increased variability

3. Stratified: The researcher divides the population into groups and randomly selects subsamples from each group. Variations include proportional, disproportional, and optimal allocation of subsample sizes.

High cost, moderately used

Ensures representation of all groups in sample; characteristics of each stratum can be estimated and comparisons made; reduces variability for same sample size

Requires accurate information on proportion in each stratum; if stratified lists are not already available, they can be costly to prepare

4. Cluster: The researcher selects sampling units at random, then does a complete observation of all units or draws a probability sample in the group.

Low cost, frequently used

If clusters geographically defined, yields lowest field cost; requires listing of all clusters, but of individuals only within clusters; can estimate characteristics of clusters as well as of population

Larger error for comparable size than with other probability samples; researcher must be able to assign population members to unique cluster or else duplication or omission of individuals will result

5. Multistage: Progressively smaller areas are selected in each stage by some combination of the first four techniques.

High cost, frequently used, especially in nationwide surveys

Depends on techniques combined

Degree of Accuracy Selecting a representative sample is important to all researchers. However, the degree of accuracy required or the researcher’s tolerance for sampling and nonsampling error may vary from project to project, especially when cost savings or another benefit may be a trade-off for a reduction in accuracy.

For example, when the sample is being selected for an exploratory research project, a high priority may not be placed on accuracy because a highly representative sample may not be neces- sary. For other, more conclusive projects, the sample result must precisely represent a population’s characteristics, and the researcher must be willing to spend the time and money needed to achieve accuracy.

Resources The cost associated with the different sampling techniques varies tremendously. If the researcher’s financial and human resources are restricted, certain options will have to be eliminated. For a

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402 • PART FIVE Sampling and Fieldwork

403

graduate student working on a master’s thesis, conducting a global survey is almost always out of the question because of limited resources. Managers concerned with the cost of the research ver- sus the value of the information often will opt to save money by using a nonprobability sampling design rather than make the decision to conduct no research at all.

Time A researcher who needs to meet a deadline or complete a project quickly will be more likely to select a simple, less time-consuming sample design. As seen in the “Who’s at Home?” Research Snapshot a telephone survey that uses a sample based on random-digit dialing, when conducted carefully, takes considerably less time than a survey that uses an elaborate disproportional stratified sample.

Advance Knowledge of the Population Advance knowledge of population characteristics, such as the availability of lists of population members, is an important criterion. In many cases, however, no list of population elements will be available to the researcher. This is especially true when the population element is defined by ownership of a particular product or brand, by experience in performing a specific job task, or on a qualitative dimension. A lack of adequate lists may automatically rule out systematic sam- pling, stratified sampling, or other sampling designs, or it may dictate that a preliminary study, such as a short telephone survey using random digit dialing, be conducted to generate informa- tion to build a sampling frame for the primary study. In many developing countries, things like reverse directories are rare. Thus, researchers planning sample designs have to work around this limitation.

National versus Local Project Geographic proximity of population elements will influence sample design. When population elements are unequally distributed geographically, a cluster sample may become much more attractive.

RESEARCH SNaPSHOt

Transitions to new jobs, new cities, or new work environments can create physical and emotional stress on people. Stress and tension can also impact students when they first arrive at a college and university. The new environment, new classroom experience, and a lack of friends can create psychological distress that can lead to alcohol or substance abuse, physical health concerns, and mental stresses or strains. The question is how students adjust to this new environment. To answer this question, researchers conducted a panel study, where incoming students were assessed on their psychological traits and coping behaviors upon entry, and were then resurveyed at the end of their first year.

The results indicate that those students who engaged in negative coping behaviors or who had perfectionist tendencies were more likely to have poor adjustment outcomes after the

first year. However, those students who were optimistic and socially oriented were much more likely to adjust to the new college environment.

The use of a panel approach was necessary, since the researchers were interested in the change that occurred within a sample of students over time. These results can be used by college administrators to develop newcomer programs or experiences that students can use to adjust to their new college environment. College is stressful enough—it is critical that new students under- stand that help and support are there if they need it!

Source: Pritchard, M. E., G. S. Wilson, and B. Yamnitz, “What Predicts Adjustment among College Students? A Longitudinal Study,” Journal of American College Health 56, no. 1 (2007), 15–21.

New on Campus: Student Adjustment to College Life

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Internet Sampling Is Unique Internet surveys allow researchers to reach a large sample rapidly—both an advantage and a disadvantage. Sample size requirements can be met overnight or in some cases almost instanta- neously. A researcher can, for instance, release a survey one morning and have data back from around the globe by the next morning. If rapid response rates are expected, the sample for an Internet survey should be metered out across global regions or across all time zones in a national study. In addition, people in some populations are more likely to go online during the weekend than on a weekday. If the researcher can anticipate a day-of-the-week effect, the survey should be kept open long enough so that all sample units have the opportunity to participate in the research project.

The ease and low cost of an Internet survey also has contributed to a flood of online question- naires, some more formal than others. As a result, frequent Internet users may be more selective about which surveys they bother answering. Researchers investigating college students’ attitudes toward environmental issues found that those who responded to an e-mail request that had been sent to all students tended to be more concerned about the environment than students who were contacted individually through systematic sampling. The researchers concluded that students who cared about the issues were more likely to respond to the online survey.6

Another disadvantage of Internet surveys is the lack of computer ownership and Internet access among certain segments of the population. This is particularly true in regions of the world where Internet availability is limited. A sample of Internet users is representative only of Internet users, who tend to be younger, better educated, and more affluent than the general population. This is not to say that all Internet samples are unrepresentative of all target populations. Nevertheless, when using Internet surveys, researchers should be keenly aware of potential sampling problems that can arise due to systematic characteristics of heavy computer users.

Website Visitors As noted earlier, many Internet surveys are conducted with volunteer respondents who visit an organization’s website intentionally or by happenstance. These unrestricted samples are clearly convenience samples. They may not be representative because of the haphazard manner by which many respondents arrived at a particular website or because of self-selection bias.

A better technique for sampling website visitors is to randomly select sampling units. ComScore (http://www.comscore.com), a company that conducts Internet surveys and focus groups, can collect data by using its “pop-up survey” software. The software selects web visitors at random and “pops up” a small JavaScript window asking the person if he or she wants to participate in an evaluation survey. If the person clicks yes, a new window containing the online survey opens up. The person can then browse the site at his or her own pace and switch to the survey at any time to express an opinion.7

Randomly selecting website visitors can cause a problem. It is possible to overrepresent fre- quent visitors to the site and thus represent site visits rather than visitors. Several programming techniques and technologies (using cookies, registration data, or prescreening) are available to help accomplish more representative sampling based on site traffic.8

This type of random sampling is most valuable if the target population is defined as visitors to a particular website. Evaluation and analysis of visitors’ perceptions and experiences of the website would be a typical survey objective with this type of sample. Researchers who have broader inter- ests may obtain Internet samples in a variety of other ways.

Panel Samples Drawing a probability sample from an established consumer panel or other prerecruited mem- bership panel is a popular, scientific, and effective method for creating a sample of Internet users.

404 • PART FIVE Sampling and Fieldwork

Typically, sampling from a panel yields a high response rate because panel members have already agreed to cooperate with the research organization’s e-mail or Internet surveys. Often panel mem- bers are compensated for their time with a sweepstakes, a small cash incentive, or redeemable points. Further, because the panel has already supplied demographic characteristics and other information from previous questionnaires, researchers are able to select panelists based on product ownership, lifestyle, or other characteristics.

Consider Harris Interactive Inc., an Internet survey research organization that maintains a panel of millions of individuals in countries around the globe. In addition, Harris has over 20 spe- cialty panels such as Americans with Disabilities, Technology Decision Makers, Sports, and Lesbian, Gay, Bisexual and Transgender (LGBT).9 A database this large allows the company to draw simple random samples, stratified samples, and quota samples from its panel members.

Harris Interactive finds that two demographic groups are not fully accessible via Internet sampling: people ages 65 and older—a group that is rapidly growing—and those with annual incomes of less than $15,000. In contrast, 18- to 25-year-olds—a group that historically has been very hard to reach by traditional research methods—are now extremely easy to reach over the Internet.10

To ensure that survey results are representative, Harris Interactive uses a propensity-weighting scheme. The research company does parallel studies—by phone as well as over the Internet—to test the accuracy of its Internet data-gathering capabilities. Researchers look at the results of the telephone surveys and match those against the Internet-only survey results. Next, they use propen- sity weighting to adjust the results, taking into account the motivational and behavioral differences between the online and offline populations.

Recruited Ad Hoc Samples Another means of obtaining an Internet sample is to obtain or create a sampling frame of e-mail addresses on an ad hoc basis. Researchers may create the sampling frame offline or online. Databases containing e-mail addresses can be compiled from many sources, including customer/client lists, advertising banners on pop-up windows that recruit survey participants, online sweepstakes, and registration forms that must be filled out in order to gain access to a particular website. Research- ers may contact respondents by “snail mail” or by telephone to ask for their e-mail addresses and obtain permission for an Internet survey. Using offline techniques, such as random-digit dialing and short telephone screening interviews, to recruit respondents can be a very practical way to get a representative sample for an Internet survey. Companies anticipating future Internet research can develop a valuable database for sample recruitment by including e-mail addresses in their customer relationship databases (by inviting customers to provide that information on product registration cards, in telephone interactions, through on-site registration, etc.).11

Opt-in Lists Survey Sampling International specializes in providing sampling frames and scientifically drawn samples. The company offers more than 3,500 lists of high-quality, targeted e-mail addresses of individuals who have given permission to receive e-mail messages related to a particular topic of interest. Survey Sampling International’s database contains millions of Internet users who opt in for limited participation. An important feature of Survey Sampling International’s database is that the company has each individual confirm and reconfirm interest in communicating about a topic before the person’s e-mail address is added to the company’s database.12

By whatever technique the sampling frame is compiled, it is important not to send unauthorized e-mail to respondents. If individuals do not opt in to receive e-mail from a particular organization, they may consider unsolicited survey requests to be spam. A researcher cannot expect high response rates from individuals who have not agreed to be surveyed. Spamming is not tolerated by experienced Internet users and can easily backfire, creating a host of problems—the most extreme being complaints to the Internet service provider (ISP), which may shut down the survey site.

opt in

To give permission to receive selected e-mail, such as questionnaires, from a company with an Internet presence.

CHAPTER 16 Sampling Designs and Sampling Procedures • 405

SUMMARY

1. Explain reasons for taking a sample rather than a complete census. Sampling is a proce- dure that uses a small number of units of a given population as a basis for drawing conclusions about the whole population. Sampling often is necessary because it would be practically impossible to conduct a census to measure characteristics of all units of a population. Samples also are needed in cases where measurement involves destruction of the measured unit.

2. Describe the process of identifying a target population and selecting a sampling frame. The first problem in sampling is to define the target population. Incorrect or vague defini- tion of this population is likely to produce misleading results. A sampling frame is a list of elements, or individual members, of the overall population from which the sample is drawn. A sampling unit is a single element or group of elements subject to selection in the sample.

3. Compare random sampling and systematic (nonsampling) errors. There are two sources of discrepancy between the sample results and the population parameters. One, random sampling error, arises from chance variations of the sample from the population. Random sampling error is a function of sample size and may be estimated using the central-limit theorem, discussed in Chapter 17. Systematic, or nonsampling, error comes from sources such as sampling frame error, mistakes in recording responses, or nonresponses from persons who are not contacted or who refuse to participate.

4. Identify the types of nonprobability sampling, including their advantages and disadvan- tages. The two major classes of sampling methods are probability and nonprobability techniques. Nonprobability techniques include convenience sampling, judgment sampling, quota sampling, and snowball sampling. They are convenient to use, but there are no statistical techniques with which to measure their random sampling error.

5. Summarize the advantages and disadvantages of the various types of probability sam- ples. Probability samples are based on chance selection procedures. These include simple random sampling, systematic sampling, stratified sampling, and cluster sampling. With these techniques, random sampling error can be accurately predicted.

6. Discuss how to choose an appropriate sample design, as well as challenges for Internet sampling. A researcher who must determine the most appropriate sampling design for a spe- cific project will identify a number of sampling criteria and evaluate the relative importance of each criterion before selecting a design. The most common criteria concern accuracy require- ments, available resources, time constraints, knowledge availability, and analytical require- ments. Internet sampling presents some unique issues. Researchers must be aware that samples may be unrepresentative because not everyone has a computer or access to the Internet. Con- venience samples drawn from website visitors can create problems. Drawing a probability sample from an established consumer panel or an ad hoc sampling frame whose members opt in can be effective.

KEY TERMS AND CONCEPTS census, 385 cluster sampling, 398 convenience sampling, 392 disproportional stratified sample, 397 judgment (purposive) sampling, 393 multistage area sampling, 399 nonprobability sampling, 392 opt in, 405 population (universe), 385

population element, 385 primary sampling unit (PSU), 390 probability sampling, 392 proportional stratified sample, 397 quota sampling, 394 random sampling error, 391 reverse directory, 389 sample, 385 sampling frame, 388

sampling frame error, 390 sampling unit, 390 secondary sampling unit, 390 simple random sampling, 396 snowball sampling, 395 stratified sampling, 397 systematic sampling, 396 tertiary sampling unit, 390

406 Part 5: Sampling and Fieldwork

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. If you decide whether you want to see a new movie or televi-

sion program on the basis of the “coming attractions” or televi- sion commercial previews, are you using a sampling technique? A scientific sampling technique?

2. Name some possible sampling frames for the following: a. Electrical contractors b. Tennis players c. Dog owners d. Foreign-car owners e. Wig and hair goods retailers f. Minority-owned businesses g. Men over six feet tall

3. Describe the difference between a probability sample and a non- probability sample.

4. In what types of situations is conducting a census more appro- priate than sampling? When is sampling more appropriate than taking a census?

5. Comment on the following sampling designs: a. A citizen’s group interested in generating public and finan-

cial support for a new university basketball arena prints a questionnaire in area newspapers. Readers return the ques- tionnaires by mail.

b. A department store that wishes to examine whether it is losing or gaining customers draws a sample from its list of credit card holders by selecting every tenth name.

c. A motorcycle manufacturer decides to research consumer characteristics by sending one hundred questionnaires to each of its dealers. The dealers will then use their sales records to track down buyers of this brand of motorcycle and distribute the questionnaires.

d. An advertising executive suggests that advertising effective- ness be tested in the real world. A one-page ad is placed in a magazine. One-half of the space is used for the ad itself. On the other half, a short questionnaire requests that readers comment on the ad. An incentive will be given for the first thousand responses.

e. A research company obtains a sample for a focus group through organized groups such as church groups, clubs, and schools. The organizations are paid for securing respondents; no individual is directly compensated.

f. A researcher suggests replacing a consumer diary panel with a sample of customers who regularly shop at a supermar- ket that uses optical scanning equipment. The burden of recording purchases by humans will be replaced by comput- erized longitudinal data.

g. A banner ad on a business-oriented website reads, “Are you a large company Sr. Executive? Qualified execs receive $50 for less than 10 minutes of time. Take the survey now!” Is this an appropriate way to select a sample of business executives?

6. When would a researcher use a judgment, or purposive, sample? 7. A telephone interviewer asks, “I would like to ask you about

race. Are you Native American, Hispanic, African-American, Asian, or White?” After the respondent replies, the interviewer says, “We have conducted a large number of surveys with people of your background, and we do not need to question you further. Thank you for your cooperation.” What type of sampling is likely being used?

8. If researchers know that consumers in various geographic regions respond quite differently to a product category, such as hot sauces, is area sampling appropriate? Why or why not?

9. What are the benefits of stratified sampling? 10. What geographic units within a metropolitan area are useful for

sampling? 11. Researchers often are particularly interested in the subset of a

market that contributes most to sales (for example, heavy beer drinkers or large-volume retailers). What type of sampling might be best to use with such a subset? Why?

12. Outline the step-by-step procedure you would use to select the following: a. A simple random sample of 150 students at your university b. A quota sample of 50 light users and 50 heavy users of beer

in a shopping mall intercept study c. A stratified sample of 50 mechanical engineers, 40 electrical

engineers, and 40 civil engineers from the subscriber list of an engineering journal

13. Selection for jury duty is supposed to be a totally random process. Comment on the following computer selection proce- dures, and determine if they are indeed random: a. A program instructs the computer to scan the list of names

and pick names that were next to those from the last scan. b. Three-digit numbers are randomly generated to select jurors

from a list of licensed drivers. If the weight information listed on the license matches the random number, the per- son is selected.

c. The juror source list is obtained by merging a list of regis- tered voters with a list of licensed drivers.

14. EtHICS To ensure a good session, a company selects focus group members from a list of articulate participants instead of conduct- ing random sampling. The client did not inquire about sample selection when it accepted the proposal. Is this ethical?

RESEARCH ACTIVITIES 1. Phone directories are sometimes used for sampling frames.

Go to www.reversephonedirectory.com and put in a phone number of someone you know in the reverse phone search (the number must be a listed number to get results). Comment on the accuracy of the information and the appropriateness of phone directories as a representative sample.

2. ’NEt Go to the U.S. Census Bureau’s home page at http:// www.census.gov. Profiles of every state are available (you may

find the “Quick Facts” or “Population Finder” helpful) from this website. Find the data for Louisiana. Suppose a representa- tive sample of the state of Louisiana is used to represent the current U.S. population. How well does Louisiana represent the United States overall? How well does Louisiana represent California or Maine? Use the profiles of the states and of the country to form your opinion.

Chapter 16: Sampling Designs and Sampling Procedures 407

Washington Times columnist Gene Mueller writes about fishing and other outdoor sporting activities.13 Mueller commented recently that although inter-

est groups express concerns about the impact of saltwater fishers on the fish population, no one really knows how many people fish for recreation or how many fish they catch. This situation would chal- lenge marketers interested in the population of anglers.

How could a researcher get an accurate sample? One idea would be to contact residents of coastal counties using random-digit dialing. This sampling frame would include many, if not all, of the people who fish in the ocean, but it would also include many people who do not fish—or who fish for business rather than recreation. A regional agency seeking to gather statistics on anglers, the Atlantic Coastal Cooperative Statistics Program, prefers to develop a sampling frame more related to people who fish.

Another idea would be to use state fishing license records. Pri- vacy would be a drawback, however. Some people might not want

their records shared, and they might withhold phone numbers. Further complicating this issue for Atlantic fishing is that most states in the Northeast do not require a license for saltwater fishing. Also exempt in some states are people who fish from the shore and from piers.

A political action group called the Recreational Fishing Alliance suggests that charter fishing businesses collect data.

Questions 1. Imagine that an agency or business has asked for help in gather-

ing data about the number of sport anglers who fish off the coast of Georgia. What advice would you give about sampling? What method or combination of methods would generate the best results?

2. What other criteria besides accuracy would you expect to consider? What sampling methods could help you meet those criteria?

Who’s Fishing? CASE 16.1

Scientific Telephone Samples (STS), located in Santa Ana, California, specializes in selling sampling frames for marketing research.14 The STS sampling frame is

based on a database of all working residential telephone exchanges in the United States. Thus, STS can draw from any part of the country— no matter how large or how small. The information is updated sev- eral times per year and cross-checked against area code and assigned exchange lists furnished by telephone companies. Exchange and/or working blocks designated for business or governmental telephones, mobile phones, and other commercial services are screened out.

STS can furnish almost any type of random-digit sample desired, including • National samples (continental United States only, or with Alaska

and Hawaii) • Stratified national samples (by census region or division) • Census region or division samples • State samples • Samples by MSA • County samples • Samples by zip code • City samples by zip code • Exchange samples generated from lists of three-digit exchanges • Targeted random-digit dialing samples (including over 40 variables

and special databases for high-income areas, Hispanics, African- Americans, and Asians)

STS offers two different methods for pulling working blocks. Either method can be used regardless of the geographic sampling

unit (for example, state, county, zip). The two versions are Type A (unweighted) and Type B (weighted/efficient).

Type A samples are pulled using a strict definition of randomness. They are called “unweighted” samples because each working block has an equal chance of being selected to generate a random-digit number. Completed interviews from a Type A sample that has been dialed to exhaustion should be highly representative of the popula- tion under study.

Type B, or “efficient,” samples are preweighted, so random-digit dialing numbers are created from telephone working blocks in pro- portion to the number of estimated household listings in each work- ing block. Working blocks that are more filled with numbers will be more prevalent in a sample. For example, a working block that had 50 known numbers in existence would have twice the probability of being included as one that had just 25 numbers.

Type B samples are most useful when a researcher is willing to overlook a strict definition of randomness in favor of slightly more calling efficiency because of fewer “disconnects.” In theory, com- pleted interviews from Type B samples may tend to overrepresent certain types of working blocks, but many researchers feel there is not much difference in representativeness.

Questions 1. Evaluate the geographic options offered by STS. Do they seem

to cover all the bases? 2. Evaluate the STS method of random-digit dialing.

Scientific Telephone Samples

CASE 16.2

408 Part 5: Sampling and Fieldwork

409

After studying this chapter, you should be able to

1. Understand basic statistical terminology

2. Interpret frequency distributions, proportions, and measures of central tendency and dispersion

3. Distinguish among population, sample, and sampling distributions

4. Explain the central-limit theorem

5. Summarize the use of confidence interval estimates

6. Discuss major issues in specifying sample size

P ayment options have gone high-tech. Businesses that sell to consumers—and even charities that seek donations from individuals—need to plan for a wide range of choices beyond traditional

cash or checks. Today’s spenders are more likely to pay with a debit or credit card or through a variety of methods for elec- tronic transfer of funds. To measure this trend in more detail, researchers at the Federal Reserve conducted surveys of deposi- tory institutions (banks, savings and loan institutions, and credit unions), asking them to report the number of each type of pay- ment the institutions processed.1

In planning this survey, the Federal Reserve researchers carefully designed the sample, including the number of institutions to contact. The total number of depository insti- tutions in the United States was already known: 14,117. The researchers had to select enough institutions from this population to be confident that the answers would be rep- resentative of transactions nationwide. A stratified random sample was used so that each type of institution would be

included. The researchers had conducted a similar survey three years earlier and obtained a 54 percent response rate, so they assumed the rate would be similar. Using techniques such as those described in this chapter, the researchers deter- mined that, given the total number of institutions and the response rate, they would need to sample 2,700 depository

LEARNING OUTCOMES

Determination of Sample Size A Review of Statistical Theory

Chapter Vignette:

Federal Reserve Finds Cards are Replacing Cash

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Introduction The first six sections of this chapter summarize key statistical concepts necessary for understanding the theory that underlies the calculation of sample size. These sections are intended for students who need to review the basic aspects of statistics theory. Even those students who received good grades in their elementary statistics classes probably will benefit from a quick review of the basic statistical concepts. Some students will prefer to just skim this material and proceed to page 430, where the discussion of the actual determination of sample size begins. Others need to study these sections carefully to acquire an understanding of statistics.

Descriptive and Inferential Statistics The Statistical Abstract of the United States presents table after table of figures associated with the number of births, the number of employees in each county of the United States, and other data that the average person calls “statistics.” Technically, these are descriptive statistics,which describe basic characteristics and summarize the data in a straightforward and understandable manner. Another type of statistics, inferential statistics, is used to make inferences or to project from a sample to an entire population. For example, when a firm test-markets a new product in Peoria and Fort Worth, it is not only concerned about how customers in these two cities feel, but they want to make an inference from these sample markets to predict what will happen throughout the United States. So, two applications of statistics exist: (1) descriptive statistics that describe characteristics of the population or sample and (2) inferential statistics that are used to generalize from a sample to a population.

Sample Statistics and Population Parameters A sample is a subset or relatively small portion of the total number of elements in a given popula- tion. Sample statistics are measures computed from sample data. Since business researchers typi- cally deal with samples—we rarely talk to every consumer, manager, or organization—we normally base our decisions off of sample data. The primary purpose of inferential statistics is to make a judgment about a population, or the total collection of all elements about which a researcher seeks information, based from a subset of that population.

Population parameters are measured characteristics of a specific population. In other words, they are information about the entire universe of interest. Sample statistics are used to make infer- ences (guesses) about population parameters based on sample data.2 In our notation, we will gen- erally represent population parameters with Greek lowercase letters—for example, m or a—and sample statistics with English letters, such as X or S.

descriptive statistics

Statistics that summarize and describe the data in a simple and understandable manner.

inferential statistics

Using statistics to project characteristics from a sample to

an entire population.

sample statistics

Variables in a sample or measures computed from

sample data.

population parameters

Variables in a population or measured characteristics of the

population.

institutions to obtain results that they could say, with 95 percent confidence, were accurate to within 65 percent.

With a response rate just above that of the prior survey, 1,500 institutions responded, giving data on the number of transactions processed in each payment category. Their responses confirmed earlier analysis showing that the number of checks paid in the United States is declining while the num- ber of electronic payments is increasing. Because this survey measured institutional transactions, it could not count the num- ber of purchases made with cash.

Formally identifying the proper sample size requires applied statistical theory. We understand that the word statistics often inspires dread among students. However, when a would-be researcher learns a few tricks of the trade, using statistics can become second nature. Many of these “tricks” involve simply learning the specialized language of statisticians. If you do not understand the basics of the language, you will have problems in conversation. Statistics is the language of the researcher. This chapter reviews some of the basic terminology of statistical analy- sis and applies statistical principles to the process of determining a sample size.

410 • PART FIVE Sampling and Fieldwork

Making Data Usable Suppose a telephone survey has been conducted for a savings and loan association. The data have been recorded on a large number of questionnaires. To make the data usable, this information must be organized and summarized. Methods for doing this include frequency distributions, propor- tions, measures of central tendency, and measures of dispersion.

Frequency Distributions One of the most common ways to summarize a set of data is to construct a frequency table, or frequency distribution. The process begins with recording the number of times a particular value of a variable occurs. This is the frequency of that value. Using an example of a telephone survey for a savings and loan association, Exhibit 17.1 represents a frequency distribution of respondents’ answers to a question that asked how much money customers had deposited in the institution. In this case, we can see that respondents most frequently checked the highest box of $12,000 or more (811).

A similar method of describing the data is to construct a distribution of relative frequency, or a percentage distribution. To develop a frequency distribution of percentages, divide the frequency of each value by the total number of observations, and multiply the result by 100. Based on the data in Exhibit 17.1, Exhibit 17.2 shows the percentage distribution of deposits; that is, the percentage of people holding deposits within each range of values. The highest percentage is in the top range, with 26 percent of all of the respondents.

percentage distribution

A frequency distribution organized into a table (or graph) that summarizes percentage values associated with particular values of a variable.

frequency distribution

A set of data organized by summarizing the number of times a particular value of a variable occurs.

Amount Frequency (Number of People Who Hold

Deposits in Each Range)

Under $3,000 499

$3,0002$5,999 530

$6,0002$8,999 562

$9,0002$11,999 718

$12,000 or more 811

3,120

Amount Percent (Percentage of People Who Hold

Amount Deposits in Each Range)

Under $3,000 16%

$3,0002$5,999 17%

$6,0002$8,999 18%

$9,0002$11,999 23%

$12,000 or more 26%

100%

EXHIBIT 17.1

Frequency Distribution of Deposits

EXHIBIT 17.2

Percentage Distribution of Deposits

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CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 411

412

SURVEY

THIS!

Probability is the long-run relative frequency with which an event will occur. Inferential sta- tistics uses the concept of a probability distribution, which is conceptually the same as a percentage distribution except that the data are converted into probabilities. Exhibit 17.3 shows the prob- ability distribution of the savings and loan deposits. We know that the probability of a respondent falling into the top category of $12,000 or more is the highest, 0.26.

Proportions When a frequency distribution portrays only a single characteristic in terms of a percentage of the total, it defines the proportion of occurrence. A proportion, such as the proportion of CPAs at an accounting firm, indicates the percentage of population elements that successfully meet some standard concerning the particular characteristic. A proportion may be expressed as a percentage (25 percent), a fraction (1/4), or a decimal value (0.25).

probability

The long-run relative frequency with which an event will occur.

proportion

The percentage of elements that meet some criterion.

Amount Probability

Under $3,000 0.16

$3,0002$5,999 0.17

$6,0002$8,999 0.18

$9,0002$11,999 0.23

$12,000 or more 0.26

1.00

This chapter covers basic statistical issues, with a focus on determining sample size and level of precision. For example, how many people do we have to survey so we know our sample proportions are within 2 percent of the population proportions? Or what if we want to ensure our answers are within 0.50 point of the population’s mean on a seven-point scale? Similarly, how do we determine how precise our measures are after we have collected our data? After reading this chapter, you should be able to address these questions.

Consider the question on our survey that asks if the respondent is employed?

1. What percentage of respondents do you think will answer “yes” to this question? 2. Based on your estimate, how many respondents would you need to be 95 percent confident your responses are

within 65 percent of the population proportion? 3. Look at the data collected for your class. At the 95 percent confidence level, how precise is the measure regarding

employment status? Consider the question that asks respondents to indicate how “interesting” or “boring” they feel their life is.

4. Review the “rule of thumb” provided in the chapter regarding estimating the value of the standard deviation of a scale. Using this rule for the above scale, how many respondents would you need to be 95 percent confident your responses are within 60.50 points of the population proportion?

5. What if you want to be 99 percent confident? What would be the required sample size? 6. Look at the data collected for your class. At the 95 percent confidence level, how precise is this measure?

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EXHIBIT 17.3

Probability Distribution of Deposits

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413

RESEARCH SNAPSHOT

Measures of Central Tendency On a typical day, a sales manager counts the number of sales calls each sales representative makes. She may want to inspect the data to find the average, center, or middle area, of the frequency dis- tribution. Central tendency can be measured in three ways—the mean, median, or mode—each of which has a different meaning. The Research Snapshot “The Well-Chosen Average” illustrates how these measures may differ.

The Mean We all have been exposed to the average known as the mean. The mean is simply the arithmetic average, and it is perhaps the most common measure of central tendency. More likely than not, you already know how to calculate a mean. However, knowing how to distinguish among the symbols o, m, and X is helpful to understand statistics.

To express the mean mathematically, we use the summation symbol, the capital Greek letter sigma (o). A typical use might look like this:

On i 5 1

X i

which is a shorthand way to write the sum

X 1

1 X 2

1 X 3

1 X 4

1 X 5

1 Á 1 X n

Below the o is the initial value of an index, usually, i, j, or k, and above it is the final value, in this case n, the number of observations. The shorthand expression says to replace i in the formula with

When you read an announcement by a corporate executive or a business proprietor that the average pay of the people who work in his or her establishment is so much, the figure may mean something or it may not. If the average is a median, you can learn something significant from it: half of the employees make more than that; half make less. But if it is a mean (and believe me, it may be, if its nature is unspecified), you may be getting nothing more revealing than the average of one $450,000 income—the proprietor’s—and the salaries of a crew of lower wage workers. “Average annual pay of $57,000” may conceal both the $20,000 salaries and the owner’s profits taken in the form of a whopping salary.

Let’s take a longer look at this scenario. This table shows how many people get how much. The boss might like to express the situation as “The average wage is $57,000” using that deceptive mean. The mode, however, is more revealing:

the most common rate of pay in this business is $20,000 a year. As usual, the median tells more about the situation than any other single figure. Half of the people get more than $30,000 and half get less.

Imagine what would happen to your hometown’s aver- age income if Ross Perot and Bill Gates moved into town! Or, perhaps your university had an NBA lottery pick or a first round NFL football player. Adding in their multimillion dollar first-year salaries would certainly raise the “mean starting salary” for stu- dents. However, the median and mode would likely not change at all with these “outliers” included.

Do politicians use statistics to lie or do the statistics lie? Politi- cians sometimes try to play one class of people against another in trying to get elected. One political claim is that the “rich do not pay taxes” or the “rich do not pay their fair share of taxes.” If you are curious about this, some facts are available at http:// www.taxfoundation.org or http://www.irs.gov/pub/irs-soi/ disindin.pdf. Do the top 1 percent of wage earners pay taxes? Do the top 5 percent of wage earners pay taxes?

Sources: Huff, Darrell and Irving Geis, How to Lie with Statistics (New York: W. W. Norton, 1954), 33; Jackson, Brooks and Kathleen H. Jamieson, “Finding Fact in Political Debate,” American Behavioral Scientist 48 (October 1, 2004), 233–247.

The Well-Chosen Average

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Number of People Title Salary

1 Proprietor $450,000 1 President 150,000 2 Vice presidents 100,000 1 Controller 57,000 ■ Mean (arithmetical average) 3 Directors 50,000 4 Managers 37,000 1 Supervisor 30,000 ■ Median (the one in the middle; 12 above, 12 below) 12 Workers 20,000 ■ Mode (occurs most frequently)

mean

A measure of central tendency; the arithmetic average.

the values from 1 to 8 and total the observations obtained. Without changing the basic formula, the initial and final index values may be replaced by other values to indicate different starting and stopping points.

Suppose our sales manager supervises the eight salespeople listed in Exhibit 17.4. To express the sum of the salespeople’s calls in o notation, we just number the salespeople (this number becomes the index number) and associate subscripted variables with their numbers of calls:

Index Salesperson Variable Number of Calls

1 5 Mike X 1

5 4

2 5 Patty X 2

5 3

3 5 Billie X 3

5 2

4 5 Bob X 4

5 5

5 5 John X 5

5 3

6 5 Frank X 6

5 3

7 5 Chuck X 7

5 1

8 5 Samantha X 8

5 5

Salesperson Number of Sales Calls

Mike 4

Patty 3

Billie 2

Bob 5

John 3

Frank 3

Chuck 1

Samantha 5

Total 26

EXHIBIT 17.4

Number of Sales Calls per Day by Salesperson

We then write an appropriate o formula and evaluate it:

O8 i 5 1

X i 5 X

1 1 X

2 1 X

3 1 X

4 1 X

5 1 X

6 1 X

7 1 X

5 4 1 3 1 2 1 5 1 3 1 3 1 1 1 5 5 26

This notation is the numerator in the formula for the arithmetic mean:

Mean 5 On i 5 1

X i

n 5

26 8

5 3.25

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414 • PART FIVE Sampling and Fieldwork

The sum On i 5 1

X i tells us to add all the Xs whose subscripts are between 1 and n inclusive, where

n equals the number of observations. The formula shows that the mean number of sales calls in this example is 3.25.

Researchers generally wish to know the population mean, m (lowercase Greek letter mu), which is calculated as follows:

m 5

On i 5 1

X i

N where

N 5 number of all observations in the population

Often we will not have the data to calculate the population mean, m, so we will calculate a sample mean, X (read “X bar”), with the following formula:

X 5 On i 5 1

X i

n where

n 5 number of observations made in the sample

In this introductory discussion of the summation sign (o), we have used very detailed notation that includes the subscript for the initial index value (i) and the final index value (n). However, from this point on, references to o will sometimes omit the subscript for the initial index value (i) and the final index value (n).

The Median The next measure of central tendency, the median, is the midpoint of the distribution, or the 50th percentile. In other words, the median is the value below which half the values in the sample fall, and above which half of the values fall. In the sales manager example, 3 is the median because half the observations are greater than 3 and half are less than 3.

The Mode In apparel, mode refers to the most popular fashion. In statistics the mode is the measure of central tendency that identifies the value that occurs most often. In our example of sales calls, Patty, John, and Frank each made three sales calls. The value 3 occurs most often, so 3 is the mode. The mode is determined by listing each possible value and noting the number of times each value occurs.

Measures of Dispersion The mean, median, and mode summarize the central tendency of frequency distributions. Accurate analysis of data also requires knowing the tendency of observations to depart from the central ten- dency. What is the spread across the observations? Thus, another way to summarize the data is to cal- culate the dispersion of the data, or how the observations vary from the mean. Consider, for instance, the 12-month sales patterns of the two products shown in Exhibit 17.5. Both have a mean monthly sales volume of 200 units, as well as a median and mode of 200, but the dispersion of observations for product B is much greater than that for product A. There are several measures of dispersion.

The Range The simplest measure of dispersion is the range. It is the distance between the smallest and the largest values of a frequency distribution. In Exhibit 17.5, the range for product A is between 196 units and 202 units (6 units), whereas for product B the range is between 150 units and 261 units (111 units). The range does not take into account all the observations; it merely tells us about the extreme values of the distribution.

median

A measure of central tendency that is the midpoint; the value below which half the values in a distribution fall.

mode

A measure of central tendency; the value that occurs most often.

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 415

Just as people may be fat or skinny, distributions may be fat or skinny. While we do not expect all observations to be exactly like the mean, in a skinny distribution they will lie a short distance from the mean. Product A is an example; the observations are close together and reasonably close to the mean. In a fat distribution, such as the one for Product B, they will be spread out. Exhibit 17.6 illustrates this concept graphically with two frequency distributions on a seven-point scale that have identical modes, medians, and means but different degrees of dispersion.

The interquartile range is the range that encompasses the middle 50 percent of the observations—in other words, the range between the bottom quartile (lowest 25 percent) and the top quartile (highest 25 percent).

Why Use the Standard Deviation? Statisticians have derived several quantitative indexes to reflect a distribution’s spread, or variability. The standard deviation is perhaps the most valuable index of spread, or dispersion. Students often have difficulty understanding it. Learning about the standard deviation will be easier if we first look at several other measures of dispersion that may be used. Each of these has certain limitations that the standard deviation does not.

EXHIBIT 17.6

Low Dispersion versus High Dispersion 5

1 2 3 4 5 6 7

Fr eq

ue nc

Value on Variable

1 2 3 4 5 6 7

Fr eq

ue nc

Value on Variable

Low dispersion High dispersion

Units Product A Units Product B

January 196 150

February 198 160

March 199 175

April 200 181

May 200 192

June 200 200

July 200 200

August 201 202

September 201 213

October 201 224

November 202 240

December 202 261

Average 200 200

EXHIBIT 17.5

Sales Levels for Two Products with Identical Average Sales

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416 • PART FIVE Sampling and Fieldwork

First is the deviation. Deviation is a method of calculating how far any observation is from the mean. To calculate a deviation from the mean, use the following formula:

d i i

5 X i 2 X

For the value of 150 units for product B for the month of January, the deviation score is 250; that is, 150 2 200 5 250. If the deviation scores are large, we will have a fat distribution because the distribution exhibits a broad spread.

Next is the average deviation. We compute the average deviation by calculating the deviation score of each observation value (that is, its difference from the mean), summing these scores, and then dividing by the sample size (n):

Average deviation 5 O(Xi 2 X )

n While this measure of spread may seem initially interesting, it is never used. Positive deviation scores are canceled out by negative scores with this formula, leaving an average deviation value of zero no matter how wide the spread may be. Hence, the average deviation is a useless spread measure.

One might correct for the disadvantage of the average deviation by computing the absolute values of the deviations, termed mean absolute deviation. In other words, we ignore all the positive and negative signs and use only the absolute value of each deviation. The formula for the mean absolute deviation is

Mean absolute deviation 5 O ƒ Xi 2 X ƒ

n While this procedure eliminates the problem of always having a zero score for the deviation mea- sure, some technical mathematical problems make it less valuable than some other measures.

The mean squared deviation provides another method of eliminating the positive/negative sign problem. In this case, the deviation is squared, which eliminates the negative values. The mean squared deviation is calculated by the following formula:

Mean squared deviation 5 O(Xi 2 X )2

n This measure is quite useful for describing the sample variability.

VARIANCE However, we typically wish to make an inference about a population from a sample, and so the divisor n 2 1 is used rather than n in most pragmatic marketing research problems.3 This new measure of spread, called variance, has the following formula:

Variance 5 S2 5 O(Xi 2 X )2

n 2 1 Variance is a very good index of dispersion. The variance, S2, will equal zero if and only if each

and every observation in the distribution is the same as the mean. The variance will grow larger as the observations tend to differ increasingly from one another and from the mean.

STANDARD DEVIATION While the variance is frequently used in statistics, it has one major drawback. The variance reflects a unit of measurement that has been squared. For instance, if measures of sales in a territory are made in dollars, the mean number will be reflected in dollars, but the variance will be in squared dollars. Because of this, statisticians often take the square root of the variance. Using the square root of the variance for a distribution, called the standard deviation, eliminates the drawback of having the measure of dispersion in squared units rather than in the original measurement units. The formula for the standard deviation is

S 5 D o (Xi 2 X )2n 2 1

variance

A measure of variability or dispersion. Its square root is the standard deviation.

standard deviation

A quantitative index of a distribution’s spread, or variability; the square root of the variance for a distribution.

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 417

Exhibit 17.7 illustrates that the calculation of a standard deviation requires the researcher to first cal- culate the sample mean. In the example with eight salespeople’s sales calls (Exhibit 17.4), we calculated the sample mean as 3.25. Exhibit 17.7 illustrates how to calculate the standard deviation for these data.

At this point we can return to thinking about the original purpose for measures of dispersion. We want to summarize the data from survey research and other forms of business research. Indexes of central tendency, such as the mean, help us interpret the data. In addition, we wish to calculate a mea- sure of variability that will give us a quantitative index of the dispersion of the distribution. We have looked at several measures of dispersion to arrive at two very adequate means of measuring dispersion: the variance and the standard deviation. The formula given is for the sample standard deviation, S.

The formula for the population standard deviation, s, which is conceptually very similar, has not been given. Nevertheless, you should understand that s measures the dispersion in the popu- lation and S measures the dispersion in the sample. These concepts are crucial to understand-

ing statistics. Remember, a business researcher must know the language of statistics to use it in a research project. If you do not understand the language at this point, you should review this material now.

The Normal Distribution One of the most common probability distributions in statistics is the normal distribution, commonly represented by the normal curve. This mathematical and theoretical distribution describes the expected distribution of sample means and many other chance occurrences. The normal curve is bell shaped, and almost all (99 percent) of its values are within 63 standard deviations from its mean. An example of a normal curve, the distribution of IQ scores, appears in

normal distribution

A symmetrical, bell-shaped distribution that describes

the expected probability distribution of many chance

occurrences.

X (X 2 X ) (X 2 X )2

4 (4 2 3.25) 5 .75 .5625

3 (3 2 3.25) 5 2.25 .0625

2 (2 2 3.25) 5 21.25 1.5625

5 (5 2 3.25) 5 1.75 3.0625

3 (3 2 3.25) 5 2.25 .0625

1 (1 2 3.25) 5 22.25 5.0625

5 (5 2 3.25) 5 1.75 3.0625

oa 0 13.5000

n 5 8 X 5 3.25

S 5 D o (X 2 X)2n 2 1 5 A 13.58 2 1 5 A 13.57 5 21.9286 5 1.3887 aThe summation of this column is not used in the calculation of the standard deviation.

EXHIBIT 17.7

Calculating a Standard Deviation: Number of Sales Calls per Day for Eight Salespeople

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418 • PART FIVE Sampling and Fieldwork

standardized normal distribution

A purely theoretical probability distribution that reflects a specific normal curve for the standardized value, z.

Exhibit 17.8. In this example, 1 standard deviation for IQ equals 15. We can identify the proportion of the curve by measuring a score’s distance (in this case, standard deviation) from the mean (100).

The standardized normal distribution is a specific normal curve that has several characteristics:

1. It is symmetrical about its mean; the tails on both sides are equal. 2. The mode identifies the normal curve’s highest point, which is also the mean and median, and

the vertical line about which this normal curve is symmetrical. 3. The normal curve has an infinite number of cases (it is a continuous distribution), and the area

under the curve has a probability density equal to 1.0. 4. The standardized normal distribution has a mean of 0 and a standard deviation of 1.

Exhibit 17.9 illustrates these properties. Exhibit 17.10 is a summary version of the typical standardized normal table found at the end of most statistics textbooks. A more complex table of areas under the standardized normal distribution appears in Table A.2 in the appendix.

The standardized normal distribution is a purely theoretical probability distribution, but it is the most useful distribution in inferential statistics. Statisticians have spent a great deal of time and effort making it convenient for researchers to find the probability of any portion of the area under the standardized normal distribution. All we have to do is transform, or convert, the data from other observed normal distributions to the standardized normal curve. In other words, the

EXHIBIT 17.9

Standardized Normal Distribution

–3 –2 –1 0 1 2 3 Z

Pr (Z )

EXHIBIT 17.8

Normal Distribution: Distribution of Intelligence Quotient (IQ) Scores

2.14% 13.59% 34.13% 34.13% 13.59% 2.14% 55 70 85 100 115 130 145 IQ

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CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 419

standardized normal distribution is extremely valuable because we can translate, or transform, any normal variable, X, into the standardized value, Z. Exhibit 17.11 illustrates how either a skinny distribution or a fat distribution can be converted into the standardized normal distribution. This ability to transform normal variables has many pragmatic implications for the business researcher. The standardized normal table in the back of most statistics and research books allows us to evaluate the probability of the occurrence of many events without any difficulty.

Computing the standardized value, Z, of any measurement expressed in original units is simple: subtract the mean from the value to be transformed, and divide by the standard deviation

EXHIBIT 17.10

Standardized Normal Table: Area under Half of the Normal Curvea

Z Standard

Deviations from the Mean (Units)

Z Standard Deviations from the Mean (Tenths of Units)

.0 .1 .2 .3 .4 .5 .6 .7 .8 .9

0.0 .000 .040 .080 .118 .155 .192 .226 .258 .288 .315

1.0 .341 .364 .385 .403 .419 .433 .445 .455 .464 .471

2.0 .477 .482 .486 .489 .492 .494 .495 .496 .497 .498

3.0 .499 .499 .499 .499 .499 .499 .499 .499 .499 .499

aArea under the segment of the normal curve extending (in one direction) from the mean to the point indicated by each row2column combination. For example, about 68 percent of normally distributed events can be expected to fall within 1.0 standard deviation on either side of the mean (0.341 3 2). An interval of almost 2.0 standard deviations around the mean will include 95 percent of all cases.

EXHIBIT 17.11

Standardized Values Can Be Computed from Flat or Peaked Distributions Resulting in a Standardized Normal Curve

2223 21 0 1 2

Either, A flat distribution or, A peaked distribution, can be converted into a Standard normal distribution through standardization.

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420 • PART FIVE Sampling and Fieldwork

(all expressed in original units). The formula for this procedure and its verbal statement follow. In the formula, note that s, the population standard deviation, is used for calculation.4 Also note that we do not use an absolute value, but rather allow the Z value to be either negative (below the mean) or positive (above the mean).

Standardized value 5 Value to be transformed 2 Mean

Standard deviation

Z 5 X 2 m

s where

m 5 hypothesized or expected value of the mean

Suppose that in the past a toy manufacturer has experienced mean sales, m, of 9,000 units and a standard deviation, s, of 500 units during September. The production manager wishes to know whether wholesalers will demand between 7,500 and 9,625 units during September of the upcoming year. Because no tables are available showing the distribution for a mean of 9,000 and a standard deviation of 500, we must transform our distribution of toy sales, X, into the standardized form using our simple formula:

Z 5 X 2 m

s 5

7,500 2 9,000 500

5 23.00

Z 5 X 2 m

s 5

9,625 2 9,000 500

5 1.25

The 23.00 indicates the standardized Z for sales of 7,500, while the 1.25 is the Z score for 9,625. Using Exhibit 17.10 (or Table A.2 in the appendix), we find that

When Z 5 −3.00, the area under the curve (probability) equals 0.499.

When Z 5 1.25, the area under the curve (probability) equals 0.394.

Thus, the total area under the curve is 0.499 1 0.394 5 0.893. In other words, the probability (Pr) of obtaining sales in this range is equal to 0.893. This is illustrated in Exhibit 17.12 in the shaded area. The sales manager, therefore, knows there is a 0.893 probability that sales will be between 7,500 and 9,625. We can go a step further here by comparing the area under the curve to the total. Since the distribution is symmetrical, 0.500 of the distribution is on either side of the center line. For the 7,500 figure the area under our curve is 0.499, so the probability of sales being less than 7,500 is 0.001 (0.500 2 0.499). Similarly, the probability of sales being more than 9,625 is 0.106 (0.500 2 0.394).

EXHIBIT 17.12

Standardized Distribution Curve

�3 �2 �1

Shaded Area � 0.499 Shaded Area � 0.394

0 �

1 2 3 Z

Pr (Z )

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CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 421

At this point, it is appropriate to repeat that understanding statistics requires an understanding of the language that statisticians use. Each concept discussed so far is relatively simple, but a clear- cut command of this terminology is essential for understanding what we will discuss later on.

Population Distribution, Sample Distribution, and Sampling Distribution Before we outline the technique of statistical inference, three additional types of distributions must be defined: population distribution, sample distribution, and sampling distribution. When conduct- ing a research project or survey, the researcher’s purpose is typically not to describe only the sample of respondents, but to make an inference about the population. As defined previously, a population, or universe, is the total set, or collection, of potential units for observation. The sample is a smaller subset of this population.

A frequency distribution of the population elements is called a population distribution. The mean and standard deviation of the population distribution are represented by the Greek letters m and s. A frequency distribution of a sample is called a sample distribution. The sample mean is designated X , and the sample standard deviation is designated S.

The concepts of population distribution and sample distribution are relatively simple. How- ever, we must now introduce another distribution, which is the crux of understanding statistics: the sampling distribution of the sample mean. The sampling distribution is a theoretical probability distribution that in actual practice would never be calculated. Hence, practical, business-oriented students have difficulty understanding why the notion of the sampling distribution is important. Statisticians, with their mathematical curiosity, have asked themselves, “What would happen if we were to draw a large number of samples (say, 50,000), each having n elements, from a specified population?” Assuming that the samples were randomly selected, the sample means, X

S , could be

arranged in a frequency distribution. Because different people or sample units would be selected in the different samples, the sample means would not be exactly equal. The shape of the sampling distribution is of considerable importance to statisticians. If the sample size is sufficiently large and if the samples are randomly drawn, we know from the central-limit theorem (discussed below) that the sampling distribution of the mean will be approximately normally distributed.

A formal definition of the sampling distribution is as follows:

A sampling distribution is a theoretical probability distribution that shows the functional relation between the possible values of some summary characteristic of n cases drawn at random and the probability (density) associated with each value over all possible samples of size n from a particular population.5

The sampling distribution’s mean is called the expected value of the statistic. The expected value of the mean of the sampling distribution is equal to m. The standard deviation of a sampling distribution of X is called standard error of the mean (S

X ) and is approximately equal to

S X

5 s1n

To review, for us to make an inference about a population from a sample, we must know about three important distributions: the population distribution, the sample distribution, and the sam- pling distribution. They have the following characteristics:

Mean Standard Deviation

Population distribution m s

Sample distribution X S

Sampling distribution mx 5 m SX

standard error of the mean

The standard deviation of the sampling distribution.

sampling distribution

A theoretical probability distribution of sample means

for all possible samples of a certain size drawn from a

particular population.

population distribution

A frequency distribution of the elements of a population.

sample distribution

A frequency distribution of a sample.

‘‘Order is heaven’s law.’’ —ALEXANDER POPE

422 • PART FIVE Sampling and Fieldwork

We now have much of the information we need to understand the concept of statistical infer- ence. To clarify why the sampling distribution has the characteristic just described, we will elabo- rate on two concepts: the standard error of the mean and the central-limit theorem. You may be wondering why the standard error of the mean, S

X , is defined as S

X 5 s/1n. The reason is based

on the notion that the variance or dispersion within the sampling distribution of the mean will be less if we have a larger sample size for independent samples. It should make intuitive sense that a larger sample size allows the researcher to be more confident that the sample mean is closer to the population mean. In actual practice, the standard error of the mean is estimated using the sample’s standard deviation. Thus, S

X is estimated using S/1n.

Exhibit 17.13 shows the relationship among a population distribution, the sample distribution, and three sampling distributions for varying sample sizes. In part (a) the population distribution is not a normal distribution. In part (b) the first sample distribution resembles the distribution of the population; however, there may be other distributions as shown in the second and third sample distributions. In part (c) each sampling distribution is normally distributed and has the same mean. However, as sample size increases, the spread of the sample means around m decreases. Thus, with a larger sample size we will have a narrower sampling distribution.

EXHIBIT 17.13

Fundamental Types of Distributions

(a) The Population

Distribution

Provides Data for

(b) Possible Sample

Distributions

Provide Data for

the Sample Means

X XX 1 –

X 2 – X n

– X

Samples of size n, e.g., 2,500

Samples of size n, e.g., 500

Samples of size n, e.g., 100

–x X –

Mean of the population Standard deviation of the

population X Values of items in the population

– X Mean of a sample distribution S Standard deviation of a sample distribution X Values of items in a sample

–x

–

Mean of the sampling distribution of means Standard deviation of the sampling distribution of means Values of all possible sample means

Source: Adapted from Sanders, D. H., A. F. Murphy, and R. J. Eng, Statistics: A Fresh Approach (New York: McGraw-Hill, 1980), 123.

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 423

Central-Limit Theorem Finding that the means of random samples of a sufficiently large size will be approximately normal in form and that the mean of the sampling distribution will approach the population mean is very useful. Mathematically, this is the assertion of the central-limit theorem, which states that as the sample size, n, increases, the distribution of the mean, X, of a random sample taken from practically any population approaches a normal distribution (with a mean m and a standard deviation s/1n).6 The central-limit theorem works regardless of the shape of the original population distribution (see Exhibit 17.14).

A simple example will demonstrate the central-limit theorem. Assume that a quality con- trol specialist is examining the number of defects in the products produced by assembly line workers. Assume further that the population the researcher is investigating consists of six dif- ferent workers in the same plant. Thus, in this example, the population consists of only six individuals. Exhibit 17.15 shows the population distribution of defects in a week. Donna,

EXHIBIT 17.14

Distribution of Sample Means for Samples of Various Sizes and Population Distributions

Population Population Population Population

Values of X –

Values of X Values of X Values of X Values of X

Values of X –

Sampling distribution of X

– Sampling distribution of X

–

Sampling distribution of X

– Sampling distribution of X

–

Sampling distribution of X

– Sampling distribution of X

–

n � 2 n � 2 n � 2 n � 2

n � 5 n � 5n � 5

n � 5

n � 30 n � 30

n � 30

Source: Kurnow, Ernest, Gerald J. Glasser, and Frederick R. Ottman, Statistics for Business Decisions (Homewood, IL: Richard D. Irwin, 1959), 182–183.

central-limit theorem

The theory that, as sample size increases, the distribution

of sample means of size n, randomly selected, approaches

a normal distribution.

424 • PART FIVE Sampling and Fieldwork

a dedicated and experienced worker, only has one defect in the entire week’s production. On the other hand, Eddie, a sloppy worker with little regard for quality, has six defects a week. The average number of defects each week is 3.5, so the population mean, m, equals 3.5 (see Exhibit 17.16.

Now assume that we do not know everything about the population, and we wish to take a sample with two observations, to be drawn randomly from the population of the six individuals. How many possible samples are there? The answer is 15, as follows:

1, 2 1, 3 2, 3 1, 4 2, 4 3, 4 1, 5 2, 5 3, 5 4, 5 1, 6 2, 6 3, 6 4, 6 5, 6

Exhibit 17.17 lists the sample mean for each of the possible 15 samples and the frequency dis- tribution of these sample means with their appropriate probabilities. These sample means comprise a sampling distribution of the mean, and the distribution is approximately normal. If we increased the sample size to three, four, or more, the distribution of sample means would more closely approx- imate a normal distribution. While this simple example is not a proof of the central-limit theorem, it should give you a better understanding of the nature of the sampling distribution of the mean.

This theoretical knowledge about distributions can be used to solve two practical business research problems: estimating parameters and determining sample size.

EXHIBIT 17.15

Population Distribution: Hypothetical Product Defects

Employee Defects

Donna 1

Heidi 2

Jason 3

Jennifer 4

Mark 5

Eddie 6

EXHIBIT 17.16

Calculation of Population Mean

o 21

Calculations: m 5 o X n

5 21 6

5 3.5 5 m X

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CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 425

EXHIBIT 17.17

Arithmetic Means of Samples and Frequency Distribution of Sample Means

Sample Means

Sample oX X Probability

1, 2 3.00 1.50 1/15

1, 3 4.00 2.00 1/15

1, 4 5.00 2.50 1/15

1, 5 6.00 3.00 1/15

1, 6 7.00 3.50 1/15

2, 3 5.00 2.50 1/15

2, 4 6.00 3.00 1/15

2, 5 7.00 3.50 1/15

2, 6 8.00 4.00 1/15

3, 4 7.00 3.50 1/15

3, 5 8.00 4.00 1/15

3, 6 9.00 4.50 1/15

4, 5 9.00 4.50 1/15

4, 6 10.00 5.00 1/15

5, 6 11.00 5.50 1/15

Frequency Distribution

Sample Mean Frequency Probability

1.50 1 1/15

2.00 1 1/15

2.50 2 2/15

3.00 2 2/15

3.50 3 3/15

4.00 2 2/15

4.50 2 2/15

5.00 1 1/15

5.50 1 1/15

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426 • PART FIVE Sampling and Fieldwork

Estimation of Parameters A catalog retailer, such as dELiA*s, may rely on sampling and statistical estimation to prepare for Christmas orders. The company can expect that 28 days after mailing a catalog, it will have received X percent of the orders it will get. With this information, the company can tell within 5 percent how many pairs of Harlow Low-Rise Super Skinny Jeans they will sell by Christmas. Making a proper inference about population parameters is highly practical for a business that must have the inventory appropriate for a particular selling season.

Suppose you are a product manager for ConAgra Foods and you recently conducted a taste test to measure intention to buy a reformulated Swiss Miss Lite Cocoa Mix. The results of the research indicate that when the product was placed in eight hundred homes and a callback was made two weeks later, 80 percent of the respondents said they would buy it: 76 percent of those who had not previously used low-calorie cocoa and 84 percent of those who had. How can you be sure there were no statistical errors in this estimate? How confident can you be that these figures accurately reflect the true attitudes?

Students often wonder whether statistics are really used in the business world. The answer is yes. The two situations just described provide examples of the need for statistical estimation of parameters and the value of statistical techniques as managerial tools.

Point Estimates Our goal in using statistics is to make an estimate about population parameters. A population mean, m, and standard deviation, s, are constants, but in most instances of business research, they are unknown. To estimate population values, we are required to sample. As we have discussed, X and S are random variables that will vary from sample to sample with a certain probability (sampling) distribution.

Our previous example of statistical inference was somewhat unrealistic because the population had only six individuals. Consider the more realistic example of a prospective racquetball entrepre- neur who wishes to estimate the average number of days players participate in this sport each week. When statistical inference is needed, the population mean, m, is a constant but unknown parameter. To estimate the average number of playing days, we could take a sample of three hundred racquet- ball players throughout the area where our entrepreneur is thinking of building club facilities. If the sample mean, X , equals 2.6 days per week, we might use this figure as a point estimate. This single value, 2.6, would be the best estimate of the population mean. However, we would be extremely lucky if the sample estimate were exactly the same as the population value. A less risky alternative would be to calculate a confidence interval.

Confidence Intervals If we specify a range of numbers, or interval, within which the population mean should lie, we can be more confident that our inference is correct. A confidence interval estimate is based on the knowledge that m 5 X 6 a small sampling error. After calculating an interval estimate, we can determine how probable it is that the population mean will fall within this range of statistical values. In the racquetball project, the researcher, after setting up a confidence interval, would be able to make a statement such as, “With 95 percent confidence, I think that the average number of days played per week is between 2.3 and 2.9.” This information can be used to estimate market demand because the researcher has a certain confidence that the interval contains the value of the true population mean.

The crux of the problem for a researcher is to determine how much random sampling error to tolerate. In other words, what should the confidence interval be? How much of a gamble should be taken that m will be included in the range? Do we need to be 80 percent, 90 percent, 95 percent, or 99 percent sure? The confidence level is a percentage or decimal that indicates the long-run probability that the results will be correct. Traditionally, researchers have used the 95 percent con- fidence level. While there is nothing magical about the 95 percent confidence level, it is useful to select this confidence level in our examples.

point estimate

An estimate of the population mean in the form of a single value, usually the sample mean.

confidence interval estimate

A specified range of numbers within which a population mean is expected to lie; an estimate of the population mean based on the knowledge that it will be equal to the sample mean plus or minus a small sampling error.

confidence level

A percentage or decimal value that tells how confident a researcher can be about being correct; it states the long-run percentage of confidence intervals that will include the true population mean.

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 427

428

As mentioned, the point estimate gives no information about the possible magnitude of ran- dom sampling error. The confidence interval gives the estimated value of the population parameter, plus or minus an estimate of the error. We can express the idea of the confidence interval as follows:

m 5 X 6 a small sampling error

More formally, assuming that the researchers select a large sample (more than 30 observations), the small sampling error is given by

Small sampling error 5 Z c.l.

S X

where

Z c.l.

5 value of Z, or standardized normal variable, at a specified confidence level (c.l.)

S X 5 standard error of the mean

The precision of our estimate is indicated by the value of Z c.l.

S X . It is useful to define the range of

possible error, E, as follows:

E 5 Z c.l.

S X

Thus,

m 5 X 6 E

where

X 5 sample mean

E 5 range of sampling error

m 5 X 6 Z c.l.

S X

We are often inundated with information derived from polls. Polls are built to gauge people’s opinions about topics impor- tant to their current well-being, or to reflect opinions about their society. Sophisticated sampling techniques are used to ensure that the polls reflect the populations of interest— oftentimes these polls are conducted at national levels, and reflect the national sentiment of a country. But what about the world? Is it possible that an organization might attempt to reflect the sentiments of people across the globe? Not surpris- ingly, the answer is yes. And the company that does this is one of the acknowledged leaders in sampling and sampling meth- odology—the Gallup organization. Their “sample of the world” program, called WorldView, generates a comprehensive snap- shot of the opinions of people from across the globe.

Gallup has, for some time, collected worldwide data from over 150 counties on such general topics as government and business, education, health care, and well-being. Their survey methodology is designed to provide a representative sample of 98 percent of the world’s adult population, with samples generally consisting of 1,000 individuals across both rural and urban geographies within a country. Using a standard set of general questions, Gallup trans- lates these questions into the major languages within a particular

country, and conducts telephone and face-to-face interviews within carefully developed sampling frames. In addition to the general questions, Gallup also asks questions that may be more specifically tied to local or regional conditions.

While it may seem that a sample developed around the world’s population may be too generic to provide much benefit, in fact the Gallup WorldView program provides extremely valu- able information on poorer and more rural populations, which often are ignored for more developed countries or popula- tions. To accurately reflect the world’s sentiments and opinions, you must have an accurate sample of the world’s population. Gallup’s approach, using a sophisticated sampling methodol- ogy and comprehensive interview training techniques, provides insights to those global opinions.

Source: www.gallup.com, accessed August 15, 2011.

Sampling the World

RESEARCH SNAPSHOT

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The confidence interval 6E is always stated as one-half (thus the plus or minus) of the total con- fidence interval.

The following step-by-step procedure can be used to calculate confidence intervals:

1. Calculate X from the sample. 2. Assuming s is unknown, estimate the population standard deviation by finding S, the sample

standard deviation. 3. Estimate the standard error of the mean, using the following formula:

S X

5 S1n

4. Determine the Z-value associated with the desired confidence level. The confidence level should be divided by 2 to determine what percentage of the area under the curve to include on each side of the mean.

5. Calculate the confidence interval.

The following shows how a confidence interval can be calculated. Suppose you are a finan- cial planner and are interested in knowing how long investors tend to own individual stocks. In a survey of 100 investors, you find that the mean length of time a stock is held (X ) is 37.5 months, with a standard deviation (S) of 12.0 months. Even though 37.5 months is the “expected value” and the best guess for the true mean in the population (m), the likelihood is that the mean is not exactly 37.5. Thus, a confidence interval around the sample mean computed using the steps just given will be useful:

1. X 5 37.5 months 2. S 5 12.0 months

3. S X 5

12.02100 5 1.2 4. Suppose you wish to be 95 percent confident—that is, assured that 95 times out of 100, the

estimates from your sample will include the population parameter. Including 95 percent of the area requires that 47.5 percent (one-half of 95 percent) of the distribution on each side be included. From the Z-table (Table A.2 in the appendix), you find that 0.475 corresponds to the Z-value 1.96.

5. Substitute the values for Z c.l.

and S X into the confidence interval formula:

m 5 37.5 6 (1.96)(1.2)

5 37.5 6 2.352

You can thus expect that m is contained in the range from 35.148 to 39.852 months. Intervals constructed in this manner will contain the true value of m 95 percent of the time.

Step 3 can be eliminated by entering S and n directly in the confidence interval formula:

m 5 X 6 Z c.l.

S1n Remember that S/1n represents the standard error of the mean. Its use is based on the central- limit theorem.

If you wanted to increase the probability that the population mean will lie within the con- fidence interval, you could use the 99 percent confidence level, with a Z-value of 2.57. You may want to calculate the 99 percent confidence interval for the preceding example; you can expect that m will be in the range between 34.416 and 40.584 months. It should make intuitive sense that if we are 99 percent confident the spread will be larger than if we are only 95 percent confident. If we want to be more confident, we need a broader range.

We have now examined the basic concepts of inferential statistics. You should understand that sample statistics such as the sample mean, X, can provide good estimates of population parameters such as m. You should also realize that there is a certain probability of being in error when you

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 429

estimate a population parameter from sample statistics. In other words, there will be a random sampling error, which is the difference between the results of a survey of a sample and the results of surveying the entire population. If you have a firm understanding of these basic terms and ideas, which are the essence of statistics, the remaining statistics concepts will be relatively simple for you. Several ramifications of the simple ideas presented so far will permit you to make better decisions about populations based on surveys or experiments.

Sample Size Random Error and Sample Size When asked to evaluate a business research project, most people, even those with little research training, begin by asking, “How big was the sample?” Intuitively we know that the larger the sample, the more accurate the research. This is in fact a statistical truth; random sampling error varies with samples of different sizes. In statistical terms, increasing the sample size decreases the width of the confidence interval at a given confidence level. Obviously if we collect information from every member of the population, we know the population parameters, so there would be no interval. When the standard deviation of the population is unknown, a confidence interval is calculated using the following formula:

Confidence interval 5 X 6 Z S1n

Observe that the equation for the plus or minus error factor in the confidence interval includes n, the sample size:

E 5 Z S1n

If n increases, E is reduced. Exhibit 17.18 illustrates that the confidence interval (or magnitude of error) decreases as the sample size, n, increases.

We already noted that it is not necessary to take a census of all elements of the population to conduct an accurate study. The laws of probability give investigators sufficient confidence regard- ing the accuracy of data collected from a sample. Knowledge of the characteristics of the sampling distribution helps researchers make reasonably precise estimates.

EXHIBIT 17.18

Relationship between Sample Size and Error

Large

Small Small Large

Size of Sample (n)

Ra nd

om S

am pl

in g

Er ro

r or

Z • S

/ n

430 • PART FIVE Sampling and Fieldwork

431

RESEARCH SNAPSHOT

Increasing the sample size reduces the sampling error. However, those familiar with the law of diminishing returns in economics will easily grasp the concept that increases in sample size reduce sampling error at a decreasing rate. For example, doubling a sample of 1,000 will reduce random sampling error by 1 percentage point, but doubling the sample from 2,000 to 4,000 will reduce random sampling error by only another half percentage point. More technically, random sampling error is inversely proportional to the square root of n. (Exhibit 17.18 gives an approximation of the relationship between sample size and error.) Thus, the main issue becomes one of determining the optimal sample size. The Research Snapshot “Target and Walmart Shoppers Really Are Different” discusses sample size and shows that some samples are extremely large.

Factors in Determining Sample Size for Questions Involving Means Three factors are required to specify sample size: (1) the heterogeneity (i.e., variance) of the popu- lation; (2) the magnitude of acceptable error (i.e., 6 some amount); and (3) the confidence level (i.e., 90 percent, 95 percent, 99 percent).

The determination of sample size heavily depends on the variability within the sample. The vari- ance, or heterogeneity, of the population is the first necessary bit of information. In statistical terms, this refers to the standard deviation of the population. Only a small sample is required if the population is homogeneous. For example, predicting the average age of college students requires a smaller sample than predicting the average age of people who visit the zoo on a given Sunday afternoon. As hetero- geneity increases, so must sample size. Thus, to test the effectiveness of an employee training program, the sample must be large enough to cover the range of employee work experience (for example).

Scarborough Research conducts ongoing consumer research that combines a telephone interview on media behavior with a mail survey about shopping habits and life- style and a television diary for detailed data about television viewing. Scarborough recognizes the importance of sample size for minimizing errors. Its sample includes over 200,000 adults so that it can make estimates of the U.S. population.

An example is a recent comparison of consumers who shop exclusively at either Target or Walmart. When respondents were asked to identify the stores at which they had shopped dur- ing the preceding three months, the largest share (40 percent) named both Target and Walmart. However, 31 percent shopped at Walmart but not Target, and 12 percent shopped at Target but not Walmart. Scarborough compared the consumer behav- ior of the latter two groups.

Target shoppers who shunned Walmart were more likely to shop at more upscale stores, including Macy’s and Nord- strom. They also were more likely than the average shopper to visit many different stores. Walmart shoppers who stayed away from Target were more likely to shop at discounters such as Dollar General and Kmart, and they were more likely to be at least 50 years old. Target-only shoppers tended to be younger and were more likely to have a high household income.

Given a U.S. adult population of approximately 220 million, do you think the sample size was adequate to make these comparisons?

Source: Based on “In the Battle for Discount Shoppers, Target and Wal-Mart Find Brand Loyalty in Different Customer Groups,” Scarborough Research news release (September 19, 2005), http://www.scarborough.com/ press_releases/FINAL%20Retail%209.19.05.pdf, accessed August 15, 2011; “Methodology,” Scarborough Research, http://www.scarborough.com/ pdf/scarborough-methodology.pdf, accessed August 15, 2011; U.S. Census Bureau, Statistical Abstract of the United States (2006), table 11, p. 13.

Target and Walmart Shoppers Really Are Different

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The magnitude of error, or the confidence interval, is the second necessary bit of informa- tion. Defined in statistical terms as E, the magnitude of error indicates how precise the estimate must be. It indicates a certain precision level. From a managerial perspective, the importance of the decision in terms of profitability will influence the researcher’s specifications of the range of error. If, for example, favorable results from a test-market sample will result in the construction of a new plant and unfavorable results will dictate not marketing the product, the acceptable range of error probably will be small; the cost of an error would be too great to allow much room for random sampling errors. In other cases, the estimate need not be extremely precise. Allowing an error of 6$1,000 in total family income instead of E 5 650 may be acceptable in most market segmentation studies.

The third factor of concern is the confidence level. In our examples, as in most business research, we will typically use the 95 percent confidence level. This, however, is an arbitrary decision based on convention; there is nothing sacred about the 0.05 chance level (that is, the probability of 0.05 of the true population parameter being incorrectly estimated). Exhibit 17.19 summarizes the information required to determine sample size.

EXHIBIT 17.19

Statistical Information Needed to Determine Sample Size for Questions Involving Means

Variable Symbol Typical Source of Information

Standard deviation S Pilot study or rule of thumb

Magnitude of error E Managerial judgment or

calculation (Z S X )

Confidence level Z c.l.

Managerial judgment

Estimating Sample Size for Questions Involving Means Once the preceding concepts are understood, determining the actual size for a simple random sample is quite easy. The researcher must follow three steps:

1. Estimate the standard deviation of the population. 2. Make a judgment about the allowable magnitude of error. 3. Determine a confidence level.

The judgment about the allowable error and the confidence level are the manager’s decision to make. Thus, the only problem is estimating the standard deviation of the popula- tion. Ideally, similar studies conducted in the past will give a basis for judging the standard deviation. In practice, researchers who lack prior information may conduct a pilot study to estimate the population parameters so that another, larger sample of the appropriate sample size may be drawn. This procedure is called sequential sampling because researchers take an initial look at the pilot study results before deciding on a larger sample to provide more pre- cise information.

A rule of thumb for estimating the value of the standard deviation is to expect it to be about one-sixth of the range. If researchers conducting a study on television purchases expected the price paid to range from $100 to $700, a rule-of-thumb estimate for the standard deviation would be $100. This is also useful when the question is a scaled response on a questionnaire. For example, if we plan on using a 10-point purchase intention scale, we can use our rule to determine the esti- mate for the standard deviation (10/6 5 1.67).

For the moment, assume that the standard deviation has been estimated in some preliminary work. If our concern is to estimate the mean of a particular population, the formula for sample size is

n 5 aZS E b

432 • PART FIVE Sampling and Fieldwork

where

Z 5 standardized value that corresponds to the confidence level

S 5 sample standard deviation or estimate of the population standard deviation

E 5 acceptable magnitude of error, plus or minus error factor (range is one-half of the total confidence interval)7

Suppose a survey researcher studying annual expenditures on lipstick wishes to have a 95 percent confidence level (Z 5 1.96) and a range of error (E) of less than $2. If the estimate of the standard deviation is $29, the sample size can be calculated as follows:

n 5 aZS E b

5 a(1.96)(29) 2

b 2

5 a56.84 2 b

5 28.422 5 808

If a range of error (E) of $4 is acceptable, the necessary sample size will be reduced:

n 5 aZS E b

5 a(1.96)(29) 4

b 2

5 a56.84 4 b

5 14.212 5 202

Thus, doubling the range of acceptable error reduces sample size to approximately one-quarter of its original size. Stated conversely in a general sense, doubling sample size will reduce error by only approximately one-quarter.

The Influence of Population Size on Sample Size The Nielsen Company estimates television ratings. Throughout the years, it has been plagued with questions about how it is possible to rate 98 million or more television homes with such a small sample (approximately 5,000 households). The answer to that question is that in most cases the size of the population does not have an effect on the sample size. As we have indicated, the variance of the population has the largest effect on sample size. However, a finite correction factor may be needed to adjust a sample size that is more than 5 percent of a finite population. If the sample is large relative to the population, the foregoing procedures may overestimate sample size, and the researcher may need to adjust sample size. The finite correction factor isA (N 2 n)(N 2 1) where

N 5 population size and n 5 sample size.

Factors in Determining Sample Size for Proportions Researchers frequently are concerned with determining sample size for problems that involve estimating population proportions or percentages. When the question involves the estimation of a proportion, the researcher requires some knowledge of the logic for determining a confidence interval around a sample proportion estimation (p) of the population proportion (p). For a con- fidence interval to be constructed around the sample proportion (p), an estimate of the standard error of the proportion (S

p ) must be calculated and a confidence level specified.

The precision of the estimate is indicated by the value Z c.l.

S p . Thus, the plus-or-minus estimate

of the population proportion is

Confidence interval 5 p 6 Z c.l.

S p

If the researcher selects a 95 percent probability for the confidence interval, Z c.l.

will equal 1.96 (see Table A.2 in the appendix). The formula for S

p is

S p

5 A pqn or Sp 5 A p(1 2 p)n

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 433

where

S p 5 estimate of the standard error of the proportion

p 5 proportion of successes

q 5 1 2 p, or proportion of failures

Suppose that 20 percent of a sample of 1,200 television viewers recall seeing an advertisement. The proportion of successes (p) equals 0.2, and the proportion of failures (q) equals 0.8. We esti- mate the 95 percent confidence interval as follows:

Confidence Interval 5 p 6 Z c.l.

S p

5 0.2 6 1.96S p

5 0.2 6 1.96A p(1 2 p)n 5 0.2 6 1.96A 0.2(1 2 0.2)1,200 5 0.2 6 1.96A 0.161,200 5 0.2 6 1.96(0.0115) 5 0.2 6 0.022

Thus, the population proportion who see an advertisement is estimated to be included in the interval between 0.178 and 0.222, or roughly between 18 and 22 percent, with a 95 percent con- fidence coefficient.

To determine sample size for a proportion, the researcher must make a judgment about confi- dence level and the maximum allowance for random sampling error. Furthermore, the size of the proportion influences random sampling error, so an estimate of the expected proportion of suc- cesses must be made, based on intuition or prior information. The formula is

n 5 Z 2

c.l. pq

where

n 5 number of items in sample

Z 2 c.l.

5 square of the confidence level in standard error units

p 5 estimated proportion of successes

q 5 1 2 p, or estimated proportion of failures

E2 5 square of the maximum allowance for error between the true proportion and the sample proportion,or Z

c.l. S

p squared

Suppose a researcher believes that a simple random sample will show that 60 percent of the popula- tion (p) recognizes the name of an automobile dealership. The researcher wishes to estimate with 95 percent confidence (Z

c.l. 5 1.96) that the allowance for sampling error is not greater than 3.5

percentage points (E). Substituting these values into the formula gives

n 5 (1.96)2(0.6)(0.4)

0.0352

5 (3.8416)(0.24)

0.001225

5 0.992

0.001225 5 753

434 • PART FIVE Sampling and Fieldwork

Calculating Sample Size for Sample Proportions In practice, a number of tables have been constructed for determining sample size. Exhibit 17.20 illustrates a sample size table for problems that involve sample proportions (p).

The theoretical principles underlying calculation of sample sizes of proportions are similar to the concepts discussed in this chapter. Suppose we wish to take samples in two large cities,

Sample Size for a 95 Percent Confidence Level When Parameter in Population Is Assumed to Be over 70 Percent or under 30 Percent

Reliability

Size of Population 61% Point 62% Points 63% Points 65% Points

1,000 a a 473 244

2,000 a a 619 278

3,000 a 1,206 690 291

4,000 a 1,341 732 299

5,000 a 1,437 760 303

10,000 4,465 1,678 823 313

20,000 5,749 1,832 858 318

50,000 6,946 1,939 881 321

100,000 7,465 1,977 888 321

500,000 to ` 7,939 2,009 895 322

Sample Size for a 95 Percent Confidence Level When Parameter in Population Is Assumed to Be over 85 Percent or under 15 Percent

Reliability

Size of Population 61% Point 62% Points 63% Points 65% Points

1,000 a a 353 235

2,000 a 760 428 266

3,000 a 890 461 278

4,000 a 938 479 284

5,000 a 984 491 289

10,000 3,288 1,091 516 297

20,000 3,935 1,154 530 302

50,000 4,461 1,195 538 304

100,000 4,669 1,210 541 305

500,000 to ` 4,850 1,222 544 306

aIn these cases, more than 50 percent of the population is required in the sample. Since the normal approximation of the hypergeometric distribution is a poor approximation in such instances, no sample value is given. Source: Lin, Nan, Foundations of Social Research (New York: McGraw-Hill, 1976), 447. Copyright © 1976 by Nan Lin. Used with permission.

EXHIBIT 17.20

Selected Tables for Determining Sample Size When the Characteristic of Interest Is a Proportion

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 435

New York and Paris. We wish no more than 2 percentage points of error, and we will be satisfied with a 95 percent confidence level (see Exhibit 17.20). If we assume all other things are equal, then in the New York market, where 15 percent of the consumers favor our product and 85 percent prefer competitors’ brands, we need a sample of 1,222 to get results with only 2 percentage points of error. In the Paris market, however, where 30 percent of the consumers favor our brand and 70 percent prefer other brands (a less heterogeneous market), we need a sample size of 2,009 to get the same sample reliability.

Exhibit 17.21 shows a sampling error table typical of those that accompany research proposals or reports. Most studies will estimate more than one parameter. Thus, in a survey of 100 people in which 50 percent agree with one statement and 10 percent with another, the sampling error is expected to be 10 and 6 percentage points of error, respectively.

EXHIBIT 17.21

Allowance for Random Sampling Error (Plus and Minus Percentage Points) at 95 Percent Confidence Level

Sample Size

Response 2,500 1,500 1,000 500 250 100 50

10 (90) 1.2 1.5 2.0 3.0 4.0 6.0 8.0

20 (80) 1.6 2.0 2.5 4.0 5.0 8.0 11.0

30 (70) 1.8 2.5 3.0 4.0 6.0 9.0 13.0

40 (60) 2.0 2.5 3.0 4.0 6.0 10.0 14.0

0 (50) 2.0 2.5 3.0 4.0 6.0 10.0 14.0

Source: Lin, Nan, Foundations of Social Research (New York: McGraw-Hill, 1976). Reprinted by permission.

Determining Sample Size on the Basis of Judgment Just as sample units may be selected to suit the convenience or judgment of the researcher, sample size may also be determined on the basis of managerial judgments. Using a sample size similar to those used in previous studies provides the inexperienced researcher with a comparison with other researchers’ judgments.

Another judgmental factor that affects the determination of sample size is the selection of the appropriate item, question, or characteristic to be used for the sample size calculations. Several different characteristics affect most studies, and the desired degree of precision may vary for these items. The researcher must exercise some judgment to determine which item will be used. Often the item that will produce the largest sample size will be used to determine the ultimate sample size. However, the cost of data collection becomes a major consideration, and judgment must be exercised regarding the importance of such information.

Another consideration stems from most researchers’ need to analyze various subgroups within the sample. For example, suppose a researcher wants to look at employee attitudes, but is particu- larly interested in differences across genders and age groups. The analyst will want to make sure to sample an adequate number of men and women, as well as across the various age groups to ensure that subgroup comparisons are reliable. There is a judgmental rule of thumb for selecting minimum subgroup sample size: each subgroup to be separately analyzed should have a minimum of 100 units in each category of the major breakdowns. With this procedure, the total sample size is computed by totaling the sample sizes necessary for these subgroups.

436 • PART FIVE Sampling and Fieldwork

Determining Sample Size for Stratified and Other Probability Samples Stratified sampling involves drawing separate probability samples within the subgroups to make the sample more efficient. With a stratified sample, the sample variances are expected to differ by strata. This makes the determination of sample size more complex. Increased complexity may also characterize the determination of sample size for cluster sampling and other probability sampling methods. The formulas are beyond the scope of this book. Students interested in these advanced sampling techniques should investigate advanced sampling textbooks.

Determining Level of Precision after Data Collection Up to this point, we have discussed the process for determining how large of a sample we need to collect given the estimated variance among the responses and our desired level of precision and acceptable error. This is a very important consideration for researchers. However, after we have collected the data, we also want to determine our level of precision, given the size of the sample, the variance, and the confidence level. In this case, we can rewrite our equation for determining sample size:

n 5 aZS E b

Rather than solving for n, we now know n and instead want to solve for E, the magnitude of error. Our new equation would be:

E 2 5 (ZS)2

So, we could solve for E2, and then take the square root of this to determine our level of preci- sion. This is a useful approach to use after the fact to show our final level of precision. In our earlier example of sample size regarding lipstick expenditures, we found that if we wanted to be 95 percent confident (Z value of 1.96) that our estimate of expenditures was within $2.00 and we had a standard deviation of $29.00, we would need a sample size of 808. Using the same situation, let’s assume we had already collected the data, but were not certain of our level of precision. Our formula above would show:

(1.96 * 29)2/808 5 4

The square root of 4 is 2.

When completing a research project it is often a good idea to provide managers with the level of precision for key measures. This formula will allow you to do so.

A Reminder about Statistics Learning the terms and symbols defined in this chapter will provide you with the basics of the language of statisticians and researchers. As you learn more about the pragmatic use of statistics in marketing research, do not forget these concepts. Rules are important in learning a foreign lan- guage and when the rules are forgotten, being understood becomes very difficult. The same is true for the student who forgets the basics of the “foreign language” of statistics.

CHAPTER 17 Determination of Sample Size: A Review of Statistical Theory • 437

SUMMARY 1. Understand basic statistical terminology. Determination of sample size requires a knowl- edge of statistics. Statistics is the language of the researcher, and this chapter introduced its vocabu- lary. Descriptive statistics describe characteristics of a population or sample. Thus, calculating a mean and a standard deviation to “describe” or profile a sample is a commonly applied descriptive statistical approach. Inferential statistics investigate samples to draw conclusions about entire popu- lations. If a mean is computed and then compared to some preconceived standard, then inferential statistics are being implemented.

2. Interpret frequency distributions, proportions, and measures of central tendency and dis- persion. A frequency distribution shows how frequently each response or classification occurs. A simple tally count illustrates a frequency distribution. A proportion indicates the percentage of group members that have a particular characteristic. Three measures of central tendency are commonly used: the mean, or arithmetic average; the median, or halfway value; and the mode, or most frequently observed value. These three values may be either the same or they may differ, and care must be taken to understand distortions that may arise from using the wrong measure of central tendency. Measures of dispersion further describe a distribution. The range is the differ- ence between the largest and smallest values observed. The most useful measures of dispersion are the variance (the summation of each observation’s deviation from the mean, divided by one less than the number of observations) and standard deviation, which is the square root of the variance.

3. Distinguish among population, sample, and sampling distributions. The techniques of statistical inference are based on the relationship among the population distribution, the sample distribution, and the sampling distribution. The population distribution is a frequency distribution of the elements of a population. The sample distribution is a frequency distribution of a sample. A sampling distribution is a theoretical probability distribution of sample means for all possible samples of a certain size drawn from a particular population. The sampling distribution’s mean is the expected value of the mean, which equals the population’s mean. The standard deviation of the sampling distribution is the standard error of the mean, approximately equal to the standard deviation of the population, divided by the square root of the sample size.

4. Explain the central-limit theorem. The central-limit theorem states that as sample size increases, the distribution of sample means of size n, randomly selected, approaches a normal distri- bution. This theoretical knowledge can be used to estimate parameters and determine sample size.

5. Summarize the use of confidence interval estimates. Estimating a population mean with a single value gives a point estimate. The confidence interval estimate is a range of numbers within which the researcher is confident that the population mean will lie. The confidence level is a percentage that indicates the long-run probability that the confidence interval estimate will be correct. Many research problems involve the estimation of proportions. Statistical techniques may be used to determine a confidence interval around a sample proportion.

6. Discuss the major issues in specifying sample size. The statistical determination of sample size requires knowledge of (1) the variance of the population, (2) the magnitude of acceptable error, and (3) the confidence level. Several computational formulas are available for determining sample size. Furthermore, a number of easy-to-use tables have been compiled to help researchers calculate sample size. The main reason a large sample size is desirable is that sample size is related to random sampling error. A smaller sample makes a larger error in estimates more likely. Calculation of sample size for a sample proportion is not difficult. However, most researchers use tables that indicate predetermined sample sizes.

KEY TERMS AND CONCEPTS central-limit theorem, 424 confidence interval estimate, 427 confidence level, 427 descriptive statistics, 410 frequency distribution, 411 inferential statistics, 410 mean, 413 median, 415

mode, 415 normal distribution, 418 percentage distribution, 411 point estimate, 427 population distribution, 422 population parameters, 410 probability, 412 proportion, 412

sample distribution, 422 sample statistics, 410 sampling distribution, 422 standard deviation, 417 standard error of the mean, 422 standardized normal

distribution, 419 variance, 417

438 Part 5: Sampling and Fieldwork

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is the difference between descriptive and inferential

statistics? 2. Suppose the speed limits in 13 countries in miles per hour are as

follows:

Country Highway Miles per Hour

Italy 87

France 81

Hungary 75

Belgium 75

Portugal 75

Great Britain 70

Spain 62

Denmark 62

Netherlands 62

Greece 62

Japan 62

Norway 56

Turkey 56

What is the mean, median, and mode for these data? Feel free to use your computer (statistical software or spreadsheet) to get the answer.

3. Prepare a frequency distribution for the data in question 2. 4. Why is the standard deviation rather than the average deviation

typically used? 5. Calculate the standard deviation for the data in question 2. 6. Draw three distributions that have the same mean value but

different standard deviation values. Draw three distributions that have the same standard deviation value but different mean values.

7. A manufacturer of MP3 players surveyed one hundred retail stores in each of the firm’s sales regions. An analyst noticed that in the South Atlantic region the average retail price was $165 (mean) and the standard deviation was $30. However, in the Mid-Atlantic region the mean price was $170, with a standard deviation of $15. What do these statistics tell us about these two sales regions?

8. What is the sampling distribution? How does it differ from the sample distribution?

9. What would happen to the sampling distribution of the mean if we increased sample size from 5 to 25?

10. Suppose a fast-food restaurant wishes to estimate average sales vol- ume for a new menu item. The restaurant has analyzed the sales of the item at a similar outlet and observed the following results:

X 5 500 (mean daily sales) s 5 100 (standard deviation of sample) n 5 25 (sample size)

The restaurant manager wants to know into what range the mean daily sales should fall 95 percent of the time. Perform this calculation.

11. In our example of research on lipstick, where E 5 $2 and S 5 $29, what sample size would we require if we desired a 99 percent confidence level? What about if we keep the 95% confidence level, but decide that our acceptable error is $4?

12. Suppose you are planning to sample cat owners to determine the average number of cans of cat food they purchase monthly. The following standards have been set: a confidence level of 99 percent and an error of less than five units. Past research has indicated that the standard deviation should be six units. What is the required sample size?

13. In a survey of 500 people, 60 percent responded with agree- ment to an attitude question. Calculate a confidence interval at 95 percent to get an interval estimate for a proportion.

14. What is a standardized normal curve? 15. A researcher expects the population proportion of Cubs fans

in Chicago to be 80 percent. The researcher wishes to have an error of less than 5 percent and to be 95 percent confident of an estimate to be made from a mail survey. What sample size is required?

16. ETHICS Using the formula in this chapter, a researcher deter- mines that at the 95 percent confidence level, a sample of 2,500 is required to satisfy a client’s requirements. The researcher actually uses a sample of 1,200, however, because the client has specified a budget cap for the survey. What are the ethical con- siderations in this situation?

17. ’NET Go to http://www.dartmouth.edu/~chance/ to visit the Chance course. The Chance course is an innovative program to creatively teach introductory materials about probability and statistics. The Chance course is designed to enhance quantitative literacy. Numerous videos can be played online.

18. ’NET Go to http://www.researchinfo.com. Click on “Market Research Calculators.” and select the sample size calculator. How big of a sample is needed to make an inference about the U.S. population 65 percent? How large a sample is needed to make an inference about the population of Norway 65 percent? Remember, population statistics can be found in the CIA World Factbook online. Comment.

19. ’NET A random number generator and other statistical infor- mation can be found at http://www.random.org. Flip some virtual coins. Perform 20 flips with an Aurelian coin. Perform 20 flips with a Constatius coin. Perform frequency tables for each result. What conclusion might you draw? Would the result change if you flipped the coins 200 times or 2,000 times?

Chapter 17: Determination of Sample Size: A Review of Statistical Theory 439

RESEARCH ACTIVITY 1. ’NET Use an online library service to find basic business research

studies that report a “response rate” or number of respondents compared to number of contacts. You may wish to consult journals such as the Journal of Business Research, the Journal of

Marketing, or the Journal of Management. Find at least 25 such studies. What is the average response rate across all of these stud- ies? Do there appear to be any trends or factors that are associated with lower response rates? Write a brief report on your findings.

When salespeople, construction supervisors, manag- ers, and other employees are away from the work- place, many of them carry mobile devices such as

laptop computers and PDAs, often containing valuable, private data related to their jobs. Pointsec (www.checkpoint.com/products/ datasecurity/mobile/index.html) provides security systems to pro- tect such data. To bring home the vulnerability of mobile devices, Pointsec decided to share information about the number of such devices left behind in taxis.8

The research involved conducting a survey of taxi drivers. Staff members at Pointsec’s public relations firm called major taxi compa- nies in nine cities in Australia, Denmark, Finland, France, Germany, Norway, Sweden, Great Britain, and the United States. Each of the cooperating companies put these interviewers in touch with about one hundred drivers. Drivers were asked how many devices of each type—cell phones, PDAs, computers, and so on—had been left in their cab over the preceding six months. From these numbers, they came up with the rate of items left behind. Multiplying by the size

of taxi fleets in each city, the researchers came up with city-by-city numbers: 3.42 cell phones per cab yielded 85,619 cell phones left behind in Chicago, for example. In London, the researchers con- cluded 63,135 cell phones were left in cabs, a startling increase of 71 percent compared to four years earlier.

Questions 1. Discuss why the sampling method and sample size make these

results questionable, even though the numbers were reported as if they were precise.

2. The simple survey method described in the case may have been sufficient as a way to draw attention to the issue of data secu- rity. However, if the company were using data on lost mobile devices to predict demand for a product, accuracy might be more significant. Imagine that you have been asked to collect data on mobile devices left in cabs, and you wish to be able to report results with a 95 percent confidence level. How can you improve the sample design and select an appropriate sample size?

Pointsec Mobile Technologies CASE 17.1

440 Part 5: Sampling and Fieldwork

441

After studying this chapter, you should be able to

1. Describe the role and job requirements of fieldworkers

2. Summarize the skills to cover when training inexperienced interviewers

3. List principles of good interviewing

4. Describe the activities involved in the management of fieldworkers

5. Discuss how supervisors can minimize errors in the field

C H A P T E R

F ieldwork is difficult to quantify, but it can best be described as collecting data “out there.” Whether it is in a mall or customer service location or even in remote towns and villages, fieldwork requires

a researcher to oftentimes directly interact with consumers and households to gather specialized or detailed data. In the past, fieldworkers used notebooks and clipboards to gather data, capturing information by hand as they interacted with the respondent. Once the information was collected, the field- worker often would return to the research organization and arduously code the handwritten notes into a database or sta- tistical package. Fortunately, technology has made this process significantly easier.

One example of a company that has specialized in face-to- face fieldwork software is Askia. Askia has developed a fully functioning software application that works with tablet PCs and PDAs for field researchers. Their system (askiaface) represents an important advantage to field research, since their interface provides seamless integration with telephone-assisted survey databases and an ability to directly download data into an analysis program. The askiaface system fieldworkers can have their survey applications updated on-the-fly, without having to return to their research base to update materials. Users can even use multimedia to present products or services, or provide

illustrations for the respondent. Askia has developed client rela- tionships with companies around the world, and has seen their number of customers triple each year.

Conducting field research is hard enough without having the proper quality control and information turnaround that is so critical these days. The ability to capture and integrate field notes and information through askiaface is one example of how technology has assisted fieldwork research. So, if you are challenged by your own fieldwork data needs, perhaps you too should “Ask Askia”!1

LEARNING OUTCOMES

Fieldwork

Chapter Vignette:

Software for Fieldwork? Ask Askia

te ve

C ob

ur n/

Sh ut

te rs

to ck

in-house interviewer

A fieldworker who is employed by the company conducting

the research.

Introduction Much of the work of a business researcher involves interacting with stakeholders within the organization. But when it is required, the researcher must conduct data collection with potential clients or customers outside of the firm. Similar to the term used for anthropologists or biolo- gists who must go into far-away countries or into local fields and forests, this is referred to as working “in the field,” or fieldwork. This chapter highlights this critical activity for business researchers.

The Nature of Fieldwork A fieldworker may be a personal interviewer administering a questionnaire door to door, a telephone interviewer calling from a central location, an observer counting pedestrians in a shopping mall, or another involved in the collection of data and the supervision of that pro- cess. The activities fieldworkers perform vary substantially. The supervision of data collection for a mail survey differs from that for an observation study as much as the factory production process for cereal differs from that for a pair of ski boots. Yet, just as quality control is basic to each production operation, the same basic issues arise in the various types of fieldwork. For ease of presentation, this chapter focuses on the interviewing process conducted by personal interviewers. However, many of the issues apply to all fieldworkers, no matter what their spe- cific settings.

Who Conducts the Fieldwork? The data-collecting stage is crucial, because the research project is no better than the data collected in the field. However, actual data collection process is rarely carried out by the person who designs the research. The research administrators must concentrate on carefully selecting fieldworkers and place trust in them to gather the data. When a second party is subcontracted, the job of the study designer at the parent firm is not only to hire a research supplier but also to build in supervisory controls over the field service and manage the process.

Much fieldwork is conducted by research suppliers that specialize in data collection. For example, a company may subcontract the fieldwork to a field interviewing service. As seen in the Research Snapshot “Interviewing for Horizon Research Services,” various field interview- ing services and full-service research agencies perform all manner of personal surveys including central location telephone interviewing, mall-intercept interviews, and other forms of fieldwork for a fee. These agencies typically employ field supervisors who train and monitor interviewers, edit completed questionnaires in the field, and telephone or recontact respondents to confirm that interviews have indeed been conducted.

Whether the research administrator hires an in-house interviewer or selects a field inter- viewing service, fieldworkers should ideally meet certain job requirements. Although the job requirements for different types of surveys vary, normally interviewers should be healthy, out- going, and of pleasing appearance—that is, well-groomed and tailored. People who enjoy talking with strangers usually make better interviewers. An essential part of the interviewing process is establishing rapport with the respondent. An outgoing nature may help interviewers ensure respondents’ full cooperation. Interviewer bias may occur if the fieldworker’s clothing or physical appearance is unattractive or unusual. One exception to this would be ethnographic research. In ethnographic research, the interviewer should dress to blend in with the group being studied. So, if holey jeans and a dirty T-shirt are the dress du jour, then the interviewer should dress likewise!

fieldworker

An individual who is responsible for gathering data

in the field.

field interviewing service

A research supplier that specializes in gathering data.

‘‘The knowledge of the world is only to be acquired in the world and not in the closet.’’—LORD CHESTERFIELD

442 • PART FIVE Sampling and Fieldwork

443

SURVEY

THIS!

Survey interviewers generally are paid hourly rates or per-interview fees. Often interviewers are part-time workers from a variety of backgrounds—homemakers, graduate students, schoolteachers, and others. Some research projects require special knowledge or skills, such as familiarity with the topic they are asking about. For example, in a survey investigating whether health education improves the likelihood that people who have suffered a stroke will quit smoking, the researchers used trained nurses to administer questionnaires that included each patient’s medical history.2 Taking an accurate medical history is a skill that requires more training than most interviewers would likely have.

In-House Training for Inexperienced Interviewers After personnel are recruited and selected, they must be trained.3 Suppose a woman who has just sent her youngest child off to first grade decides to seek a part-time job as a professional inter- viewer. The training she will receive after being hired may vary from virtually no training to an extensive, multiple-day program if she is selected by one of the larger research companies. Almost always, trainees will receive a briefing session on the particular project.

The core objective of training is to ensure that each respondent is provided with common information and the data collection instrument is administered in a uniform fashion by all field- workers. If the data are collected in a uniform manner from all respondents, the training session will have succeeded.

More extensive training programs are likely to cover the following topics:

■ How to make initial contact with the respondent and secure the interview ■ How to ask survey questions ■ How to probe ■ How to record responses ■ How to terminate the interview

Typically, recruits engage in role playing activities and record answers on a practice questionnaire during a simulated training interview.

Making Initial Contact and Securing the Interview Personal Interviews Personal interviewers may carry a letter of identification or an ID card to indicate that the study is a bona fide research project and not a sales pitch. Interviewers are trained to make appropriate

Take a look at the section of the questionnaire shown in this picture. Respondents answered these questions without the benefit of an interviewer. Do you think an interviewer could help respondents provide better answers to these questions than the self-administered survey? Consider the pros and cons of a personal interviewer or a telephone interviewer for this type of information. What would your recommendation be to a researcher conducting this type of interview? If you think an interviewer should be used, explain why and give an indication of the instructions the field- worker should receive. If you do not believe an interviewer would be helpful, explain how the interviewer may actually contribute to lower quality in responses.

briefing session

A training session to ensure that each interviewer is provided with common information.

Co ur

te sy

o f Q

ua ltr

ic s.

co m

444

opening remarks that will convince the respondent that his or her coop- eration is important, as in this example:

Good afternoon, my name is _____________, and I’m with [insert name of firm], an international business research company. Today, we are conduct- ing a survey concerning _____________. I would like to get a few of your ideas. It will take [insert accurate time estimate] minutes.

Telephone Interviews For the initial contact in a telephone interview, the introduction might be something like this:

Good evening, my name is ________________. I am not trying to sell anything. I’m calling from [insert name of firm] in Delavan, Illinois. We are seeking your opinions on some important matters and it will only take [insert accurate time estimate] minutes of your time.

Giving the interviewer’s name personalizes the call. The name of the research agency is used to imply that the caller is trustworthy. The respondent must be given an accurate estimate of the time it will take to participate in the interview. If someone is told that only three minutes will be required for participation, and the interview proceeds to five minutes or more, the respondent will tend to quit before com- pleting the interview. Providing an accurate estimate of the time not only helps gain cooperation, but it is also the ethically correct thing to do.

Along with the big-name national and international research firms like Yankelovich, Nielsen, and Gallup, many smaller research companies offer interviewing and other services to clients in their city or region. One example is Horizon Research Services (http://www.horizonresearch.com), located in Columbia, Missouri. Founded by Kathleen Anger, a psycholo- gist, Horizon has served many local organizations including Columbia’s banks and hospitals. The company conducts focus groups, telephone surveys, and other research projects.

One of the most significant challenges of a Horizon inter- viewer’s job is simply to keep the respondent from hanging up. In the first few seconds of the phone call, fieldworkers quickly reassure the person that the call is for research, not to sell them something. After that, retaining respondents becomes a matter of reinforcing that they are “doing a good service [because] it’s for research.”

Horizon’s telephone interviewers also recruit people to participate in focus groups. Typically, the company

needs four interviewers to spend about three hours just to fill a 12- person focus group. Finding willing individuals who meet the project’s specifications may require up to six hundred phone calls!

Horizon has recently formed partnerships with World Marketing and Fusion Marketing & Design. As a result, a researcher can get both consulting services and creative design to go along with the field research. In the end, they can get the total package by using all three partners together.

Sources: Based on Coleman, Kevin, “Research Firm Reflects Consumer Trends,” Columbia (Missouri) Daily Tribune (May 21, 2005), downloaded from http://www.columbiatribune.com; Horizon Research Services website, http://www.horizonresearch.com, accessed August 13, 2011.

Interviewing for Horizon Research Services

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Internet Surveys A similar approach should be used for an Internet survey. Potential respondents should receive an e-mail briefly explaining the project requesting assistance, as in the following example:

We are contacting you because of your interest in [subject matter inserted here]. We would like to invite you to participate in a survey that asks your opinion on matters related to [subject matter inserted here]. In return for your participation, we will [insert incentive here]. To participate, click on this URL: http:// www.clickhere.com.

Gaining Participation The Interviewer’s Manual from the Survey Research Center at the University of Michigan rec- ommends avoiding questions that ask permission for the interview, such as “May I come in?” and “Would you mind answering some questions?” Some people will refuse to participate or object to being interviewed. Interviewers should be instructed on handling objections. For example, if the respondent says, “I’m too busy right now,” the interviewer might be instructed to respond, “Will you be in at four o’clock this afternoon? I would be happy to schedule a time with you.” In other cases, client companies will not wish to offend any individual. In this case, the interviewer will be instructed to merely say, “Thank you for your time.”

The foot-in-the-door compliance technique and the door-in-the-face compliance technique are useful in securing interviews. Foot-in-the-door theory attempts to explain compliance with a large or difficult task on the basis of respondents’ earlier compliance with a smaller initial request. One experiment has shown that compliance with a minor telephone interview (that is, a small request that few people refuse) will lead to greater compliance with a second, larger request to fill out a long mail questionnaire. An interviewer employing door-in-the-face technique begins by making a substantial initial request that most people will reject (that is, slams the door in his or her face). When this happens, the interviewer can then request a smaller favor, such as asking a respondent to participate in a “short” survey of a few questions. However, this technique presents an ethical issue if the respondent is deceived. Thus, the initial request should also be a legitimate request.

Asking the Questions The purpose of an interview is, of course, to record a respondent’s answers. Training in the art of asking questions can be extremely beneficial, because interviewer bias can be a source of consider- able error in survey research.

There are five major rules for asking questions:

1. Ask questions exactly as they are worded in the questionnaire. 2. Read each question very carefully and clearly. 3. Ask the questions in the specified order. 4. Ask every question specified in the questionnaire. 5. Repeat questions that are misunderstood or misinterpreted.4

Interviewers are generally trained to know these rules, but when working in the field, many do not follow these procedures exactly. Inexperienced interviewers may not understand the importance of strict adherence to the instructions. Even professional interviewers have a tendency to take shortcuts when the task becomes monotonous. Interviewers may shorten questions or rephrase unconsciously when they rely on their memory of the question rather than reading the question as it is worded. Even the slightest change in wording may inject some bias into a study. By reading the question, the interviewer may be reminded to concentrate on avoiding slight variations in tone of voice on particular words or phrases.

If respondents do not understand a question, they usually will ask for some clarification. The recommended procedure is to repeat the question. If the person does not understand a term such as Roth 401(k) in the question, “How likely would you be to participate in a Roth 401(k) plan if

foot-in-the-door compliance technique

A technique for obtaining a high response rate, in which compliance with a large or difficult task is induced by first obtaining the respondent’s compliance with a smaller request.

door-in-the-face compliance technique

A two-step process for securing a high response rate. In step 1 an initial request, so large that nearly everyone refuses it, is made. Next, a second request is made for a smaller favor; respondents are expected to comply with this more reasonable request.

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offered by the company?” the interviewer should respond with a standard description of the term. If the respondent still doesn’t understand, interviewers are tempted to supply their own personal definitions and ad lib clarifications, and they may include words that are not free from bias. One reason interviewers do this is that field supervisors tend to reward people for submitting completed questionnaires and to be less tolerant of interviewers who leave questions blank because of alleged misunderstandings. However, researchers should be extremely careful to stay with a standardized definition to minimize bias.

Often respondents volunteer information relevant to a question that is supposed to be asked at a later point in the interview. In this situation the response should be recorded under the question that deals specifically with that subject. Then, rather than skip the question that was answered out of sequence, the interviewer should be trained to say something like, “We have briefly discussed this, but let me ask you . . . .” By asking every question, the interviewer can be sure that complete answers are recorded. If the partial answer to a question answered out of sequence is recorded on the space reserved for the earlier question and the subsequent question is skipped, an omission error will occur when the data are tabulated.

Probing When No Response Is Given Similar to the approach discussed for qualitative interviews, interviewers should be provided instructions on how to probe when respondents give no answer, incomplete answers, or answers that require clarification. As demonstrated in the Research Snapshots, probing questions can help in the clarification of a question within the interview process. By asking “why” carefully, the researcher can gain additional insight into the thoughts, attitudes, and behaviors of the respon- dent. First, probing is necessary when a respondent must be motivated to expand on, clarify, explain, or complete his or her answer. Interviewers must encourage respondents to clarify or expand on answers by providing a stimulus that will not suggest their own ideas or attitudes. An ability to probe with neutral stimuli is a mark of an experienced and effective interviewer. Second, probing may be necessary when a respondent begins to ramble or lose track. In such cases, a respondent must be led to focus on the specific content of the interview and to avoid irrelevant and unnecessary information.

The use of field interviews to answer specific research questions has many logistic and quality management challenges, but in many ways field interviews are unique in the ability to really capture what a respondent is thinking about. This is due to the very nature of the field interview—the ability to follow up and probe deeper on a respondent’s initial response. A key way that interviewers can capture this is through asking “why” follow-up questions.

Calo Research Services makes asking “why” their business. Whether it is for consumer research or for managerial strat- egy, Calo Research Services has adopted a philosophy from the top down that stresses the importance of asking why. For example, a company that was a participant in a professional trade show determined that capturing the number of visitors to their booth would help them evaluate the success of their presentation. However, it became clear that counting visitors does not really determine success—visitors can stop by for any

number of reasons—the real question is why they stopped by the booth.

Calo assisted the company by conducting a short interview that asked why the visitor to the booth was there, and what got their attention when they first appeared. This allowed the company to understand what was really connecting visitors to their booth, and allowed them to build on what was successful for their other presentations around the country.

Field interviewers that can probe deeper into the question of interest will recognize the value of this approach. Because the face-to-face interview can help tease out this kind of information—why not take advantage of this approach?

Source: Calo Research Services, Inc., http://www.caloresearch.com, accessed August 13, 2011.

Why Is “Why” Important?

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Interviewers have several possible probing tactics to choose from, depending on the situation:

■ Repeating the question. When the respondent remains completely silent, he or she may not have understood the question or decided how to answer it. Mere repetition may encourage the respondent to answer in such cases. For example, if the question is, “What do you not like about Guinness?” and the respondent does not answer, the interviewer may probe: “Just to check, is there anything that you do not like about Guinness?”

■ Using a silent probe. If the interviewer believes that the respondent has more to say, a silent probe—that is, an expectant pause, look, or nod of the head—may motivate the respondent to gather his or her thoughts and give a complete response.

■ Repeating the respondent’s reply. As the interviewer records the response, he or she may repeat the respondent’s reply verbatim. This may stimulate the respondent to expand on the answer.

■ Asking a neutral question. Asking a neutral question may specifically indicate the type of information that the interviewer is seeking. For example, if the interviewer believes that the respondent’s motives should be clarified, he or she might ask, “Tell me about this feeling.” If the interviewer feels that there is a need to clarify a word or phrase, he or she might say, “What do you mean by ______________?” Exhibit 18.1 gives some examples of common interview probes.

EXHIBIT 18.1

Commonly Used ProbesInterviewer’s Probe

What do you mean by…?

Can you tell me more about…?

Could you provide an example of…?

I am not sure I understand. Can you be more clear about…?

What exactly do you mean by…?

It seems that you are saying… Is that correct?

How would you feel if…?

Why do you feel that way?

Why is that important?

What would that look/feel/taste/sound/smell like?

What would your friends say about…?

How does this differ from…?

Is there anything else you want to share?

The purpose of asking questions as probes is to encourage responses. Such probes should be neutral and not leading. Probes may be general (such as “Anything else?”) or they may be questions specifi- cally designed by the interviewer to clarify a particular statement by the respondent.

Recording the Responses An analyst who fails to instruct fieldworkers in the techniques of properly recording survey answers rarely forgets to do so a second time. Although recording an answer seems extremely simple, mis- takes can occur in this phase of the research. Each fieldworker should use the same recording process. The opening chapter vignette demonstrates how one company is harnessing technology to help with this recording process.

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Rules for recording responses to fixed-alternative questions vary with the specific question- naire. A general rule, however, is to place a check mark in the box that correctly reflects the respon- dent’s answer. All too often interviewers don’t bother recording the answer to a filter question because they believe the subsequent answer will make the answer to the filter question obvious. However, editors and coders do not know how the respondent actually answered a question.

The general instruction for recording open-ended questions is to record the response verba- tim, a task that is difficult for most people. Inexperienced interviewers should be given an oppor- tunity to practice verbatim recording of answers before being sent into the field. Some suggestions for recording open-ended answers include

■ Record responses during the interview. ■ Use the respondent’s own words. ■ Do not summarize or paraphrase the respondent’s answer. ■ Include everything that pertains to the question objectives. ■ Include all of your probes.5

Especially for sensitive topics, decisions about how to record responses may be more difficult than these guidelines suggest. For a survey that included open-ended questions about sexual behavior, researchers found that some decisions about how to record answers affected the way responses were later interpreted. For example, they defined notation that would indicate pauses and vocal emphasis, which helped researchers identify answers that involved confusion or strong emotions. However, recording every nonverbal behavior led researchers to speculate about whether one respondent was crying or using drugs (he had a cold). Likewise, when transcriptions recorded the respondent’s exact words and pronunciation, including dialects and mistakes in grammar and word usage, researchers were tempted to speculate about demographic characteristics, such as a speaker’s

At Olson Zaltman Associates, highly trained interviewers probe for the deeper thinking that underlies attitudes toward brands or product categories. The research firm’s method, called ZMET (for Zaltman Metaphor Elicitation Technique), begins by asking each respondent to come to a one-on-one interview, bring- ing along a set of eight to ten photographs related to their thoughts and feelings about the interview’s topic. The inter- viewer uses the photos as nonverbal clues about the associa- tions the person makes with the product or brand.

A typical interview lasts two hours. The interviewer’s challenge is to ask questions that reveal what is behind the selection of the photographs without actually suggesting the interviewer’s own ideas. The interviewer begins by asking the respondent to describe the topic-related thoughts and feel- ings that each picture illustrates. The interviewer then probes to uncover a deeper meaning by asking the respondent to elaborate on the initial statements. This process requires skill based on training in fields such as psychotherapy and sociology. Finally, the respondent works with an associate to create a com- puterized collage that illustrates the respondent’s thoughts and feelings about the topic.

Researchers then use computer software to identify response patterns that suggest “metaphors” for the product—a

general theme that describes respondents’ attitudes. In a study of air fresheners, people want to avoid having odors in their home alienate them from visitors (an underlying desire for con- nection with others); they also want an air freshener to seem natural, rather than masking something (an underlying desire to evoke nature). Based on these ideas, the client developed Breeze air freshener. In another project, Motorola hired Olson Zaltman to help it market a high-tech security system. Many research participants brought in images of dogs, signifying the protection that dogs give their owners. As a result, Motorola avoided brand names emphasizing technology, instead calling the new system the Watchdog.

The ZMET approach has shown to be useful across a wide variety of situations and cultures. In fact, Olson Zaltman Associ- ates has conducted nearly 500 ZMET studies in 40 countries in “languages ranging from Arabic to Zulu.”

Sources: Christensen, Glenn L. and Jerry C. Olson, “Mapping Consumers’ Mental Models with ZMET,” Psychology & Marketing 19, no. 6 (June 2002), 477–502; Olson Zaltman Associates, http://www.olsonzaltman.com, accessed August 3, 2011.

Probing for Deeper Meaning at Olson Zaltman Associates

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race or educational level. As the researchers evaluated the effects of these decisions about how to record answers, they concluded that such decisions should be made carefully in light of the research objectives.6

Terminating the Interview The final aspect of training is to instruct interviewers on how to close the interview. Fieldworkers should wait to close the interview until they have secured all pertinent information. The inter- viewer who departs hastily will be unable to record the spontaneous comments respondents some- times offer after all formal questions have been asked. Merely recording one of these comments may result in a new idea or creative interpretation of the results. Avoiding hasty departures is also a matter of courtesy. The fieldworker should also answer any respondent questions concerning the nature and purpose of the study to the best of his or her ability.

Finally, it is extremely important to thank the respondent for his or her time and cooperation. Not only is this the polite thing to do, but the fieldworker may be required to reinterview the respondent at some future time. So, the respondent should be left with a positive feeling about having cooperated in a worthwhile operation.

Principles of Good Interviewing Yankelovich Partners is one of the nation’s top research organizations.7 One reason for its success is its careful attention to fieldwork. This section presents this organization’s principles of good interviewing. These principles apply no matter what the nature of the specific assignment; they are universal and represent the essence of sound data collection for business research purposes. For clarity, they have been divided into two categories: the basics (the interviewing point of view) and required practices (standard inquiry premises and procedures).

The Basics Interviewing is a skilled occupation so not everyone can do it, and even fewer can do it extremely well. A good interviewer observes the following basic principles:

1. Have integrity, and be honest. This is the cornerstone of all professional inquiry, regardless of its purpose.

2. Have patience and tact. Interviewers ask for information from people they do not know. Thus, all the rules of human relations that apply to inquiry situations—patience, tact, and courtesy—apply even more to interviewing. You should at all times follow the standard busi- ness conventions that control communications and contact.

3. Pay attention to accuracy and detail. Among the greatest interviewing “sins” are inaccuracy and superficiality, for the professional analyst can misunderstand, and in turn mislead, a cli- ent. A good rule to follow is not to record a response unless you fully understand it yourself. Probe for clarification and rich, full answers. Record responses verbatim: never assume you know what a respondent is thinking or jump to conclusions as to what he or she might have said but did not.

4. Exhibit a real interest in the inquiry at hand, but keep your own opinions to yourself. Impar- tiality is imperative—if your opinions were wanted, you would be asked, not your respondent. You are an asker and a recorder of other people’s opinions, not a contributor to the study data.

5. Be a good listener. Too many interviewers talk too much, wasting time when respondents could be supplying more pertinent facts or opinions on the study topic.

6. Keep the inquiry and respondents’ responses confidential. Do not discuss the studies you are doing with relatives, friends, or associates; it is unacceptable to both the research agency and its clients. Above all, never quote one respondent’s opinion to another—that is the greatest violation of privacy.

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7. Respect others’ rights. Business research depends on people’s willingness to provide infor- mation. In obtaining this information, you must follow a happy medium path. Between the undesirable extremes of failure to get it all and unnecessary coercion, this middle road is one of clear explanation, friendliness, and courtesy, offered in an interested and persuasive tone. Impress upon prospective respondents that their cooperation is important and valuable.

Required Practices Here are practical rules of research inquiry that should be followed and used without exception:

1. Complete the number of interviews according to the sampling plan assigned to you. Both are calculated with the utmost precision so that when assignments are returned, the study will benefit from having available the amount and type of information originally specified.

2. Follow the directions provided. Remember that many other interviewers are working on the same study in other places. Lack of uniformity in procedure can only spell disaster for later analysis. Each direction has a purpose, even though it may not be completely evident to you.

3. Make every effort to keep schedules. Schedules range from “hurry up” to “there should be plenty of time,” but there is always a good reason, and you should be as responsive as possible. If you foresee problems, call and explain.

4. Keep control of each interview you do. It is up to you to determine the pace of a particular interview, keeping several points in mind: a. There is an established average length of an interview from the time you start to talk to

the respondent to the time you finish. It represents a guideline, but some interviews will be shorter and some longer.

b. Always get the whole story from a respondent, and write it all down in the respondent’s own words. Also, remember to keep the interview focused on the subject at hand and prevent it from wandering off into unnecessary small talk.

c. Avoid offending the respondent by being too talkative yourself. 5. Complete the questionnaires meticulously.

a. Follow exactly all instructions that appear directly on the questionnaire. Before you start interviewing, learn what these instructions direct you to do.

b. Ask the questions from the first to the last in the exact numerical order (unless directed to do otherwise in some particular instances). Much thought and effort go into determining the order of the questioning to avoid bias or to set the stage for subsequent questions.

c. Ask each question exactly as it is written. The cost of doing so is lack of uniformity; the research agency would never know whether all respondents were replying to the same question or replying to 50 different interviewers’ interpretations of the question.

d. Never leave a question blank. It will be difficult to tell whether you failed to ask it, whether the respondent could not answer it because of lack of knowledge or certainty, or whether the respondent refused to answer it for personal reasons. If none of the answer categories provided prove suitable, write in what the respondent said, in his or her own words.

e. Use all the props provided to aid both interviewers and respondents: show cards, pictures, descriptions, sheets of questions for the respondents to answer themselves, and so on. All have a specific interview purpose. Keys to when and how to use them appear on the questionnaire at the point at which they are to be used.

6. Check over each questionnaire you have completed. This is best done directly after it has been completed. If you find something you did wrong or omitted, correct it. Often you can call a respondent back, admit you missed something (or are unclear about a particular response), and then straighten out the difficulty.

7. Compare your sample execution and assigned quota with the total number of questionnaires you have completed. Do not consider your assignment finished until you have done this.

8. Clear up any questions with the research agency. At the start of an assignment or after you have begun, if you have questions for which you can find no explanatory instructions, call the agency to get the matter clarified.

450 • PART FIVE Sampling and Fieldwork

Fieldwork Management Research managers preparing for the fieldwork stage should consider the meaning of the following stanza from Robert Burns’s poem “To a Mouse”:

The best laid schemes o’ mice and men Gang aft a-gley; An’ lea’e us nought but grief and pain, For promis’d joy.

In other words, the best plans of mice and men—as well as business researchers—may go astray. An excellent research plan may get sidetracked if the field operations are performed incorrectly. A proper research design will minimize numerous sources of error, but careful execution of the fieldwork is necessary to produce results without substantial error. For these reasons fieldwork management is an essential part of the research process.

Managers of field operations select, train, supervise, and control fieldworkers. Our discussion of fieldwork principles mentioned selection and training. This section investigates the tasks of the fieldwork managers in greater detail.

Briefing Session for Experienced Interviewers Whether interviewers have just completed their training in fundamentals or are already expe- rienced, they always need to be informed about the individual project. Both experienced and inexperienced fieldworkers must be briefed on the background of the sponsoring organization, sampling techniques, asking of questions, callback procedures, and other matters specific to the particular project.

If there are any special instructions—for example, about using show cards or video equipment or restricted interviewing times—they should also be covered during the training session. Instruc- tions for handling certain key questions are always important. For example, the following field- worker instructions appeared in a survey of institutional investors who make buy-and-sell decisions about stocks for banks, pension funds, and so on:

Questions 13a, 13b

These questions will provide verbatim comments for the report to the client. Probe for more than one- or two-word answers and record verbatim. Particularly, probe for more information when respondent gives a general answer—e.g., “Poor management,” “It’s in a good industry.” Ask, “In what ways is manage- ment poor?” “What’s good about the industry?” And so on.

A training session for experienced interviewers might go something like this: All interviewers report to the central office, where they receive a brief explanation of the firm’s background and the general aims of the study. Interviewers are provided with minimal information about the pur- pose of the study to ensure that they will not transmit any preconceived notions to respondents. For example, in a survey about the banks in a community, the interviewers would be told that the research is a banking study but not the name of the sponsoring bank. To train the interviewers about the questionnaire, a field supervisor conducts an interview with another field supervisor who acts as a respondent. The trainees observe the interviewing process, after which they each interview and record the responses of another field supervisor who acts as a respondent. After the practice interview, the trainees receive additional instructions.

Training to Avoid Procedural Errors in Sample Selection The briefing session also covers the sampling procedure. A number of research projects allow the interviewer to be at least partially responsible for selecting the sample. These sampling methods offer the potential for selection bias. This potential for bias is obvious in the case of quota sampling but less obvious in other cases. For example, in probability sampling in which every nth house is selected, the fieldworker uses his or her discretion in identifying housing units. Avoiding selection

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bias may be more difficult than it sounds. For example, in an old, exclusive neighborhood, a man- sion’s coach house or servants’ quarters may have been converted into an apartment that should be identified as a housing unit. This type of dwelling and other unusual housing units (apartments with alley entrances only, lake cottages, or rooming houses) may be overlooked, giving rise to selec- tion error. Errors may also occur in the selection of random-digit dialing samples. Considerable effort should be expended in training and supervisory control to minimize these errors.

Another selection problem is the practice of contacting a respondent when and where it is con- venient for both parties. Consider the following anecdote from an industrial research interviewer:

Occasionally getting to the interview is half the challenge and tests the interviewer’s ingenuity. Finding your way around a huge steel mill is not easy. Even worse is trying to find a correct turn-off to gravel pit D when it’s snowing so hard that most direction signs are obliterated. In arranging an appointment with an executive at a rock quarry outside Kansas City, he told me his office was in “Cave Number 3.” It was no joke. To my surprise, I found a luxurious executive office in a cave, which had long ago been hollowed by digging for raw material.8

In that case, just finding the sample unit was half the battle!

Supervision of Fieldworkers Although briefing and training interviewers will minimize the probability of their interview- ing the wrong households or asking biased questions, there is still considerable potential for errors in the field. Direct supervision of personal interviewers, telephone interviewers, and other

Interviewers and their supervisors can improve the process of data collection to minimize errors. One popular method, total quality management (TQM), seeks continuous improvement by getting everyone involved in measuring performance and look- ing for ways to improve processes:

■ Measure response rates, and improve interviewer training to improve response rates. To do this, researchers must describe the procedure for contacting subjects and consider alternatives, such as letters of introduction, the timing of contacts, and the number of attempts to make before a subject is classified as a nonrespondent. Interviewers should be taught about the impact on research quality of interview- ing only the people who are easiest to contact, and they should be trained to persuade people to participate.

■ Measure defects in terms of measurement errors and improve interviewer techniques and respondent behavior. Researchers should measure the pattern of response rates by interviewer, looking for interviewer variance (a tendency for different interviewers to obtain different answers). To measure respondent behavior, researchers can ask inter- viewers for objective information such as the presence of a third person, as well as for an evaluation of each interview’s success; the data may signal respondent behaviors with a

potential to bias responses from cer- tain segments.

■ Measure the interview process, including the train- ing provided, the application of prin- ciples from train- ing, and feedback about the inter- viewer. The training should be aimed at specific, measurable objectives, with a plan for measuring whether the inter- viewers’ performance shows that training objectives were met. For a standardized interview, one way to tell whether the interviews are following the guidelines is to measure whether they all last about the same amount of time. Veri- fication by reinterviewing a subsample provides insight into the accuracy of recording responses. Where variances occur, the supervisor and interviewers should investigate the cause, looking for ways to improve training and interviewing.

Source: Based on Loosveldt, Geert, Ann Carton, and Jaak Billiet, “Assess- ment of Survey Data Quality: A Pragmatic Approach Focused on Interviewer Tasks,” International Journal of Market Research (Spring 2004), 65–82.

Total Quality Management for Interviewing

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fieldworkers is necessary to ensure that the techniques communicated in the training sessions are implemented in the field.

Supervision of interviewers, like other forms of supervision, refers to controlling the efforts of workers. Field supervision of interviewers requires checking to see that field procedures are being properly followed. A supervisor checks field operations to ensure that the interviewing schedule is being met. Supervisors collect the questionnaires or other instruments daily and edit them for completeness and legibility. (See Chapter 19 for more details on editing.) If problems arise, supervi- sors discuss them with the fieldworkers, providing training when necessary.

As seen in the Research Snapshot “Total Quality Management for Interviewing,” the impor- tance of quality control cannot be underestimated. In addition to quality control, continual training may be provided. For example, if a telephone supervisor notices that interviewers are allowing the phone to ring more than eight times before considering the call a “no answer,” the supervisor can instruct interviewers not to do so, as the person who eventually answers is likely to be annoyed.

Sampling Verification Another important job of a supervisor is to verify that interviews are being conducted according to the sampling plan rather than with the sampling units most accessible to the interviewer. An interviewer might be tempted to go to the household next door for an interview rather than record the sampling unit as not at home, which would require a callback. Carefully recording the number of completed interviews will help ensure that the sampling procedure is being properly conducted. Supervisors are responsible for motivating interviewers to follow the sampling plan carefully.

Closer supervision of the interviewing procedure can occur in central-location telephone interviewing. Supervisors may be able to listen to the actual interview by switching onto the interviewer’s line. Of course, this is harder to do when interviewers call from their own homes.

Supervisors must also make sure that the right people within the household or sampling unit are being contacted. One research project for a children’s cereal required that several products be placed in the home and that children record their daily consumption and reactions to each cereal in a diary. Although the interviewers were supposed to contact the children to remind them to fill out the diaries, a field supervisor observed that in almost half the cases the mothers were filling out the diaries after the children left for school because their children had not done so. The novelty of the research project had worn off after a few days; eating a specific cereal each day was no longer fun after the first few times, and the children had stopped keeping the diaries. Similar situations may occur with physicians, executives, and other busy people. The interviewer may find it easier to interview a nurse, secretary, or other assistant rather than wait to speak with the right person.

Interviewer Cheating The most blatant form of interviewer cheating occurs when an interviewer falsifies interviews, merely filling in fake answers rather than contacting respondents. This is sometimes referred to as curbstoning. Although this situation does occur, it is not common if the job of interviewer selection has been properly accomplished. However, less obvious forms of interviewer cheating occur with greater frequency. Interviewers often consider quota sampling to be time consuming, so an interviewer may stretch the requirements a bit to obtain seemingly qualified respondents. In the interviewer’s eyes, a young-looking 36-year-old may be the same as a 29-year-old who fits the quota requirement; checking off the under-30 category thus isn’t really cheating. Consider the fieldworker who must select only heavy users of a certain brand of hand lotion that the client says is used by 15 percent of the population. If the fieldworker finds that only 3 percent qualify as heavy users, he or she may be tempted to interview an occasional user to stretch the quota somewhat. While we can understand the temptation, all of these approaches are unethical.

An interviewer may fake part of a questionnaire to make it acceptable to the field supervisor. In a survey on automobile satellite radio systems, suppose an interviewer is requested to ask for five reasons why consumers have purchased this product. If he or she finds that people typically give two or perhaps three reasons and even with extensive probing cannot think of five reasons,

interviewer cheating

The practice by fieldworkers of filling in fake answers or falsifying interviews.

curbstoning

A form of interviewer cheating in which an interviewer makes up the responses instead of conducting an actual interview.

CHAPTER 18 Fieldwork • 453

verification

Quality-control procedures in fieldwork intended to ensure

that interviewers are following the sampling procedures

and to determine whether interviewers are cheating.

the interviewer might be tempted to cheat. Rather than have the supervisor think he or she was goofing off on the probing, the interviewer may fill in five reasons based on past interviews. In other cases, the interviewer may cut corners to save time and energy.

Interviewers may also fake answers when they find questions embarrassing or troublesome to ask because of sensitive subjects. Thus, the interviewer may complete most of the questionnaire but leave out a question or two because he or she found it troublesome. For example, in a survey among physicians, an interviewer might find questions about artificial-insemination donor pro- grams embarrassing, skip these questions, and fill in the gaps later.

The examples above are all, to some degree, intentional forms of interviewer cheating. What appears to be interviewer cheating can also be unintentional, most often a result of improper train- ing or fieldworkers’ inexperience. A fieldworker who does not understand the instructions may skip or miss a portion of the questionnaire.

Fieldworkers are often instructed to conclude each interview with a comment like, “Thank you for your time. By the way, my supervisor may call you to ask about my work. Please say what- ever you wish.” This or a similar statement not only increases the number of respondents willing to cooperate with the verification process but also helps to assure the quality of fieldwork.

Verification by Reinterviewing Supervision for quality control attempts to ensure that interviewers are following the sampling procedure and to detect falsification of interviews. Supervisors verify approximately 10–15 per- cent of the interviews by reinterviewing. Normally the interview is not repeated; rather, supervi- sors recontact respondents and ask about the length of the interview and their reactions to the interviewer; then they collect basic demographic data to check for interviewer cheating. Such verification does not detect the more subtle form of cheating in which only portions of the interview have been falsified. A validation check may simply point out that an interviewer con- tacted the proper household but interviewed the wrong individual in that household—which, of course, can be a serious error.

Fieldworkers should be aware of supervisory verification practices. Knowing that there may be a telephone or postcard validation check often reminds interviewers to be conscientious in their work. The interviewer who is conducting quota sampling and needs an upper-income Hispanic male will be less tempted to interview a middle-income Hispanic man and falsify the income data in this situation.

Certain information may allow for partial verification without recontacting the respon- dent. Computer-assisted telephone interviewers often do not know the phone number dialed by the computer or other basic information about the respondent. Thus, answers to ques- tions added to the end of the telephone interview to identify a respondent’s area code, phone number, city, zip code, and so on may be used to verify the interview. The computer can also record every attempted call, the time intervals between calls, and the time required to conduct each completed interview—data that may help in identifying patterns related to cheating by interviewers.

SUMMARY 1. Describe the role and job requirements of fieldworkers. Fieldworkers are responsible for gathering data in the field. These activities may be performed by the organization that needs the information, by research suppliers, or by third-party field service organizations. Proper execution of fieldwork is essential to produce research results without substantial error. Proper control of fieldwork begins with interviewer selection. Fieldworkers generally should be healthy, outgoing, and well groomed.

2. Summarize the skills to cover when training inexperienced interviewers. New field workers must be trained in opening the interview, asking the questions, probing for additional informa- tion, recording the responses, and terminating the interview.

454 • PART FIVE Sampling and Fieldwork

3. List principles of good interviewing. Good interviewers have integrity, patience, and tact. They are attentive to detail and interested in the inquiry at hand. They behave impartially, listen carefully, and maintain confidentiality. They respect the rights of others. Interviewing should adhere to several required practices. Interviewers should complete all interviews according to the sample plan and follow the directions provided. They should try to meet schedules and maintain control of the interview. They should fill in answers meticulously and then check over the ques- tionnaire to make sure it is complete. Before finishing an assignment, they should verify that the number of completed questionnaires matches the sampling plan and assigned quotas. If they have questions, they should check with the agency conducting the research.

4. Describe the activities involved in the management of fieldworkers. Experienced field- workers are briefed for each new project to familiarize them with its specific requirements. A par- ticular concern of the briefing session is reminding fieldworkers to adhere closely to the prescribed sampling procedures.

5. Discuss how supervisors can minimize errors in the field. Careful supervision of fieldwork- ers also is necessary. Supervisors gather and edit questionnaires each day. They check to see that field procedures are being properly followed and that interviews are on schedule. They also check to ensure that the correct sampling units are being used and that the proper people are responding in the study. Finally, supervisors check for interviewer cheating and verify portions of the inter- views by reinterviewing a certain percentage of each fieldworker’s respondents.

KEY TERMS AND CONCEPTS briefing session, 443 curbstoning, 453 door-in-the-face compliance technique, 445

field interviewing service, 442 fieldworker, 442 foot-in-the-door compliance technique, 445

in-house interviewer, 442 interviewer cheating, 453 verification, 454

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What qualities should fieldworkers possess? 2. ETHICS An interviewer has a rather long telephone interview.

The estimate suggests that fully completing the survey will take 30 minutes. However, what do you think the response rate will be if people are told ahead of time that it will take 30 minutes to finish participating in the survey? Should the interviewer fudge a little and state that the survey will take only 15 minutes? Explain.

3. What should the interviewer do if a question is misunderstood? If a respondent answers a question before encountering it in the questionnaire?

4. When should interviewers probe? Give some examples of how probing should be done.

5. How should respondents’ answers to open-ended questions be recorded?

6. How should the fieldworker terminate the interview? 7. Why is it important to ensure that fieldworkers adhere to the

sampling procedure specified for a project? 8. ETHICS What forms does interviewer cheating take? How can

such cheating be prevented or detected? 9. ETHICS Two interviewers are accused of curbstoning. What

have they done? 10. Comment on the following field situations.

a. After conducting a survey with 10 people, an interviewer noticed that many of the respondents were saying, “Was I right?” after a particular question.

b. A questionnaire asking about a new easy-opening can has the following instructions to interviewers:

(Hand respondent can and matching instruction card.)

“Would you please read the instructions on this card and then open this can for me?” (Interviewer: Note any comments respondent makes. Do not under any circumstances help him or her to open the can or offer any explanation as to how to open it. If respondent asks for help, tell him that the instructions are on the card. Do not discuss the can or its contents.)

c. A researcher gives balloons to children of respondents to keep the children occupied during the interview.

d. An interviewer tells the supervisor, “With the price of gas, this job isn’t paying as well as before!”

e. When a respondent asks how much time the survey will take, the interviewer responds, “15 to 20 minutes.” The respondent says, “I’m sorry, I have to refuse. I can’t give you that much time right now.”

11. Write some interviewer instructions for a telephone survey. 12. A fieldworker conducting a political poll is instructed to inter-

view registered voters. The fieldworker interviews all willing participants who are eligible to vote (those who may register in the future) because allowing their opinions to be recorded is part of her patriotic duty. Is she doing the right thing?

13. An interviewer finds that when potential respondents ask how much time the survey will take, most refuse if they are told 15 minutes. The interviewer now says 10 minutes and finds that most respondents enjoy answering the questions. Is this the right thing to do?

14. A fieldworker asks respondents whether they will answer a few questions. However, the interviewer also observes the respon- dent’s race and approximate age. Is this ethical?

Chapter 18: Fieldwork 455

RESEARCH ACTIVITY 1. ’NET Go to http://www.quirks.com/directory/telephone/

index.aspx. Using the search window, investigate the following. Suppose you were interested in conducting telephone inter-

views in a number of places. List telephone facilities in Denmark,

Mexico, South Korea, and Alabama (United States). Is CATI available in every county of Alabama?

Thomas and Dorothy Leavey Library The Thomas and Dorothy Leavey Library serves the students and faculty of the University of Southern California. Staff at the busy library wanted to know

more about its patrons, what library resources they find helpful, and whether they are satisfied with the library’s services. However, like many libraries, this organization had a tiny budget for business research. As a result, the goal was to conduct exploratory research while spending less than $250.9

Staff members studied surveys conducted by other libraries to get ideas for a one-page printed questionnaire. Colleagues on the library staff provided suggestions, and a few undergraduates tested the sur- vey for clarity. Next, the survey schedule was chosen: 36 continuous hours that did not conflict with any holidays or exams.

The fieldwork involved setting up and staffing a table offering the survey and then inviting library patrons to stop and fill out a ques- tionnaire. Possible locations included space near an elevator, stairs,

or computers, but the lobby area offered the greatest opportunity, because everyone passed through the lobby when using the facility’s only entrance. The survey’s planners divided the time into 60 slots and recruited students with jobs at the library to serve as the fieldworkers. Other members of the library staff also volunteered to fill time slots. The students in particular were enthusiastic about inviting library patrons to complete questionnaires. A bowl of candy for participants was a small incentive, combined with a raffle for donated prizes.

Questions 1. Imagine that you were asked to help prepare for this survey.

What fieldwork challenges would you expect to arise in a survey such as this, to be carried out by inexperienced fieldworkers?

2. What training would you recommend for the students and other library staffers conducting this survey? Suggest topics to cover and advice to give these fieldworkers.

Margaret Murphy O’Hara Margaret Murphy O’Hara was fatigued. As she wiped the perspiration from her brow, she felt that the Massachusetts summer sun was playing a trick

on her. It was her first day at work, and the weather was hot. She had no idea that being a field interviewer required so much stamina. Even though she was tired, she was happy with her new job. She didn’t yet have the knack of holding her purse, question- naires, and clipboard while administering the show cards, but she knew she’d get the hang of it. The balancing act can be learned, she thought.

When she met her supervisor, Mary Zagorski, at the end of her first day, Margaret described her day. Margaret said she thought the questionnaire was a bit too long. She laughed, saying that an elderly lady had fallen asleep after about 20 minutes of interviewing.

Margaret mentioned that a number of people had asked why they were selected. Margaret said she did not know exactly what to say when somebody asked, “Why did you pick me?”

She said that the nicest person she had interviewed was a man whose wife wasn’t home to be surveyed. He was very friendly and didn’t balk at being asked about his income and age like some of the other people she had interviewed.

She said she had one problem that she needed some help with resolving. Four or five people refused to grant the interview. Marga- ret explained that one woman answered the door and said she was too busy because her son, an army private, was leaving the country. The woman was throwing a little party for him before he went off to the airport. Margaret didn’t want to spoil their fun with the survey. Another lady said that she was too busy and really didn’t know any- thing about the subject anyway. However, she did suggest her next- door neighbor, who was very interested in the subject. Margaret was able to interview this person to make up for the lost interview. It actually went quite well.

Margaret said another woman wouldn’t be interviewed because she didn’t know anything about the Zagorski interviewing service, and Margaret didn’t know quite what to tell her. Finally, she couldn’t make one interview because she didn’t understand the address: 9615 South Francisco Rear. Margaret told Mary it was quite a day, and she looked forward to tomorrow.

Questions 1. Is Margaret going to be a good professional interviewer? 2. What should Mary Zagorski tell Margaret?

CASE 18.2

CASE 18.1

456 Part 5: Sampling and Fieldwork

P a r t S I X

Data Analysis and Presentation

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information

CHAPTER 20 Basic Data Analysis: Descriptive Statistics

CHAPTER 21 Univariate Statistical Analysis

CHAPTER 22 Bivariate Statistical Analysis: Differences between Two Variables

APPENDIX 22A Manual Calculation of an F-Statistic

APPENDIX 22B ANOVA for Complex Experimental Designs

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association

APPENDIX 23A Arithmetic behind OLS

CHAPTER 24 Multivariate Statistical Analysis

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-up

458

After studying this chapter, you should be able to

1. Know when a response is really an error and should be edited

2. Appreciate coding of pure qualitative research

3. Understand the way data are represented in a data file

4. Understand the coding of structured responses including a dummy variable approach

5. Appreciate the ways that technological advances have simplified the coding process

C H A P T E R

U nderstanding what respondents say, or coding the data gathered from a survey represents a challenge that all business researchers face. But technological advances now allow busi-

ness researchers a chance to collect and code data not based upon what people say, but what their face “says.” Welcome to the new world of Sensory Logic and their Facial Action Coding System (FACS)!1

Body language is a key way that people communicate their thoughts and emotional states. Researchers have long known the power of nonverbal cues and response states, and have sought to understand and capture how nonverbal communication unfolds. Sensor Logic’s FACS “specializes in quantifying emotional response through the facial coding tool” and is one example of how research in eye movement and facial coding has advanced to a point where respondent physical data can be captured in real time for research purposes. Facial coding reveals a person’s engagement, their positive and negative emotional states given a particular stimuli, and the impact or appeal of what they are responding to. Eye tracking can tell researchers exactly what a per- son is looking at, and based upon the almost imperceptible muscle changes in their facial expressions, code their emotional state.

The FACS is used in a number of consumer and market research environments. For example, a company may develop a web page designed to showcase their product line. Using FACS,

focus group members can have their nonverbal responses and eye tracking analyzed, to see which aspects of the product are being examined, and what immediate emotional response is generated. The objective data can then be paired with other data, and analyzed for business purposes.

In the future, the ability to capture and code physical data will become more and more commonplace. Perhaps you too will have a chance to participate in a product or service research study using Facial Action Coding, and be given an opportunity to “speak” without ever saying a word.

LEARNING OUTCOMES

Editing and Coding Transforming Raw Data into Information

Chapter Vignette:

Coding What a Person’s Face “Says”

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Introduction A very common phrase that is used by researchers is “garbage in, garbage out.” This refers to the idea that if data is collected improperly, or coded incorrectly, your results are “garbage,” because that is what was entered into the data set to begin with. This chapter focuses on the critical key step of data entry and coding. Like any part of the business research process, care and attention to detail are important requirements for data editing and coding.

Stages of Data Analysis Researchers tend to be very anxious to begin data analysis once the fieldwork is complete. Now, the raw data can be transformed into intelligence. However, raw data may not be in a form that lends itself well to analysis. Raw data are recorded just as the respondent indicated. For an oral response, the raw data are in the words of the respondent, whereas for a questionnaire response, the actual number checked is the number stored. Raw data will often also contain errors both in the form of respondent errors and nonrespondent errors. Whereas a respondent error is a mistake made by the respondent, a nonrespondent error is a mistake made by an interviewer or by a person responsible for creating an electronic data file representing the responses.

Exhibit 19.1 provides an overview of data analysis. The first two stages result in an electronic file suitable for data analysis. This file can then be used in the application of various statistical

raw data

The unedited information gathered from a respondent in the exact form as provided by that respondent.

nonrespondent error

Error that the respondent is not responsible for creating, such as when the interviewer marks a response incorrectly.

EXHIBIT 19.1

Overview of the Stages of Data Analysis

Raw Data

Editing

Coding

Data File

Descriptive Analysis

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Analysis Approach?

Intelligence

Error Checking Takes Place in Each of These Stages

Source: Sonquist, J. A. & Dunkelberg, W. C., Survey and Opinion Research: Procedures for Processing and Analysis, (1977). Englewood Cliffs, NJ: Prentice-Hall. Reprinted by permission of Pearson Education, Inc., Upper Saddle River, NJ.

This chapter deals with coding and editing raw data. While most of the data coding in business research is more mundane than Facial Action Coding, researchers must pay careful attention

to their coding because poor coding leads directly to nonrespon- dent error in the research data.2

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 459

460

routines including those associated with descriptive, univariate, bivariate, or multivariate analysis. Each of these data analysis approaches will be discussed in the subsequent chapters. An important part of the editing, coding, and filing stages is checking for errors. As long as error remains in the data, the process of transformation from raw data to intelligence will be made more risky and more difficult. Editing and coding are the first two stages in the data analysis process.

Data integrity refers to the notion that the data file actually contains the information that the researcher promised the decision maker he or she would obtain. Additionally, data integrity exte nds to the fact that the data have been edited and properly coded so that they are useful to the decision maker. Any errors in this process, just as with errors or shortcuts in the interview process itself, harm the integrity of the data.

Editing Fieldwork often produces data containing mistakes. For example, consider the following simple questionnaire item and response:

How long have you lived at your current address? 30

The researcher had intended the response to be provided in years. While it is possible the respon dent did provide an answer in years, perhaps the respondent has indicated the number of months rather than years he or she has lived at this address? Alternatively, if this was an interviewer’s form, he or she may have marked the response in months without indicating this on the form. How should this be treated? Further, responses may sometimes be contradictory. What if this same respondent gives the following response in regard to age?

What is your age? 28 years

This answer contradicts the earlier response. If the respondent is 28 years of age, then how could he or she have lived at the same address for 30 years? Therefore, an adjustment should be made to accommodate this information. The most likely case is that this respondent has lived at the current address for 2.5 years.

This example illustrates data editing. Editing is the process of checking and adjusting data for omissions, consistency, and legibility. In this way, the data become ready for analysis by a computer.

So, the editor’s task is to check for errors and omissions on questionnaires or other data collection forms. When the editor discovers a problem, he or she adjusts the data to make them more complete, consistent, or readable.

At times, the editor may need to reconstruct data. In the example above, the researcher can guess with some certainty that the respondent entered the original questions in months instead of years. Therefore, the probable true answer can be reconstructed. While the editor should try to make adjustments in an effort to represent as much information from a respondent as possible, reconstructing responses in this fashion should be done only when the probable true response is very obvious. Had the respondent’s age been 32 years, converting the 30 into 2.5 years would not have been advisable barring other information. Perhaps the

data integrity

The notion that the data file actually contains the

information that the researcher promised the decision maker

he or she would obtain, meaning in part that the data

have been edited and properly coded so that they are useful

to the decision maker.

editing

The process of checking the completeness, consistency, and

legibility of data and making the data ready for coding and

transfer to storage.

SURVEY

THIS! The Survey This! feature can help in understanding data cod- ing and basic analyses. How are data entry, editing, and coding made easier using a Qualtrics-type data approach relative to a paper and pencil survey approach? Do any questions in the survey present particular coding issues that are not fully addressed automatically by Qualtrics software? Are there any variables that would best be coded as dummy variables? What are they? What type of coding would you suggest for the question about your boss and animals shown here?

Field edits allow supervisors to spot errors before the data file is created.

Courtesy of Qualtrics.com

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respondent has lived in the house since childhood? That possibility would seem real enough to prevent changing the response. In the end, the researcher should make thoughtful editing changes, but should be cautious about adding bad data to good data. We are always better off with slightly less data that we are confident in than more data that has concerns.

Field Editing Field supervisors often are responsible for conducting preliminary field editing on the same day as the interview. Field editing is used to:

1. Identify technical omissions such as a blank page on an interview form. 2. Check legibility of handwriting for openended responses. 3. Clarify responses that are logically or conceptually inconsistent.

Field editing is particularly useful when personal interviews have been used to gather data. In these cases, a daily field edit allows supervisors to deal with any problems quickly by asking the interviewer while it is still fresh in their mind. The interviewer may still be able to remember the interviews and clarify any issues. In addition, the number of unanswered questions or incomplete responses can be reduced with rapid followup. A daily field edit allows fieldworkers to identify respondents who should be recontacted to fill in omissions in a timely fashion.

The supervisor may also use field edits to spot the need for further interviewer training or to correct faulty procedures. For example, if an interviewer did not correctly follow skip patterns, additional training may be required. The supervisor may also notice that an interviewer is not properly probing some openended responses.

In-House Editing Although simultaneous field editing is highly desirable, in many situations (particularly with mail questionnaires) early reviewing of the data is not always possible. In-house editing rigorously inves tigates the results of data collection. The research supplier or research department normally has a centralized office staff perform the editing and coding function.

For example, Arbitron measures radio audiences by having respondents record their listening behavior—time, station, and place (home or car)—in diaries. After the diaries are returned by mail, inhouse editors perform usability edits in which they check that the postmark is after the last day of the survey week, verify the legibility of station call letters (station WKXY could look like KWXY), look for completeness of entries on each day of the week, and perform other editing activities. If the respondent’s age or sex is not indicated, the respondent is called to ensure that this information is included.

Illustrating Inconsistency—Fact or Fiction? Consider a situation in which a telephone interviewer has been instructed to interview only regis tered voters in a state that requires voters to be at least 18 years old. If the editor’s review of a question naire indicates that the respondent was 17 years old, the editor’s task is to correct this mistake. In this case, the respondent must be deleted because this individual should never have been considered as a sampling unit. The sampling units (respondents) should all be consistent with the defined population.

The editor also should check for consistency within the data collection framework. For exam ple, a survey on outshopping behavior (shopping in towns other than the one in which the person resides) might have a question such as the following:

In which of the following cities have you shopped for clothing during the last year?

• San Francisco • Sacramento • San José • Los Angeles • Other _________

Please list the clothing stores where you have shopped during the last two months.

field editing

Preliminary editing by a field supervisor on the same day as the interview to catch technical omissions, check legibility of handwriting, and clarify responses that are logically or conceptually inconsistent.

in-house editing

A rigorous editing job performed by a centralized office staff.

‘‘Excellence is to do a common thing in an uncommon way.’’—BOOKER T. WASHIngTOn

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 461

462

Suppose a respondent checks Sacramento and San Francisco to the first question. If the same respondent lists a store that has a location only in Los Angeles in the second question, an error is indicated. Either the respondent failed to list Los Angeles in the first question or listed an errone ous store in the second question. These answers are obviously inconsistent, but how exactly should they be dealt with?

Taking Action When Response Is Obviously an Error What should the editor do? If solid evidence points to the fact that the respondent simply failed to check Los Angeles, then the response to the first question can be changed to indicate that the per son shopped in that city as well. Since Los Angeles is not listed next to Sacramento or San Francisco, it is unlikely that the respondent checked the wrong city inadvertently. Perhaps the question about the stores triggered a memory that did not come to the respondent when checking off the cities. This seems quite possible, and if another question can also point strongly to the fact that the respon dent actually shopped at the store in Los Angeles, then the change should be made.

However, perhaps the respondent placed an Internet order with the store in Los Angeles and sim ply did not physically shop in the store. If other evidence suggests this possibility, then the researcher should not make an adjustment to the first question. For example, a later question may have the respondent list any clothing orders placed via mail order (or by telephone or Internet order).

Responses should be logically consistent, but the researcher should not jump to the conclusion that a change should be made at the first site of an inconsistency. A change should only be made when multiple pieces of evidence exist that some response is in error and when the likely true response is obvious.

Many surveys use filter or “skip” questions that direct a respondent to a specific set of ques tions depending on how the filter question is answered. Common filter questions involve age, sex, home ownership, or product usage. A survey might involve different questions for a homeowner than for someone who does not own a home. A data record may sometimes contain data on

Data integrity is essential to successful research and decision making. Sometimes, this is a question of ethics. Whereas data integrity can suffer when an interviewer or coder simply makes up data, other things can occur that limit data integrity. For instance, data with a large portion of nonresponse has lower integrity than data without so much missing data. How- ever, if respondents have truly left questions blank, the editor should certainly not just “make up” responses.

Data integrity can also suffer simply because the data are edited or coded poorly. For example, the data coder must be aware that data may be used by other downstream users. Therefore, consistent coding should exist. For example, if a coder sometimes uses 1 for women and 2 for men, and then on another data set uses 0 for men and 1 for women, the possibil- ity exists that analyses using these categories will be confused. Who exactly are the men and who are the women? This is particularly true if the coder does not enter value labels for the variable.

Consider how important consistent coding is for com- panies that share or sell secondary data. Occupations need a common coding just as do product classes, industries, and

numerous other potential data values. Fortunately, industries have standard codes such as nAICS (north American Industrial Classification System) and SIC ( Standardized Industrial Classification) codes. Some professional coders have adopted postal service guidelines for coding things like states and addresses. A search of the U.S. Post Office website should come to a page with these guidelines (http://pe.usps.gov). Without a standardized approach, analysts may never be quite sure what they are looking at from one data set to another. Thus, research firms need to carefully maintain information cod- ing systems that help maximize data integrity.

Sources: Dubberly, Hugh, “The Information Loop,” CIOInsight 43 (September 2004), 55–61; Shonerd, René, “Data Integrity Rules,” Association Management 55, no. 9 (2003), 14.

Do You Have Integrity?

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variables that the respondent should never have been asked. For example, if someone indicated that he or she did not own a home, yet responses for the home questions are provided, a problem is indicated. The editor may check other responses to make sure that the screening question was answered accurately. For instance, if the respondent left the question about home value unan swered, then the editor will be confident that the person truly does not own a home. In cases like this, the editor should adjust these answers by considering all answers to the irrelevant questions as “no response” or “not applicable.”

Editing Technology Today, computer routines can check for inconsistencies automatically. Thus, for electronic ques tionnaires, rules can be entered that prevent inconsistent responses from ever being stored in the file used for data analysis. These rules should represent the conservative judgment of a trained data analyst. Some online survey services can assist in providing this service. In fact, the rules can even be preprogrammed to prevent many inconsistent responses. Thus, if a person who is 28 indicates that he or she has lived in the same house for 30 years, a popup window can appear requiring the respondent to go back and fix an earlier incorrect response. Electronic questionnaires can also prevent a respondent from being directed to the wrong set of questions based on a screening ques tion response.

Editing for Completeness In some cases the respondent may have answered only the second portion of a twopart question. The following question creates a situation in which an inhouse editor may have to adjust answers for completeness:

Does your organization have more than one computer network server? Yes No

If yes, how many? _______

If the respondent checked neither yes nor no, but indicated three computer servers, the editor should change the first response to a “Yes” as long as other information doesn’t indicate otherwise. Here again, a computerized questionnaire may either not allow a response to the “how many” question if someone checked yes or require the respondent to go back to the previous question once he or she tries to enter a number for the “how many” question.

Item nonresponse is the technical term for an unanswered question on an otherwise complete questionnaire. Missing data results from item nonresponse. Specific decision rules for handling this problem should be clearly outlined in the editor’s instructions. In many situations the decision rule is to do nothing with the missing data and simply leave the item blank. However, when the rela tionship between two questions is important, such as that between a question about job satisfaction and one’s pay, the editor may be tempted to insert a plug value. The decision rule may be to plug in an average or neutral value in each instance of missing data. Several choices are available:

1. Leave the response blank. Because the question is so important, the risk of creating error by plugging a value is too great.

2. Plug in alternate choices for missing data (“yes” the first time, “no” the second time, “yes” the third time, and so forth).

3. Randomly select an answer. The editor may flip a coin with heads for “yes” and tails for “no.” 4. The editor can impute a missing value based on the respondent’s choices to other questions.

Many different techniques exist for imputing data. Some involve complex statistical estimation approaches that use the available information to forecast a best guess for the missing response.3

Option 1 above is usually the best approach, unless a response for that particular respondent is crucial (which would rarely be the case). Option 4 could also be a good option if the response is important or if the effective sample size would be too small if all missing responses are deleted. As long as the researcher is confident that the imputation methods are providing good guesses, these methods may allow a response to this item to be salvaged.

item nonresponse

The technical term for an unanswered question on an otherwise complete questionnaire resulting in missing data.

plug value

An answer that an editor “plugs in” to replace blanks or missing values so as to permit data analysis; choice of value is based on a predetermined decision rule.

impute

To fill in a missing data point through the use of a statistical algorithm that provides a best guess for the missing response based on available information.

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 463

The editor must decide whether an entire questionnaire is usable. When a questionnaire has too many missing answers, it may not be suitable for the planned data analysis. While no exact answer exists for this question, a questionnaire with a quarter of the responses or more missing is certainly suspect. In such a situation the editor can record that a particular incomplete question naire has been dropped from the sample.

The issue of item nonresponse was a far larger concern in the past, when many statistical software programs required complete data for an analysis to take place. Some statistical routines may require that an entire sampling unit be eliminated from analysis if even a single response is missing (listwise deletion). Today, most statistical programs accommodate an occasional missing response through the use of pairwise deletion. Pairwise deletion means the data that the respon dent did provide can still be used in statistical analysis. As a result, pairwise deletion produces a larger effective sample size than listwise deletion.

Editing Questions Answered Out of Order Another task an editor may face is rearranging the answers given to openended questions such as may occur in a focus group interview. The respondent may have provided the answer to a subse quent question in his or her comments to an earlier openended question. Because the respondent already had clearly identified the answer, the interviewer may not have asked the subsequent ques tion, wishing to avoid hearing “I already answered that earlier” and to maintain interview rapport. If the editor is asked to list answers to all questions in a specific order, the editor may move certain answers to the section related to the skipped question.

Facilitating the Coding Process While all of the previously described editing activities will help coders, several editing procedures are designed specifically to simplify the coding process. For example, the editor should check writ ten responses for any stray marks. Respondents are often asked to circle responses. Sometimes, a respondent may accidentally draw a circle that overlaps two numbers. For example, the circle may include both 3 and 4. The editor may be able to decide which number is the most accurate response and indicate that on the form. Occasionally, a respondent may do this to indicate indeci sion between the 3 and the 4. Again, if the editor sees that the circle is carefully drawn to include both responses, he or she may indicate a 3.5 on the form. Such ambiguity is impossible with an electronic questionnaire.

Editing and Tabulating “Don’t Know” Answers In many situations, respondents answer “don’t know.” On the surface, this response seems to indi cate unfamiliarity with the subject matter at question. A legitimate “don’t know” response is the same as “no opinion.” However, there may be reasons for this response other than the legitimate “don’t know.” A reluctant “don’t know” is given when the respondent simply does not want to answer a question. For example, asking an individual about family income may elicit a “don’t know” answer meaning, “This is personal, and I really do not want to answer the question.” If the individual does not understand the question, he or she may give a confused “I don’t know” answer.

In some situations the editor can separate the legitimate “don’t knows” (“no opinion”) from the other “don’t knows.” The editor may try to identify the meaning of the “don’t know” answer from other data provided on the questionnaire. For instance, the value of a home could be derived from knowledge of the zip code and the average value of homes within that area.

In structured questionnaires, the researcher has to decide whether to provide the respondent with a “don’t know” or “no opinion” option. If neither of these is offered, the respondents may simply choose not to answer when they honestly don’t know how or don’t want to respond to a question. A computerized questionnaire can be set up to require a response to every question. Here, if a “no opinion” or “don’t know” opinion is not made available, the result is a forced choice design. The advantages and disadvantages of forced choice questioning were discussed in Chapter 14.

464 • PART SIX Data Analysis and Presentation

Pitfalls of Editing Subjectivity can enter into the editing process. Data editors should be intelligent, experienced, and objective. A systematic procedure for assessing the questionnaires should be developed by the research analyst so that the editor has clearly defined decision rules to follow. Any inferences such as imputing missing values should be done in a manner that limits the chance for the data editor’s subjectivity to influence the response. A key issue here is consistency. Data editors should treat all missing cases the same, not selectively based on how much they like the responses they are seeing. In other words, if the decision is made to delete any questionnaires with more than 10 percent missing data, that rule should be followed for every case. The editor should not decide to keep some surveys with more than 10 percent missing data because the information appears to be positive.

Pretesting Edit Editing questionnaires during the pretest stage can prove very valuable. For example, if respondent answers to openended questions were longer than anticipated, everyone will benefit from a change to larger spaces for the answers. Answers will be more legible because the writers have enough space, answers will be more complete, and answers will be verbatim rather than summarized. Exam ining answers to pretests may also identify poor instructions or inappropriate question wording on the questionnaire.

Coding Editing may be differentiated from coding, which is the assignment of numerical scores or classify ing symbols to previously edited data. Careful editing makes the coding job easier. Codes are meant to represent the meaning in the data.

Assigning numerical symbols permits the transfer of data from questionnaires or interview forms to a computer. Codes often, but not always, are numerical symbols. However, they are more broadly defined as rules for interpreting, classifying, and recording data. In qualitative research, numbers are seldom used for codes.

Coding Qualitative Responses Unstructured Qualitative Responses (Long Interviews) Qualitative coding was introduced in Chapter 7. In qualitative research, the codes are usually words or phrases that represent themes. Exhibit 19.2 shows a hermeneutic unit in which a qualitative researcher is applying a code to a text describing in detail a respondent’s reactions to several differ ent glasses of wine. The researcher is trying to understand in detail what defines the wine drinking experience. In this case, coding is facilitated by the use of qualitative software.

After reading through the text several times, and applying a wordcounting routine, the researcher realizes that appearance, the nose (aroma), and guessing (trying to guess what the wine will be like or what type of wine is in the glass) are important themes. A code is assigned to these categories. Similarly, other codes are assigned as shown in the code manager window. The density column shows how often a code is applied. After considerable thought and questioning of the experience, the researcher builds a network, or grounded theory, that suggests how a wine may come to be associated with feelings of romance. This theory is shown in the network view. The reader interested in learning more about using software to help with qualitative coding should refer to the software sources provided in Chapter 7.

Structured Qualitative Responses Qualitative responses to structured questions such as “yes” or “no” can be stored in a data file with letters such as “Y” or “N.” Alternatively, they can be represented with numbers, one each to

coding

The process of assigning a numerical score or other character symbol to previously edited data.

codes

Rules for interpreting, classifying, and recording data in the coding process; also, the actual numerical or other character symbols assigned to raw data.

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 465

represent the respective category. So, the number 1 can be used to represent “yes” and 2 can be used to represent “no.” Since this represents a nominal numbering system, the actual numbers used are arbitrary. Even though the codes are numeric, the variable is classificatory, simply separating the positive from the negative responses.

The research may consider adopting dummy coding for dichotomous responses like yes or no. Dummy coding assigns a 0 to one category and a 1 to the other. So, for yes/no responses, a 0 could be “no” and a 1 would be “yes.” Similarly, a 0 could represent a female respondent and a 1 would be a male respondent. There are frequently benefits in the analytical stage of using numeric dummy codes as they provide the researcher with more flexibility in statistical techniques.

Dummy coding can be used when more than two categories exist, but because a dummy vari able can only represent two categories, multiple dummy variables are needed to represent a single qualitative response that can take on more than two categories. In fact, the rule is that if k is the number of categories for a qualitative variable, k – 1 dummy variables are needed to represent the variable.

Data File Terminology Once structured qualitative responses are coded, they are stored in an electronic data file. Here, both the qualitative responses and quantitative responses are likely stored for every respondent involved in a survey or interview. A terminology exists that helps describe this process and the file that results.

Some of the terminology seems strange these days. For instance, what does a “card” have to do with a simple computer file? Most of the terminol ogy describing files goes back to the early days of computers. In those days, data and the computer programs that produced results were stored on actual computer cards. Certainly readers of this book will

dummy coding

numeric 0 or 1 coding where each number represents an alternate response such as

“female” or “male.”

EXHIBIT 19.2

Coding Qualitative Data with Words

Dummy coding is a simple (dummy-proof) way to represent classification variables.

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466 • PART SIX Data Analysis and Presentation

no longer have to use physical cards to store data, as much easier and more economical ways exist. Nonetheless, the terminology from the early days remains.

Researchers organize coded data into cards, fields, records, and files. Cards are the collection of records that make up a file. A field is a collection of characters (a character is a single number, letter, or special symbol such as a question mark) that represents a single piece of data, usually a variable. Some variables may require a large field, particularly for text data; other variables may require a field of only one character. Text variables are represented by string characters, which is computer terminology for a series of alphabetic characters (nonnumeric characters) that may form a word. String characters often contain long fields of eight or more characters. In contrast, a dummy variable is a numeric variable that needs only one character to form a field. Generally, a field forms the columns of a data set.

A record is a collection of related fields. A record was the way a single, complete computer card was represented. Researchers may use the term record to refer to one respondent’s data. Generally, a record forms the rows of a data set. A data file is a collection of related records that make up a data set.

Exhibit 19.3 shows the SPSS variable view used to describe the data in a file. Most of the head ings are straightforward, beginning with the name of the variable, the type of variable (numeric or string), the size, the label, and so forth. Notice toward the bottom that the variable country is a string variable. The values for this variable are words that correspond to the country from which the specific record originates (Australia, Brazil, Canada, France, and so on). All of the remaining variables are numeric. Often a researcher will create a second, numeric variable to represent the string variable (for example, assigning 1 to Australia, 2 to Brazil, 3 to Canada, 4 to France and so on) to assist with later analysis.

The coder will sometimes like to associate a label with specific values of a numeric variable. Exhibit 19.3 shows the Value Labels dialog box that opens when the Values column is clicked. Notice that the variable dummy contains an entry in this column. Value labels are extremely use ful and allow a word or short phrase to be associated with a numeric coding. In this case, the value label helps describe whether or not someone has an MBA degree. The labels are matched to the numeric code as follows:

■ If dummy = 0, the value label is “no degree.” ■ If dummy = 1, the value label is “MBA.”

field

A collection of characters that represents a single type of data—usually a variable.

string characters

Computer terminology to represent formatting a variable using a series of alphabetic char- acters (nonnumeric characters) that may form a word.

record

A collection of related fields that represents the responses from one sampling unit.

data file

The way a data set is stored electronically in spreadsheet-like form in which the rows represent sampling units and the columns represent variables.

value labels

Unique labels assigned to each possible numeric code for a response.

EXHIBIT 19.3

Data Storage Terminology in SPSS

By clicking in the dummy row and Values column, the Value Labels dialog box appears, which allows one to set each value to a text label as shown.

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CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 467

EXHIBIT 19.4

A Data File Stored in SPSS

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The analysts will no doubt appreciate the coder’s value labeling. Now, when frequencies or other statistical output are created for this variable, the value label will appear instead of simply a number. The advantage is that the analyst will not have to remember what coding was used. In other words, he or she won’t have to remember that a 1 meant an MBA. Other statistical programs accommodate value labels in similarly easy fashions. With SAS, the coder could create a format statement as follows:

proc format; value labels

0 = ‘no degree’ 1 = ‘mba’;

data chap19; input dummy perf sales; format dummy labels.;

This sequence reads three variables: dummy, perf (performance), and sales. Just as in the SPSS example, the sequence assigns the label “no degree” to a value of 0 for dummy and a label of “mba” to a value of 1 for dummy.

The Data File Data are generally stored in a matrix that resembles a common spreadsheet file. A data file stores the data from a research project and is typically represented in a rectangular arrangement (matrix) of data in rows and columns. Typically, each row represents a respondent’s scores on each variable and each row represents a variable for which there is a value for every respondent. Exhibit 19.4

468 • PART SIX Data Analysis and Presentation

469

RESEARCH SNaPSHOt

What is a petabyte? For those of you familiar with disk stor- age measured in gigabytes (gB), a petabyte is 1,000,000 gBs. Who could possibly need such a large data system? not surpris- ingly, the largest retailer in the world—Walmart. With over 800 million transactions tied to over 30 million customers each day, the data coding and analysis needs for such a system are clear.

A reliable and flexible data system design is crucial to meet Walmart’s substantial data requirements. Whether it is sup- pliers who wish to view product movement and sales in real time, or executives who are interested in business intelligence or scenario planning, the data design aspect of Walmart’s data warehouse is the key to its success. Because virtually all of the transactions are processed in real time, data integrity and error checking are success factors valued by Walmart executives.

Over time, as the demands for richer, more robust, and timely data analyses increase, Walmart appears to have made the investments needed to grow their data warehouse into the future. In the future, there are even plans to have data marts— smaller, subject-specific data systems that can handle the needs of a particular business area.

Source: Hayes Weier, Mary, “Hewlett-Packard Data Warehouse Lands in Wal-Mart’s Shopping Cart,” Intelligent Enterprise (August 4, 2007), http://www.informationweek.com/news/201203024, accessed August 18, 2011.

Building a Multipetabyte Data System

illustrates a data matrix corresponding to the variable view in Exhibit 19.3. In this case, data exist for 40 respondents. A spreadsheet like Excel is a good way to store a data file, as statistical pro grams like SPSS, SAS, and others can work easily with an Excel spreadsheet. The careful construc tion of data files is critical to business research. For some businesses, their data is coded and stored in data warehouses. These data warehouses, where customer, supplier, and organization level data are coded, stored, and analyzed, are often the lifeblood of the company. The Research Snapshot “Building a Multipetabyte Data System” highlights the size of such data storage systems and the investments companies make in them.

Each column in Exhibit 19.4 represents a particular variable. The first five columns are related to the salesperson’s performance (performance evaluation, sales performance, margin/profit, train ing, and work experience). The next two columns are ratio variables representing hours worked and labor costs, respectively. The next seven columns are variables taken from survey questions. Six of these are semantic differential scales that are scored from 1 to 10 based on the respondent’s opinion. The next variable is a variable indicating likelihood of quitting (turnover), also using a 10point scale. Finally, the last two variables, dummy and country, represent whether or not the respondent has an MBA and in which country he or she works, respectively.

Code Construction There are two basic rules for code construction. First, the coding categories should be totally exhaustive, meaning that a coding category should exist for all possible responses. With a categori cal variable such as sex, making categories exhaustive is not a problem. However, trouble may arise when the response represents a small number of subjects or when responses might be categorized into a class not typically found. For example, when questioned about automobile ownership, a car collector might mention that he drives a Lotus Esprit. This may present a problem if separate cat egories were developed for all common makes of cars, but they did not include Lotus. Solving this problem frequently requires inclusion of an “other” code category for rarely mentioned responses to ensure that the categories are allinclusive. For example, the U.S. Census codes household size as 1, 2, 3, 4, 5, 6, and 7 or more. The 7 or more category may be unusual (just over 1 percent), but assures all subjects of a place in a category.

Missing data should also be represented with a code. In the “good old days” of computer cards, a numeric value that was out of the range of actual values (such as 9 or 99) was used to represent missing data and maintain the correct space in columns. While this might still be a useful technique for entering data, most software will understand that either a period or a blank response represents “system” missing data.

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Second, the coding categories should be mutually exclusive (independent). This means that there should be no overlap among the categories to ensure that a subject or response can be placed in only one category.

Precoding Fixed-Alternative Questions When a questionnaire is highly structured, the categories may be precoded before the data are collected. Exhibit 19.5 presents a questionnaire for which the precoded response categories were determined before the start of data collection. The codes in the data file will correspond to the small numbers beside each choice option. In most instances, the codes will not actually appear on the questionnaire.

The questionnaire in Exhibit 19.5 shows several categorical questions classifying individuals’ scores. Question 29 has three possible answers, and they are precoded 1, 2, and 3. Question 30 asks a person to respond “yes” (1) or “no” (2) to the question “Are you the head of the household?” Question 31 provides nine categories of household income. Once again, technology is making things easier and much of this type of coding is often automated. For users of webbased survey services, all that one need do is submit a questionnaire and in return he or she will receive a coded data file in the software of his or her choice.

Telephone interviews also use a coding form to enter survey information. The partial questionnaire in Exhibit 19.6 shows a precoded format for the screening portion of a telephone interview. In this situation the interviewer circles the coded numerical score as the answer to the question.

Precoding can be used if the researcher knows what answer categories exist before data col lection occurs. Once the questionnaire has been designed and the structured (or closedform)

EXHIBIT 19.5

Precoding Fixed-Alternative Responses

29. Do you—or does anyone else in your immediate household—belong to a labor union?

1 Yes, I personally belong to a labor union. 2 Yes, another member of my household belongs to a labor union. 3 No, no one in my household belongs to a labor union.

30. Are you the head of the household—that is, the person whose income is the chief source of support of the household?

1 Yes 2 No

31. Would you please check the appropriate combined yearly income (before income taxes and any other payroll deductions) from all sources of all those in your immediate household? (Please include income from salaries, investments, dividends, rents, royalties, bonuses, commissions, etc.) Please remember that your individual answers will not be divulged.

1 Less than $25,000 2 $25,000–$49,999 3 $50,000–$74,999

4 $75,000–$99,999 5 $100,000–$124,999 6 $125,000–$149,499

7 $150,000–$174,999 8 $175,000–$199,999 9 $200,000 or more

32. a. Do you personally own corporate stocks? 1 Yes 2 No b. Do you own stocks in the corporation for which you work? Do you own them in a corporation for which you do not work? (Please check as many as apply.)

Own STOCK in:

1 Company for which I work 2 Other company

THANK YOU VERY MUCH FOR YOUR COOPERATION

If you would like to make any comments on any of the subjects covered in this study, please use the space below: _________________________________________________________________________________________________________

_________________________________________________________________________________________________________

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470 • PART SIX Data Analysis and Presentation

answers identified, coding then becomes routine. In some cases, predetermined responses are based on standardized classification schemes. A coding framework that standardizes occupation follows:

What is your occupation? ( PROBE: What kind of work is that?) 01 Professional, technical, and kindred workers 02 Farmers 03 Managers, officials, and proprietors 04 Clerical 05 Sales workers 06 Craftsmen, foremen, and kindred workers 07 Operatives and kindred workers 08 Service workers

09 Laborers, except farm and mine 10 Retired, widow, widower 11 Student 12 Unemployed, on relief, laid off 13 Homemaker 14 Other (specify) 99 No occupation given

EXHIBIT 19.6

Precoded Format for Telephone Interview

Study #45641 For office use only

Travel (Telephone Screening) Respondent #______

City:

Chicago

Gary

Ft. Wayne

Bloomington

Hello, I’m ________________ from____________, a national survey research company. We are conducting a study and would like to ask you a few questions.

A. Before we begin, do you—or any member of your family—work for … 1 A travel agency 2 An advertising agency 3 A marketing research company (If “yes” to any of the above, terminate and tally on contact sheet)

B. By the way, have you been interviewed as part of a survey research study within the past month? 1 Yes—(Terminate and tally on contact sheet) 2 No—(Continue) 1. Have you yourself made any trips of over 100 miles within the continental 48 states in the past 3 months? 1 Yes 2 No—(Skip to Question 10)

2. Was the trip for business reasons (paid for by your firm), vacation, or personal reasons?

Last Trip Second Last Trip Other Trips

Business 1 1 1

Vacation 2 2 2

Personal (excluding a vacation)

3 3 3

Computerassisted telephone interviewing (CATI) requires precoding. Changing the cod ing framework after the interviewing process has begun is extremely difficult because it requires changes in the computer programs. In any event, coding closedended structured responses is a straightforward process of entering the code into the data file.

More on Coding Open-Ended Questions Surveys that are largely structured will sometimes contain some semistructured openended ques tions. These questions may be exploratory or they may be potential followups to structured questions. The purpose of coding such questions is to reduce the large number of individual

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CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 471

responses to a few general categories of answers that can be assigned numerical codes for further analysis.

Similar answers should be placed in a general category and assigned the same code much as the codes are assigned in the qualitative sample involving wine con sumption above. In this case, however, a small amount of data may be obtained from a large number of respondents, whereas in the hermeneutic unit above, a large amount of data is obtained from one or a small number of respondents. For example, a consumer survey about frozen food also asked why a new microwaveable product would not be purchased:

■ We don’t buy frozen food very often. ■ I like to prepare fresh food, not use frozen. ■ Frozen foods are not as tasty as fresh foods. ■ I don’t like that freezer taste from frozen foods.

All of these answers could be categorized under “dislike frozen foods” and assigned the code 1. Code construction in these situations reflects the judgment of the researcher.

A major objective in the codebuilding process is to accurately transfer the meanings from written responses to numeric codes. Experienced researchers rec ognize that the key idea in this process is that code building is based on thoughts, not just words. The end result of code building should be a list, in an abbreviated and orderly form, of all comments and thoughts given in answers to the questions.

Developing an appropriate code from the respondent’s exact comments is somewhat of an art. Researchers generally perform a test tabulation to identify verbatim responses from approxi mately 20 percent of the completed questionnaires and then establish coding categories reflect ing the judgment of the person constructing the codes. Test tabulation is the tallying of a small sample of the total number of replies to a particular question. The purpose is to pre liminarily identify the stability and distribution of answers that will determine a coding scheme. Exhibit 19.7 illustrates openended responses and preliminary openended codes generated for the question, “What could our store do to better serve your equipment rental needs?” During the coding procedure, the respondent’s opinions are divided into mutually exclusive thought pat terns. These separate divisions may consist of a single word, a phrase, or a number of phrases, but in each case represent a common thought. Each separate thought is coded once. After tabulating the basic responses, the researcher must determine how many answer categories will be accept able. Typically, an “other” or “miscellaneous” code category appears to capture thoughts that are rarely expressed.

Devising the Coding Scheme A coding scheme should not be too elaborate. The coder’s task is only to summarize the data. Exhibit 19.8 shows a test tabulation of airport visitors’ responses to a question that asked for comments about the Honolulu Airport. After the first pass at devising the coding scheme, the researcher must decide whether to revise it and whether the codes are appropriate for answering management’s questions. A preliminary scheme with too many categories can always be collapsed or reduced later in the analysis. If initial coding is too abstract and only a few categories are estab lished, revising the codes to more concrete statements will be difficult unless the raw data were recorded.

In the Honolulu Airport example, the preliminary tabulation contained too many codes, but it could be reduced to a smaller number of categories. For example, the heading “Friendly/ attractive personnel” could include the responses “Friendly staff/people,” “Polite VIP/friendly/ helpful,” and “Cute VIP.” Experienced coders group answers under generalized headings that are pertinent to the research question. Individual coders should give the same code to similar responses. The categories should be sufficiently unambiguous that coders will not classify items in different ways.

test tabulation

Tallying of a small sample of the total number of replies to a

particular question in order to construct coding categories.

The coder will try to classify all of the comments from the interviewer in a code that facilitates analysis.

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EXHIBIT 19.7

Coding Open-Ended Questions about Equipment Rental

Customer Verbatim Comment Code

The customer stated that he would like an outside sales representative to visit periodically. Sales rep

The customer stated she would like any “specials” the store is running to be faxed to her office. Pricing

The customer stated that the store could lower prices to improve. Pricing

The customer just opened an account with the store and will be using it soon. Other

The customer stated that the store could improve by giving the customer preferred rates on all rentals.

Pricing

The customer stated that the store could improve by having a more convenient location. Location

The customer stated that the store could send an outside sales representative to his business. Sales rep

The customer stated that the store could improve by having a closer location. Location

The customer stated that the store could improve by having a more convenient location. Location

The customer stated that he contacted the store but the sales rep did not call back. Sales rep

The customer stated that he has never talked to a sales representative. Sales rep

The customer stated that she would like to see more store locations near her business. Location

The customer has not heard from a sales representative from the store. Sales rep

The customer stated that he disliked the store requiring him to have insurance to rent equipment. Other

The customer said the store does not carry the equipment he needs. Equipment availability

The customer stated that he would like a sales rep to provide information about the store’s products.

Sales rep

The customer stated that he would like to be contacted by a sales rep. Sales rep

The customer stated that she would like to see lower prices at the store. Pricing

The customer stated that she felt the entire process of renting equipment at the store was far too time-consuming.

Other

The customer stated that the store needed broader variety of equipment. Equipment availability

The customer stated that the store should lower their prices. Pricing

The customer stated that the store could send a sales representative with information to their store location.

Sales rep

The customer stated that the store could improve by having a closer location. Location

The customer stated the nearest location is 50 miles away from him. Location

The customer stated that the store could improve by having a closer location. Location

The customer stated that she would like to be contacted by a sales representative from the store. Sales rep

The customer stated that the store did not always have the equipment he needed. Equipment availability

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CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 473

Number

Prices high: restaurant/coffee shop/snack bar 90

Dirty—filthy—smelly restrooms/airport 65

Very good/good/excellent/great 59

Need air-conditioning 52

Nice/beautiful 45

Gift shops expensive 32

Too warm/too hot 31

Friendly staff/people 25

Airport is awful/bad 23

Long walk between terminal/gates 21

Clean airport 17

Employees rude/unfriendly/poor attitude 16

More signs/maps in lobby/streets 16

Like it 15

Love gardens 11

Need video games/arcade 10

More change machines/different locations 8

More padded benches/comfortable waiting area 8

More security personnel including HPD 8

Replace shuttle with moving walkways 8

Complaint: flight delay 7

Cool place 7

Crowded 7

Provide free carts for carry-on bags 7

Baggage storage inconvenient/need in different locations 6

Floor plan confusing 6

Mailbox locations not clear/more needed 6

More restaurants and coffee shops/more variety 6

Need a place to nap 6

Polite VIP/friendly/helpful 6

Poor help in gift shops/rude/unfriendly 6

Slow baggage delivery/service 6

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EXHIBIT 19.8

Open-Ended Responses to a Survey about the Honolulu Airport

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Coding openended questions is a very complex issue. Certainly, this task cannot be mastered simply from reading this chapter. However, the reader should have a feel for the art of coding responses into similar categories. With practice, and by using multiple coders so that consistency can be examined, one can become skilled at this task.

Code Book A code book gives each variable in the study and its location in the data matrix. In essence, the code book provides a quick summary that is particularly useful when a data file becomes very large. Exhibit 19.9 illustrates a portion of a code book from the telephone interview illustrated in Exhibit 19.6. Notice that the first few fields record the study number, city, and other information used for identification purposes. Researchers commonly identify individual respondents by giving each an identification number or questionnaire number. When each interview is identified with a number entered into each computer record, errors discovered in the tabulation process can be checked on the questionnaire to verify the answer.

Editing and Coding Combined Frequently the person coding the questionnaire performs certain editing functions, such as trans lating an occupational title provided by the respondent into a code for socioeconomic status. A question that asks for a description of the job or business often is used to ensure that there will be no problem in classifying the responses. For example, respondents who indicate “salesperson” as their occupation might write their job description as “selling shoes in a shoe store” or “selling IBM supercomputers to the defense department.” Generally, coders are instructed to perform this type of editing function, seeking the help of a tabulation supervisor if questions arise.

code book

A book that identifies each variable in a study and gives the variable’s description, code name, and position in the data matrix.

EXHIBIT 19.8 (Continued)

Open-Ended Responses to a Survey about the Honolulu Airport

Number

Very efficient/organized 6

Excellent food 5

Install chilled water drinking fountains 5

Love Hawaii 5

More TV sets 5

Noisy 5

People at sundries/camera rude 5

Shuttle drivers rude 5

Something to do for passengers with long waits 5

Airport too spread out 4

Better information for departing/arriving flights 4

Better parking for employees 4

Better shuttle service needed 4

Cute VIP 4

CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 475

476

Computerized Survey Data Processing While a very simple study may use hand tabulation, virtually all business research studies with large sample sizes use a computer for data processing. The process of transferring data from a research project, such as answers to a survey questionnaire, to computers is referred to as data entry. Several alternative means exist for entering data into a computer. In studies involving highly structured paper and pencil questionnaires, an optical scanning system may be used to read material directly into the computer’s memory from marksensed questionnaires, similar to the form used for mul tiple guess exams. As seen in the Research Snapshot “Coding Data ‘OntheGo’,” even mobile phone technology is now being used to aid data processing.

In a research study using computerassisted telephone interviewing or a selfadministered Internet questionnaire, responses are automatically stored and tabulated as they are collected. Direct data capture substantially reduces clerical errors that occur during the editing and coding process. If researchers have security concerns, the data collected in an Internet survey should be encrypted and protected behind a firewall.

As the opening vignette shows, collecting data using computer technology is an evergrowing phenomenon in business research. When data are not optically scanned or directly entered into the computer the moment they are collected, data processing begins with keyboarding. A data entry process transfers coded data from the questionnaires or coding sheets onto a hard drive. As in every stage of the research process, there is some concern about whether the data entry job has been done correctly. Data entry workers, like anyone else, may make errors. To ensure 100 percent accuracy in transferring the codes, the job should be verified by a second data entry worker. If an error has been made, the verifier corrects the data entry. This process of verifying the data is never performed by the same person who entered the original data. A person who misread the coded questionnaire during the keyboarding opera tion might make the same mistake during the verifying process, and the mistake might go undetected.

Error Checking The final stage in the coding process is error checking and verification, or data cleaning, to check for wild codes. Computer software can examine the entered data and identify coded values that lie outside the range of acceptable answers. For example, if “sex” is coded 1 for “male” and 2 for “female” and a 3 code is found, a mistake obviously has occurred and an adjustment must be made. Similarly, if a 10point scale is used for respondent answers, the researcher must check to insure that all coded data falls within the 1 to 10 range.

data entry

The activity of transferring data from a research project to

computers.

optical scanning system

A data processing input device that reads material directly from mark-sensed

questionnaires.

Collecting business-critical data takes time. Often data collec- tion specialists and researchers must stop their other organiza- tional responsibilities to code a data value into a spreadsheet or business database. This can take valuable and productive time away from their other responsibilities. For example, a ware- house worker operating a forklift might need to code where a particular pallet of product is in a warehouse, after they have moved it from one section to another. This would require that they stop and code into a computer the new pallet location. Vangard Voice Systems aims to change this in a big way.

Vangard’s AccuSpeech and Mobile Voice Platform (MVP) is a mobile enterprise system that uses cellular phone technol- ogy and proprietary voice recognition software to execute voice commands to store, code, or recode data hands-free. For

forklift drivers, data can be entered through voice commands, thus allowing them to continue to work within their warehouse without any productivity downtime. In many ways, taking advantage of the portability of cellular technology is a natural fit for supply chain companies. Using Vangard’s AccuSpeech, data can truly be collected and coded “on-the-go”!

Source: http://www.vangardvoice.com, Vangard Voice Systems, accessed August 18, 2011.

Coding Data “On-the-Go”

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EXHIBIT 19.9

Portion of a Code Book from a Travel Study

Study #45641 January 20__ N = 743

Question Number

Field or Column Number Description and Meaning of Code Values

— 1–5 Study number (45641)

— 6 City 1. Chicago 2. Gary 3. Ft. Wayne 4. Bloomington

— 7–9 Interview number (3 digits on upper left-hand corner of questionnaire)

A Not entered Family, work for 1. Travel agency 2. Advertising agency 3. Marketing research company

B Not entered Interviewed past month 1. Yes 2. No

1. 10 Traveled in past 3 months 1. Yes 2. No

2. 11 Purpose last trip 1. Business 2. Vacation 3. Personal

12 Purpose second last trip 1. Business 2. Vacation 3. Personal

13 Purpose other trips 1. Business 2. Vacation 3. Personal

SUMMARY

1. Know when a response is really an error and should be edited. Data editing is necessary before coding and storing the data file. The data editor must sometimes alter a respondent’s answer. Often, this situation arises because of inconsistent responses; that is, responses to different ques tions that contradict each other. The editor should be cautious in altering a respondent’s answer. Only when a certain response is obviously wrong and the true response is easily determined should the coder substitute a new value for the original response. Ideally, multiple pieces of evidence would suggest the original response as inaccurate and also suggest the accurate response before the respondent takes such a step. Missing data should generally be left as missing, although imputation methods exist to provide an educated guess for missing values. These imputation methods can be used when the sample size is small and the researcher needs to retain as many responses as possible.

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CHAPTER 19 Editing and Coding: Transforming Raw Data into Information • 477

2. Appreciate coding of pure qualitative research. Qualitative research such as typified by depth interviews, conversations, or other responses is coded by identifying the themes underlying some interview. The codes become a key component of a hermeneutic unit that ultimately can be linked with others to form a grounded theory. The frequency with which some thought is expressed helps to identify appropriate coding for unstructured qualitative data.

3. Understand the way data are represented in a data file. A survey provides an overview of respondents based on their answers to questions. These answers are edited, coded, and then stored in a data file. The data file is structured as a data matrix in which the rows represent respondents and the columns represent variables. Thus, a survey in which 200 respondents are asked 50 struc tured questions would result in a data matrix consisting of 200 rows and 50 columns.

4. Understand the coding of structured responses including a dummy variable approach. Structured quantitative responses are generally coded simply by marking the number corresponding to the choice selected by the respondent. Structured qualitative responses must also be coded. Dichotomous variables lend themselves well to dummy coding. With dummy coding, the two possible choices to a question are coded with a 0 for one response and a 1 for the other. Shortanswer or list questions are coded by assigning a number to all responses that seem to suggest the same theme even if different words are used.

5. Appreciate the ways that technological advances have simplified the coding pro- cess. Throughout the chapter, technological advances in data collection were mentioned. These advances have automated a great deal of data coding and reduced the chances of respondent error. For instance, some inconsistent responses can be automatically screened and the respondent can be prompted to go back and correct a response that seems inconsistent. Also, if a respondent fails to answer a question, a popup window can take that respondent back to the question and force him or her to respond in order to continue through the rest of the questionnaire.

KEY TERMS AND CONCEPTS code book, 475 codes, 465 coding, 465 data entry, 476 data file, 467 data integrity, 460 dummy coding, 466

editing, 460 field editing, 461 field, 467 impute, 463 inhouse editing, 461 item nonresponse, 463 nonrespondent error, 459

optical scanning system, 476 plug value, 463 raw data, 459 record, 467 string characters, 467 test tabulation, 472 value labels, 467

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is the purpose of editing? Provide some examples of

questions that might need editing. 2. When should the raw data from a respondent be altered by a

data editor? 3. How is data coding different from data editing? 4. A 25yearold respondent indicates that she owns her own

house in Springfield, Illinois, and it is valued at $990 million. Later in the interview, she indicates that she didn’t finish high school and that she drives a 1993 Buick Century. Should the editor consider altering any of these responses? If so, how?

5. What role might a word counter play in coding qualitative research results?

6. A survey respondent from Florida has been asked to respond as to whether or not he or she owns a boat, and if so, whether he or she stores the boat at a marina. Over two hundred respon dents are included in this sample. What suggestions do you have for coding the information provided?

7. How would a dummy variable be used to represent whether or not a respondent in a restaurant ordered dessert after their meal?

8. List at least three ways in which recent technological advances (within the last 15 years) have changed the way data are coded.

9. EtHICS A large retail company implements an employee survey that ostensibly is aimed at customer satisfaction. The survey includes a yes or no question that asks whether or not the employee has ever stolen something from the workplace. How could this data be coded? What steps could be attempted to try and ensure that the employee’s response is honest? Do you believe it is fair to ask this question? Should the employee take action against employees who have indicated that they have stolen something?

10. A researcher asks, “What do you remember about advertising for iPad 2?” A box with enough room for 100 words is pro vided in which the respondent can answer the question. The survey involves responses from 250 consumers. How should the code book for this question be structured? What problems might it present?

478 Part 6: Data Analysis and Presentation

RESEARCH ACTIVITIES 1. Design a short questionnaire with fewer than five fixedalterna

tive questions to measure student satisfaction with your college bookstore. Interview five classmates and then arrange the data base into a data matrix.

2. ’NEt The web page of the Research Triangle Institute (http:// www.rti.org) describes its research tools and methods in some detail. Click on Survey Research and Services and explore the surveys and survey tools described there. How might these methods assist in coding?

11. ’NEt Use http://www.naicscode.com to help with this response. What is the NAICS code for golf (country) clubs? What is the NAICS code for health clubs? How can these codes be useful in creating data files?

12. ’NEt Explore the advantages of computerized software such as ATLAS.ti. The website is at http://www.atlasti.com. How do you think it might assist in coding something like a depth inter view or a collage created by a respondent?

Some years ago the U.S. Department of the Interior conducted a telephone survey to help plan for

future outdoor recreation. A ninepage questionnaire concern ing participation in outdoor recreational activities and satisfaction with local facilities was administered by the Opinion Research Corporation of Princeton, New Jersey, to 4,029 respondents. The last two pages of the questionnaire appear in Case Exhibit 19.11. Assume the data will be entered into a data file in which each data entry should include the following information:

• Respondent number • State code (all 50 states)

Question Design the coding for this portion of the questionnaire. Assume that the data from previous pages of the questionnaire will fol low these data.

CASE 19.1

CASE EXHIBIT 19.1-1

Sample Page from Questionnaire

The following questions are for background purposes. 32. Do you live in an … Urban location Suburban location Rural location 33. Counting yourself, how many members of your family live

here? (If 1 on Q.33, go to Q.35) 34. How many family members are … Under 5 years _______ 5 to 11 years ________ 12 to 20 years _______ 21 to 39 years _______ 40 to 65 years _______ Over 65 years _______ 35. What is your age? (Years) 36. In school, what is the highest grade (or year) you have com

pleted? (Circle response)

Elementary school 01 02 03 04 05 06 Junior high school 07 08 High school 09 10 11 12 College 13 14 15 16 Graduate school 17 18 19 20 21

37. What is your occupation? What kind of work is that? Professional, technical, and kindred workers Farmers Managers, officials, and proprietors Clerical and kindred workers Sales workers

Craftspersons, forepersons, and kindred workers Operatives and kindred workers Service workers Laborers, except farm and mine

Retired, widow, widower Student Unemployed, on relief, laid off → Go to Q.43 Housewife

Other (specify) 38. How many hours a week do you work at your place of

employment? ___ (hours) 39. How many days of vacation do you get in a year? ___ (days) 40. Please tell me which of the following income categories most

closely describe the total family income for the year before taxes, including wages and all other income. Is it …

Under $12,000 $12,000–$20,000 $20,001–$30,000 $30,001–$50,000 $50,001–$100,000 Over $100,001 41. Sex of respondent … Male Female 42. What is the zip code at your place of employment? This concludes the interview; thank you very much for your cooperation and time.

U.S. Department of the Interior Heritage Conservation and Recreation Service

Chapter 19: Editing and Coding: Transforming Raw Data into Information 479

Shampoo 9–10 A shampoo, codenamed 9–10, was given to women for trial use.4 The respondents were asked what they liked and disliked about the product. Some sample

codes are given in Case Exhibit 19.21 and 19.22. There were two separate sets of codes: the codes in Case Exhibit

19.21 were for coding the respondents’ likes and the codes in Case Exhibit 19.22 were for coding their dislikes. The headings iden tify fields in the data matrix and the different attributes of shampoo. The specific codes are listed under each attribute. The coding instruc tions were first to look for the correct heading, and then to locate the correct comment under that heading and use that number as the code.

For example, if, in response to a “like” question, a respondent had said, “The shampoo was gentle and mild,” a coder would look in

field 10, the “gentleness” field, and find the comment “Gentle/mild/ not harsh”; then the coder would write 11 next to the comment. If, under “dislikes,” someone had said, “I would rather have a shampoo with a crême rinse,” the coder would look in field 16 for comparison to other shampoos and write 74 (“Prefer one with a crême rinse”) beside that response.

The sample questionnaires appear in Case Exhibit 19.23.

Questions 1. Code each of the three questionnaires. 2. Evaluate this coding scheme.

CASE 19.2

CASE EXHIBIT 19.2-1

Sample Codes for “like” Questions

Test No. Shampoo

Question: Likes

Field 10 Gentleness Field 11 Result on Hair

11 Gentle/mild/not harsh 21 Good for hair/helps hair

12 Wouldn’t strip hair of natural oils 22 Leaves hair manageable/no tangles/no need for crême rinse

13 Doesn’t cause/helps flyaway hair 23 Gives hair body

14 Wouldn’t dry out hair 24 Mends split ends

15 Wouldn’t make skin/scalp break out 25 Leaves hair not flyaway

16 Organic/natural 26 Leaves hair silky/smooth

17 27 Leaves hair soft

18 28 Leaves hair shiny

19 29 Hair looks/feels/good/clean

20 30

1− 2−

1+ Other gentleness 2+ Other results on hair

Field 12 Cleaning Field 13 Miscellaneous

31 Leaves no oil/keeps hair dry 41 Cheaper/economical/good price

32 It cleans well 42 Smells good/nice/clean

33 Lifts out oil/dirt/artificial conditioners 43 Hairdresser recommended

34 Don’t have to scrub as much 44 Comes in different formulas

35 No need to wash as often/keeps hair cleaner longer 45 Concentrated/use only a small amount

36 Doesn’t leave a residue on scalp 46 Good for whole family (unspecified)

37 Good lather 47

38 Good for oily hair 48

39 49

(Continued )

480 Part 6: Data Analysis and Presentation

Field 12 Cleaning Field 13 Miscellaneous

40 50

3− 4– Other miscellaneous

3+ Other cleaning 4+ Don’t know/nothing

CASE EXHIBIT 19.2-2

Sample Codes for “Dislike” Questions

Test No. Shampoo

Question: Dislikes

Field 14 Harshness Field 15 Cleaning

51 Too strong 61 Doesn’t clean well

52 Strips hair/takes too much oil out 62 Leaves a residue on scalp

53 Dries hair out 63 Poor lather

54 Skin reacts badly to it 64 Not good for oily hair

55 65

56 66

57 67

58 68

59 69

60 70

5− 6−

5+ Other harshness 6+ Other cleaning

Field 16 Comparison to Others Field 17 Miscellaneous

71 Prefer herbal/organic shampoo 81 Don’t like the name

72 Prefer medicated/dandruff shampoo 82 Too expensive

73 Same as other shampoos—doesn’t work any differently 83 Not economical for long hair

74 Prefer one with a crême rinse 84 Use what hairdresser recommends

75 Prefer another brand (unspecified) 85

76 86

77 87

78 88

79 89

80 90

7− 8− Other miscellaneous

7+ Other comparison to others 8+ Don’t know what/disliked/nothing

CASE EXHIBIT 19.2-1 (Continued)

Sample Codes for “like” Questions

(Continued )

Chapter 19: Editing and Coding: Transforming Raw Data into Information 481

CASE EXHIBIT 19.2-3

Sample Questionnaires for Shampoo 9–10 Survey

Case 19.2 (Continued )

482 Part 6: Data Analysis and Presentation

483

Basic Data Analysis Descriptive Statistics

After studying this chapter, you should be able to

1. Know what descriptive statistics are and why they are used

2. Create and interpret simple tabulation tables

3. Understand how cross-tabulations can reveal relationships

4. Perform basic data transformations

5. List different computer software products designed for descriptive statistical analysis

6. Understand a researcher’s role in interpreting the data

C H A P T E R

M ost Americans enjoy an adult beverage occasionally. But not all Americans like the same drink. Many decision makers are interested in what Americans like to drink.

Retailers need to have the correct product mix for their par- ticular customers to increase profits and customer satisfaction. Restaurants need to know what their customers like to have with the types of food they serve. Policy makers need to know what types of restrictions should be placed on what types of products to prevent underage drinking and alcohol abuse. Researchers could apply sophisticated statistics to address ques- tions related to Americans’ drinking preferences, but a lot can be learned from just counting what people are buying.

A grocery store built in 1975 in Chicago allocates 15 per- cent of their floor space to adult beverage products. Out of this 15 percent, 60 percent is allocated to beer, 25 percent to spirits, and 15 percent to wine. Since the products are not merchandised the same way (different types of shelving, aisles, and racking are needed), adjusting the floor space to change these percentages is not an easy task. Over the three-decade history of the store, the customer base has changed. Originally, stay- at-home moms buying groceries for the family best char- acterized the customer base. During the 1990s, empty-nesters, including retirees with high disposable incomes, characterized

the customer base. More recently, younger singles just starting careers have moved into the nearby neighborhoods. Should the store reconsider its adult beverage merchandising?

In 1992, American consumers showed a heavy preference toward beer. Among American adults who drank adult beverages,1

■ 47 percent drank beer ■ 21 percent drank spirits ■ 27 percent drank wine

LEARNING OUTCOMES

Chapter Vignette:

Choose Your “Poison”

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By 2005, Americans had changed their drinking preferences. At this point,

■ 36 percent drank beer ■ 21 percent drank spirits ■ 39 percent drank wine

A couple of other facts also became clear. A count of the pre- ferred beverages among American adult consumers 29 and younger showed the following preferences in 2005:

■ 48 percent drank beer ■ 32 percent drank liquor ■ 17 percent drank wine

Perhaps due to the emergence of this younger group, a 2008 study shows beer has regained the position as America’s favor- ite adult beverage:2

■ 42 percent drink beer ■ 23 percent drink spirits ■ 31 percent drink wine

Across America, grocers account for 35 percent of all beer sales, but convenience stores, where younger consum- ers tend to shop, account for 45 percent.3 If the grocery store is converting more to a convenience store, maybe a contin- ued emphasis on beer is wise. However, wine consumers are more attractive from several perspectives. Wine now ranks among the top 10 food categories in America, based on gro- cery store dollar sales volume. Forty-five percent of all wine is sold in grocery stores. What we find is that the consumer who buys wine is also more likely to buy products like prime or choice beef and imported cheeses, instead of lower quality and lower priced meat and cheese products. As a result, the average $13.44 spent on wine in a grocery store (as opposed to $11.94 on beer) is only part of the story in explaining why wine customers may be grape customers!4

What should the grocer emphasize in marketing adult bever- ages? Perhaps the research based on counting can address this decision.

Introduction Perhaps the most basic statistical analysis is descriptive analysis. Descriptive statistics can sum- marize responses from large numbers of respondents in a few simple statistics. When a sample is obtained, the sample descriptive statistics are used to make inferences about characteristics of the entire population of interest. This chapter introduces basic descriptive statistics, which are simple but powerful. This chapter also provides the foundation for Chapter 21, which will extend basic statistics into the area of univariate statistical analysis.

The Nature of Descriptive Analysis Descriptive analysis is the elementary transformation of data in a way that describes the basic char- acteristics such as central tendency, distribution, and variability. For example, consider the business researcher who takes responses from 1,000 American consumers and tabulates their favorite soft drink brand and the price they expect to pay for a six-pack of that product. The mean, median, and mode for favorite soft drink and the average price across all 1,000 consumers would be descriptive statistics that describe central tendency in three different ways. Means, medians, modes, variance, range, and standard deviation typify widely applied descriptive statistics.

Chapter 13 indicated that the level of scale measurement helps the researcher choose the most appropriate form of statistical analysis. Exhibit 20.1 shows how the level of scale measurement influences the choice of descriptive statistics. Remember that all statistics appropriate for lower- order scales (nominal and ordinal) are suitable for higher-order scales (interval and ratio), but the reverse is not true.

Consider the following data. Sample consumers were asked where they most often purchased beer. The result is a nominal variable that can be described with a frequency distribution (see the bar chart in Exhibit 20.1). Ten percent indicated they most often purchased beer in a drug store, 45 percent indicated a convenience store, 35 percent indicated a grocery store, and 7 percent indicated a specialty store. Three percent listed some “other” outlet (not shown in the bar chart).

descriptive analysis

The elementary transformation of raw data in a way

that describes the basic characteristics such as central

tendency, distribution, and variability.

484 • PART SIX Data Analysis and Presentation

485

SURVEY

THIS!

The mode is convenience store since more respondents chose this than any other category. A simi- lar distribution may have been obtained if the chart plotted the number of respondents ranking each store as their favorite type of place to purchase beer.

The bottom part of Exhibit 20.1 displays example descriptive statistics for interval and ratio variables. In this case, the chart displays results of a question asking respondents how much they typically spend on a bottle of wine purchased in a store. The mean and standard deviation are dis- played beside the chart as 11.7 and 4.5, respectively. Additionally, a frequency distribution is shown with a histogram. A histogram is a graphical way of showing a frequency distribution in which the height of a bar corresponds to the frequency of a category. Histograms are useful for any type of data, but with continuous variables (interval or ratio) the histogram is useful for providing a quick assessment of the distribution of the data. A normal distribution line is superimposed over the histogram, providing an easy comparison to see if the data are skewed or multimodal.

One item in the questionnaire asks respondents to report the career they view as most attractive. A simple way to get an understanding of this popula- tion’s career aspiration is to simply count the num- ber who rank each profession as their preferred career. Try to draw some conclusions about which job is most attractive: (1) Calculate the number of respondents who rank each profession as the most attractive (assign it a 1). Report this tabulation. (2) Do you think female and male respondents respond similarly to this item? Try to create the appropriate cross-tabulation table to show which jobs are preferred by men and women respectively.

EXHIBIT 20.1

Levels of Scale Measurement and Suggested Descriptive Statistics

Measurement Level Statistic Example

Beer Sales

0 5

10 15 20 25 30 35 40 45 50

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Drug Store Convenience Store Grocery Store Specialty

Purchase Location

25.0020.0015.0010.005.000.00 Price

Fr eq

ue nc

Mean � 11.6667 Std. Dev. � 4.54888 N � 27

Frequency Table Proportion

(Precentages) Mode

Means Standard

Deviations

Nominal

Ordinal

Interval

Ratio

histogram

A graphical way of showing a frequency distribution in which the height of a bar corresponds to the observed frequency of the category.

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Tabulation Tabulation refers to the orderly arrangement of data in a table or other summary format. When this tabulation process is done by hand, the term tallying is used. Counting the different ways respon- dents answered a question and arranging them in a simple tabular form yields a frequency table. The actual number of responses to each category is a variable’s frequency distribution. A simple tabulation of this type is sometimes called a marginal tabulation.

Simple tabulation tells the researcher how frequently each response occurs. This starting point for analysis requires the researcher to count responses or observations for each category or code assigned to a variable. A frequency table showing where consumers generally purchase beer can be computed easily. The tabular results that correspond to the chart would appear as follows:

Response Frequency Percent Cumulative Percentage

Drugstore 50 10 10

Convenience store 225 45 55

Grocery store 175 35 90

Specialty 35 7 97

Other 15 3 100

The frequency column shows the tally result or the number of respondents listing each store, respectively. The percent column shows the total percentage in each category. From this chart, we can see the most common outlet—the mode—is convenience store since more people indicated this as their top response than any other. The cumulative percentage keeps a running total, showing the percentage of respondents indicating this particular category and all preceding categories as their preferred place to purchase beer. The cumulative percentage column is not so important for nominal or interval data, but is quite useful for interval and ratio data, particularly when there are a large number of response categories.

Similarly, in the Research Snapshot you see some recent results of a study of Americans’ responses to the simple question, “Do you consider your pet to be a member of the family?”5 The idea that gender influences the types of things we do for our pets brings us to cross-tabulation.

Cross-Tabulation A frequency distribution or tabulation can address many research questions. As long as a question deals with only one categorical variable, tabulation is probably the best approach. Although fre- quency counts, percentage distributions, and averages summarize considerable information, simple tabulation may not yield the full value of the research. Cross-tabulation is the appropriate tech- nique for addressing research questions involving relationships among multiple less-than interval variables. We can think of a cross-tabulation as a combined frequency table. Cross-tabs allow the inspection and comparison of differences among groups based on nominal or ordinal categories. One key to interpreting a cross-tabulation table is comparing the observed table values with hypo- thetical values that would result from pure chance. A statistical test for this comparison is discussed in Chapter 21. Here, we focus on constructing and interpreting cross-tabs.

Exhibit 20.2 summarizes several cross-tabulations from responses to a questionnaire on how fam- ilies would respond to financial hardship or instability associated with their children. Panel A pres- ents results on whether grown children should be allowed to share a home with their parents. The cross-tab suggests this may vary with basic demographic variables. From the results, we can see that more men (24 percent) than women (22 percent) reported they consider it a “good idea.” Further, it appears that there is a slight increase when comparisons are made between very young (18–29)

tabulation

The orderly arrangement of data in a table or other

summary format showing the number of responses to each

response category; tallying.

frequency table

A table showing the different ways respondents answered a

question.

cross-tabulation

The appropriate technique for addressing research questions involving relationships among

multiple less-than interval variables; results in a combined frequency table displaying one variable in rows and another in

columns.

486 • PART SIX Data Analysis and Presentation

and middle class respondents, although this trend decreases substantially for those respondents over 60 years of age. Panel B provides another example of a cross-tabulation table. The question asks if parents should provide financial help to their children if they are having difficulties. In this case, we see some differences between men (38.6 percent strongly agree or agree) and women (33.7 percent strongly agree or agree). However, before reaching any conclusions based on this survey, one must carefully scrutinize this finding for possible extraneous variables.

Contingency Tables Exhibit 20.3 shows example cross-tabulation results using contingency tables. A contingency table is a data matrix that displays the frequency of some combination of possible responses to multiple variables. Two-way contingency tables, meaning they involve two less-than interval vari- ables, are used most often. A three-way contingency table involves three less-than interval variables. Beyond three variables, contingency tables become difficult to analyze and explain easily. For all practical purposes, a contingency table is the same as a cross-tabulation.

Two variables are depicted in the contingency table shown in panel A:

■ Row Variable: Biological Sex _____M _____F ■ Column Variable: “Do you shop at Target? YES or NO”

Several conclusions can be drawn initially by examining the row and column totals:

1. 225 men and 225 women responded, as can be seen in the row totals column. 2. Out of 450 total consumers responding, 330 consumers indicated that “yes” they do shop at

Target and 120 indicated “no,” they do not shop at Target. This can be observed in the col- umn totals at the bottom of the table. These row and column totals often are called marginals because they appear in the table’s margins.

contingency table

A data matrix that displays the frequency of some combination of possible responses to multiple variables; cross-tabulation results.

marginals

Row and column totals in a contingency table, which are shown in its margins.

EXHIBIT 20.2

Cross-Tabulation Tables from a Survey of Families Regarding Family Responses to Financial Hardships

(A) As you know, many older people share a home with their grown children. Do you think this is generally a good idea or a bad idea?

Total Gender Age

Adults Male Female 18–29 30–44 45–59 60+

Good idea 22.8% 23.9% 21.8% 21.2% 24.4% 27.4% 18.4%

Bad idea 14.2% 14.4% 14.0% 12.2% 13.1% 11.8% 18.3%

It depends 58.1% 56.5% 59.8% 61.1% 57.3% 56.4% 58.8%

Don’t know 4.8% 5.3% 4.4% 5.5% 5.2% 4.3% 4.6%

(B) Parents ought to provide financial help to their adult children when the children are having financial difficulty.

Total Gender Age

Adults Male Female 18–29 30–44 45–59 60+

Strongly Agree 5.0% 5.3% 4.6% 6.1% 4.0% 4.2% 5.7%

Agree 31.2% 33.3% 29.1% 31.6% 27.6% 29.0% 36.0%

Neither 49.9% 48.6% 51.2% 48.7% 53.6% 50.8% 46.6%

Disagree 12.1% 10.9% 13.3% 12.8% 12.6% 13.6% 9.9%

Strongly Disagree 1.9% 1.9% 1.9% .8% 2.1% 2.5% 1.8%

Source: Bibliographic Citation: Judith A. Seltzer & Suzanne M. Bianchi, National Center for Family and Marriage Research, “Familial Responses to Financial Instability, Doubling Up When Times Are Tough: Obligations to Share a Home in Response to Economic Hardship,” [United States] [Computer file] ICPSR26543-v1 (2009). Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], accessed May 20, 2010. doi:10.3886/ICPSR26543.v1.

CHAPTER 20 Basic Data Analysis: Descriptive Statistics • 487

Researchers usually are more interested in the inner cells of a contingency table. The inner cells display conditional frequencies (combinations). Using these values, we can draw some more specific conclusions:

3. Out of 330 consumers who shop at Target, 150 are male and 180 are female. 4. Alternatively, out of the 120 respondents not shopping at Target, 75 are male and 45 are

female.

This finding helps us know whether the two variables are related. If men and women equally patronized Target, we would expect that hypothetically 165 of the 330 shoppers would be male and 165 would be female. Because we have equal numbers of men and women, the 330 would be equally male and female. The hypothetical expectations (165m/165f ) are not observed. What is the implication? Target shoppers are more likely to be female than male. Notice that the same meaning could be drawn by analyzing non-Target shoppers. The Research Snapshot provides an example of the information provided by cross-tabs.

A two-way contingency table like the one shown in part A is referred to as a 2 × 2 table because it has two rows and two columns. Each variable has two levels. A two-way contingency table displaying two variables, one (the row variable) with three levels and the other with four levels, would be referred to as a 3 × 4 table. Any cross-tabulation table may be classified according to the number of rows by the number of columns (R by C).

Percentage Cross-Tabulations When data from a survey are cross-tabulated, percentages help the researcher understand the nature of the relationship by making relative comparisons simpler. The total number of respon- dents or observations may be used as a statistical base for computing the percentage in each cell. When the objective of the research is to identify a relationship between answers to two questions

(A) Cross-Tabulation of Question “Do you shop at Target?” by Sex of Respondent

Yes No Total

Men 150 75 225

Women 180 45 225

Total 330 120 450

(B) Percentage Cross-Tabulation of Question “Do you shop at Target?” by Sex of Respondent, Row Percentage

Yes No Total (Base)

Men 66.7% 33.3% 100% (225)

Women 80.0% 20.0% 100% (225)

Yes No

Men 45.5% 62.5%

Women 54.5% 37.5%

Total 100% 100%

(Base) (330) (120)

EXHIBIT 20.3

Possible Cross-Tabulations of One Question

statistical base

The number of respondents or observations (in a row or column) used as a basis for

computing percentages.

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489

Our Four-Legged Family Members

(or two variables), one of the questions is commonly chosen to be the source of the base for determining percentages. For example, look at the data in parts A, B, and C of Exhibit 20.3. Compare part B with part C. In part B, we are considering gender as the base—what percent- age of men and of women shop at Target? In part C, we are considering Target shoppers as the base—what percentage of Target shoppers are men? Selecting either the row percentages or the column percentages will emphasize a particular comparison or distribution. The nature of the problem the researcher wishes to answer will determine which marginal total will serve as a base for computing percentages.

Fortunately, a conventional rule determines the direction of percentages. The rule depends on which variable is identified as an independent variable and which is a dependent variable. Simply put, independent variables should form the rows in a contingency table. The marginal total of the independent variable should be used as the base for computing the percentages. Although survey research does not establish cause-and-effect evidence, one might argue that it would be logical to assume that a variable such as biological sex might predict beverage preference. This makes more sense than thinking that beverage preference would determine biological sex!

‘‘The more we study, the more we discover our ignorance.’’—PERCY BYSSHE SHEllEY

RESEARCH SNAPSHOT

ric Is

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We all have different sized families, but what is often the case in American households is that at least one member of the family is a pet. And, overwhelmingly that pet is a dog, a cat, or both. The interesting question is: What does it mean to be a “member of the family,” and do men or women treat their four-legged friends as family members in different ways? This would suggest a set of contingent arguments, that is, does treating a pet as a member of the family depend upon whether you are a male or female? And, does the type of treatment of that pet depend on whether you are male or female as well? A Harris Interactive Poll conducted in May 2011 sought to answer this interesting question.

The Harris Interactive Poll conducted a stratified survey of over 2,000 adults across the United States. The sample included regional information, age, race, and gender, as well as income and political party identification. Consider the following 2-by-2 contingency table, excluding those that responded as “Not Sure.” Harris asked an initial question, “Do you consider your pet to be a member of your family?”

% Male % Female

Yes 85 95

No 12 3

It would appear that women are much more likely to see their pet as a part of the family. But the second contingent question is also interesting: If you see your pet as a family member, are there different things pet owners do for their

pets by gender? When examining the contin- gency tables for this question, males and females who responded “Frequently” or “Occa- sionally” indicated the following:

% Male % Female

Allowed the pet to sleep in the bed 67 72

Bought my pet a holiday present 56 63

Bought my pet a birthday present 41 33

Cooked especially for my pet 27 22

Dressed my pet in some type of clothing

17 15

Took my pet to work 17 6

The results are fairly similar, but some interesting results do appear. First, while females are more likely to have their pet sleep in the bed and buy them a holiday present, males appear more likely to buy a birthday present, cook something special for them, and take them to work. The results suggest that the ways we treat our pets as a family member depends upon (i.e., is contingent upon) whether we are males or females.

Source: “Pets Really are Members of the Family,” Harris Interactive, http:// www.harrisinteractive.com/vault/HI-Harris-Poll-Pet-Ownership-2011-06-10 .pdf, accessed August 4, 2011.

Elaboration and Refinement The Oxford Universal Dictionary defines analysis as “the resolution of anything complex into its simplest elements.” Once a researcher has examined the basic relationship between two variables, he or she may wish to investigate this relationship under a variety of different conditions. Typically, a third variable is introduced into the analysis to elaborate and refine the researcher’s understand- ing by specifying the conditions under which the relationship between the first two variables is strongest and weakest. In other words, a more elaborate analysis asks, “Will interpretation of the relationship be modified if other variables are simultaneously considered?”

Elaboration analysis involves the basic cross-tabulation within various subgroups of the sam- ple. The researcher breaks down the analysis for each level of another variable. If the researcher has cross-tabulated shopping preference by sex (see Exhibit 20.3) and wishes to investigate another variable (say, marital status), a more elaborate analysis may be conducted. Exhibit 20.4 breaks down the responses to the question, “Do you shop at Target?” by sex and marital status. The data show women display the same preference whether married or single. However, married men are much more likely to shop at Target than are single men. The analysis suggests that the original conclusion about the relationship between sex and shopping behavior for women be retained. However, a rela- tionship that was not discernible in the two-variable case is evident. Married men more frequently shop at Target than do single men.

elaboration analysis

An analysis of the basic cross-tabulation for each level

of a variable not previously considered, such as subgroups

of the sample.

Single Married

Men Women Men Women

“Do you shop at Target?”

Yes 55% 80% 86% 80%

No 45% 20% 14% 20%

EXHIBIT 20.4

Cross-Tabulation of Marital Status, Sex, and Responses to the Question “Do You Shop at Target?”

The finding is consistent with an interaction effect. The combination of the two variables, sex and marital status, is associated with differences in the dependent variable. Interactions between variables are the result of one variable being used as a moderating variable. A moderator variable is a third variable that changes the nature of a relationship between the original independent and dependent variables. Marital status is a moderator variable in this case. The interaction effect sug- gests that marriage changes the relationship between sex and shopping preference.

In other situations the addition of a third variable to the analysis may lead us to reject the original conclusion about the relationship. When this occurs, the elaboration analysis suggests the relationship between the original variables is spurious (see Chapter 3).

The chapter vignette described data suggesting a relationship between the type of store in which a consumer shops and beverage preference. Convenience store shoppers seem to choose beer over wine, while grocery store shoppers choose wine over beer. Does store type drive drink- ing preference? Perhaps a third variable, age, determines both the type of store consumers choose to buy in and their preference for adult beverages. Younger consumers both disproportionately shop in convenience stores and drink beer.

How Many Cross-Tabulations? Surveys may ask dozens of questions and hundreds of categorical variables can be stored in a data warehouse. Using computer programs, business researchers could “fish” for relationships by cross-tabulating every categorical variable with every other categorical variable. Thus, every possible response becomes a possible explanatory variable. A researcher addressing an exploratory research question may find some benefit in such a fishing expedition. Software exists that can automatically search through volumes of cross-tabulations. These may even provide some insight into the business questions under investigation. Alternatively, the program may flag the cross- tabulations suggesting

moderator variable

A third variable that changes the nature of a r elationship

between the original independent and dependent

variables.

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490 • PART SIX Data Analysis and Presentation

the strongest relationship. CHAID (chi-square automatic interaction detection) software exemplifies software that makes searches through large numbers of variables possible.7 Data mining can be con- ducted in a similar fashion and may suggest relationships that are worth considering further.

However, outside of exploratory research, researchers should conduct cross-tabulations that address specific research questions or hypotheses. When hypotheses involve relationships among two categorical variables, cross-tabulations are the right tool for the job.

Quadrant Analysis Quadrant analysis is a variation of cross-tabulation in which responses to two rating scale ques- tions are plotted in four quadrants of a two-dimensional table. A common quadrant analysis in business research portrays or plots relationships between average responses about a product attri- bute’s importance and average ratings of a company’s (or brand’s) performance on that product feature. The term importance-performance analysis is sometimes used because consumers rate perceived importance of several attributes and rate how well the company’s brand performs on that attribute. Generally speaking, the business would like to end up in the quadrant indicating high performance on an important attribute.

Exhibit 20.5 illustrates a quadrant analysis for an international, mid-priced hotel chain.8 The chart shows the importance and the performance ratings provided by business travelers. After plot- ting the scores for each of eight attributes, the analysis suggests areas for improvement. The arrows indicate attributes that the hotel firm should concentrate on to move from quadrant three, which means the performance on those attributes is low but business consumers rate those attributes as important, to quadrant four, where attributes are both important and rated highly for performance.

quadrant analysis

An extension of cross- tabulation in which responses to two rating-scale questions are plotted in four quadrants of a two-dimensional table.

importance-performance analysis

Another name for quadrant analysis.

EXHIBIT 20.5

An Importance-Performance or Quadrant Analysis of Hotels

High Performance

Low Importance

High Importance

Attractive Interior

Room Cleanliness

Room Prices

Breakfast Availability

24-Hour Room Service

Prompt Service

Quietness

Entertainment

Low Performance

Data Transformation Simple Transformations Data transformation (also called data conversion) is the process of changing the data from their original form to a format suitable for performing a data analysis that will achieve research objec- tives. Researchers often modify the values of scalar data or create new variables. For example, many

data transformation

Process of changing the data from their original form to a format suitable for performing a data analysis addressing research objectives.

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CHAPTER 20 Basic Data Analysis: Descriptive Statistics • 491

researchers believe that less response bias will result if interviewers ask respondents for their year of birth rather than their age. This presents no problem for the research analyst, because a simple data transformation is possible. The raw data coded as birth year can easily be transformed to age by subtracting the birth year from the current year.

In earlier chapters, we discussed recoding and creating summated scales. These also are com- mon data transformations.

Collapsing or combining adjacent categories of a variable is a common form of data transfor- mation used to reduce the number of categories. A Likert scale may sometimes be collapsed into a smaller number of categories. For instance, consider the following Likert item administered to a sample of state university seniors:

Strongly Disagree Disagree Neutral Agree

Strongly Agree

I am satisfied with my college experience at this university

□ □ □ □ □

The following frequency table describes results for this survey item:

Strongly Disagree Disagree Neutral Agree Strongly Agree

110 30 15 35 210

The distribution of responses suggests the responses are bimodal. That is, two “peaks” exist in the distribution, one at either end of the scale. Exhibit 20.6 shows an example of a bimodal distribution. Since the vast majority of respondents [80 percent 5 (110 1 210)/400] indicated either strongly disagree or strongly agree, the variable closely resembles a categorical variable. In general, students either strongly disagreed or strongly agreed with the statement. So, the researcher may wish to collapse the responses into two categories. While multiple ways exist to accomplish this, the researcher may assign the value of one to all respondents who either strongly disagreed or disagreed and the value two to all respondents who either agreed or strongly agreed. Respondents marking neutral would be deleted from analysis. In this case, we would end up with 140 (110 1 30) respondents that disagree with this statement and 245 (210 1 35) that agreed.

‘‘All that we do is done with an eye to something else.’’—ARISTOTlE

EXHIBIT 20.6

Bimodal Distributions Are Consistent with Transformations into Categorical Values

125

Histogram

100

Fr eq

ue nc

40.00 50.00 60.00 70.00 80.00 90.00 100.00

Mean � 68.1429 Std. Dev. � 21.82851 N � 350

Exams

Adapted from 1987 Nielsen Television Report.

492 • PART SIX Data Analysis and Presentation

Problems with Data Transformations Researchers often perform a median split to collapse a scale with multiple response points into two categories. The median split means respondents below the observed median go into one category and respondents above the median go into another. Although this is common, the approach is best applied only when the data do indeed exhibit bimodal characteristics. When the data are unimodal, such as would be the case with normally distributed data, a median split will throw away valuable information and lead to error.

Exhibit 20.7 illustrates this problem. Clearly, most respondents either slightly agree or slightly dis- agree with this statement. The central tendency could be represented by the median of 3.5, a mean of 3.5, and modes of 3 and 4 (3 and 4 each have the same number of responses). The “outliers,” if any, appear to be those not indicating something other than slight agreement/disagreement. A case can be made that the respondents indicating slight disagreement are more similar to those indicating slight agreement than they are to those respondents indicating strong disagreement. Yet we can see the recode places values 1 and 3 in the same new category, but places values 3 and 4 in a different category (see the recoding scheme in Exhibit 20.7). The data distribution does not support a median split into two cate- gories and so a transformation collapsing these values into agreement and disagreement is inappropriate.

When a sufficient number of responses exist and a variable is ratio, the researcher may choose to delete one-fourth to one-third of the responses around the median to effectively ensure a bimodal distribution. However, median splits should always be performed only with great care, as the inappropriate collapsing of continuous variables into categorical variables ignores the informa- tion contained within the untransformed values. Rather than splitting a continuous variable into two categories to conduct a frequency distribution or cross-tabulation, we have more appropriate analytical techniques that are discussed in the chapters which follow.

median split

Dividing a data set into two categories by placing respondents below the median in one category and respondents above the median in another.

EXHIBIT 20.7

The Problem with Median Splits with Unimodal Data

1.00 2.00 3.00 4.00 5.00 6.00 Shop at Convenience Stores

100

120

140

Fr eq

ue nc

Mean � 3.50 Std. Dev. � 1.02616 N � 350

Shop at Convenience Store

Median (3.5)

Treated Differently

Treated the Same

Frequency Distribution: X 1 5 I Do Most of My Shopping at

Convenience Stores.

Response Category (Code) Counts Cumulative Percentage

Strongly Disagree (1) 10 2.86%

Disagree (2) 40 14.29%

Slightly Disagree (3) 125 50.00%

Slightly Agree (4) 125 85.71%

Agree (5) 40 97.14%

Strongly Agree (6) 10 100.00%

Median 5 3.5

Recode to Complete Data Transformation:

Old Values 1 2 3 4 5 6

New Values 1 1 1 2 2 2 ©

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Index Numbers The consumer price index and wholesale price index are secondary data sources that are frequently used by business researchers. Price indexes, like other index numbers, represent simple data transformations that allow researchers to track a variable’s value over time and compare a variable(s) with other variables. Recalibration allows scores or observations to be related to a certain base period or base number.

Consider the information in Exhibit 20.8. Weekly television viewing statistics are shown grouped by household size. Index numbers can be computed for these observations in the following manner:

1. A base number is selected. The U.S. household average of 52 hours and 36 minutes represents the central tendency and will be used.

2. Index numbers are computed by dividing the score for each category by the base number and multiplying by 100. The index reflects percentage changes from the base:

1 person hh: 41:01 52:36

= 0.7832 * 100 = 78.32

2 person hh: 47:58 52:36

= 0.9087 * 100 = 90.87

3+ person hh: 60:49 52:36

= 1.1553 * 100 = 115.53

Total U.S. average: 52:36 52:36

= 1.0000 * 100 = 100.00

index numbers

Scores or observations recalibrated to indicate how

they relate to a base number.

EXHIBIT 20.8

Hours of Television Usage per Week

Household Size Hours:Minutes

1 41:01

2 47:58

31 60:49

Total U.S. average 52:36

Adapted from 1987 Nielsen Television Report.

If the data are time-related, a base year is chosen. The index numbers are then computed by dividing each year’s activity by the base-year activity and multiplying by 100. Index numbers require ratio measurement scales. Managers may often chart consumption in some category over time. Relating back to the chapter vignette, grocers may wish to chart the U.S. wine consumption index. Using 1968 as a base year, the current U.S. wine consumption index is just over 2.0, mean- ing that the typical American consumer drinks about twice as much wine today as in 1968, which is just over 8.7 liters of wine per year.9 The Research Snapshot “Twitter and the ReTweetability Index” shows another application of data transformation and index creation.

Calculating Rank Order Survey respondents are often asked to rank order their preference for some item, issue, or char- acteristic. For instance, consumers may be asked to rank their three favorite brands or employee respondents may provide rankings of several different employee benefit plans. Ranking data can be summarized by performing a data transformation. The transformation involves multiplying the frequency by the ranking score for each choice to result in a new scale.

For example, suppose a CEO had 10 executives rank their preferences for locations in which to hold the company’s annual conference. Exhibit 20.9 shows how executives ranked each of four

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494 • PART SIX Data Analysis and Presentation

495

locations: Hawaii, Paris, Greece, and Hong Kong. Exhibit 20.10 tabulates frequencies for these rankings. A ranking summary can be computed by assigning the destination with the highest pref- erence the lowest number (1) and the least preferred destination the highest consecutive number (4). The summarized rank orderings were obtained with the following calculations:

Hawaii: (3 3 1) 1 (5 3 2) 1 (1 3 3) 1 (1 3 4) = 20 Paris: (3 3 1) 1 (1 3 2) 1 (3 3 3) 1 (3 3 4) = 26 Greece: (2 3 1) 1 (2 3 2) 1 (4 3 3) 1 (2 3 4) = 26 Hong Kong: (2 3 1) 1 (2 3 2) 1 (2 3 3) 1 (4 3 4) = 28

RESEARCH SNAPSHOT

EXHIBIT 20.9

Executive Rankings of Potential Conference Destinations

Executive Hawaii Paris Greece Hong Kong

1 1 2 4 3

2 1 3 4 2

3 2 1 3 4

4 2 4 3 1

5 2 1 3 4

6 3 4 1 2

7 2 3 1 4

8 1 4 2 3

9 4 3 2 1

10 2 1 3 4

Twitter is one of the fastest growing social networks. A privately funded organization in San Francisco, Twitter’s first prototype was developed in March of 2006 and launched pub- licly five months later. Since then, Twitter has evolved into a real-time messaging service compatible with several different networks and multiple devices:

Simplicity has played an important role in Twitter’s success. People are eager to connect with other people and Twitter makes that simple. Twitter asks one question, “What are you doing?” Answers must be under 140 characters in length and can be sent via mobile texting, instant message, or the web.

Twitter’s core technology is a device agnostic message routing system with rudimentary social networking fea- tures. By accepting messages from sms, web, mobile web, instant message, or from third party API projects, Twitter makes it easy for folks to stay connected.

If you are not familiar with Twitter, a basic understanding of the terminology is necessary. After signing up for a Twitter account, you can tweet your 140 character message. Followers are people who have signed up to receive someone’s Twitter messages. A retweet (or RT) occurs when a follower takes a tweet and then tweets that message to everyone in their own Twitter network.

Encouraging other Twitter users to retweet your messages is the key in spreading your message across the Twittersphere. Wow!

Dan Zarrella, a self-proclaimed viral marketing scientist, has developed an index to assess the most influential Twitter users. While several sites rank users by their number of followers, and others report the number of RTs, Zarrella has combined these figures with the daily number of tweets to calculate the ReTweetability Index:

(Retweets per Day/Tweets per Day)/Followers

The index is intended to provide a score and ranking of Twitter users based on the power of their tweets. The higher the num- ber, the more influential your Twitter account is!

Sources: “About Twitter,” Twitter, http://twitter.com/about; Dan Zarrella’s ReTweetability Index, http://www.retweetability.com; Saric, Marko, “Make Your Blog Go Viral with Twitter ReTweets,” HowToMakeMyBlog.com (January 13, 2009), http://www.howtomakemyblog.com/twitter/ make-your-blog-go-viral-with-twitter-retweets.

Twitter and the ReTweetability Index

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Three executives chose Hawaii as the best destination (ranked 1), five executives selected Hawaii as the second best destination, and so forth. The lowest total score indicates the first (highest) preference ranking. The results show the following rank ordering: (1) Hawaii, (2) Paris, (3) Greece, and (4) Hong Kong. Company employees may be glad to hear their conference will be in Hawaii!

Tabular and Graphic Methods of Displaying Data Tables, graphs, and charts may simplify and clarify data. Graphical representations of data may take a number of forms, ranging from a computer printout to an elaborate pictograph. Tables, graphs, and charts, however, all facilitate summarization and communication. For example, see how the simple frequency table and histogram shown in Exhibit 20.7 provide a summary that quickly and easily com- municates meaning that would be more difficult to see if all 350 responses were viewed separately.

Today’s researcher has many convenient tools to quickly produce charts, graphs, or tables. Even common programs such as Excel and Word include chart functions that can construct the chart within the text document. Bar charts (histograms), pie charts, curve/line diagrams, and scatter plots are among the most widely used tools. Some choices match well with certain types of data and analyses.

Bar charts and pie charts are very effective in communicating frequency tabulations and simple cross-tabulations. Exhibit 20.11 displays frequency data from the chapter vignette with pie charts. Each pie summarizes preference in the respective year. The size of each pie slice corresponds to a frequency value associated with that choice. When the three pie charts are compared, the result

EXHIBIT 20.11

Pie Charts Work Well with Tabulations and Cross-Tabulations

2005 Beverage Preference

Beer 36%

Spirits 21%

Wine 39%

Other 4%

Wine 27%

1992 Beverage Preference

Beer 47%

Spirits 21%

Other 5%

Wine 31%

2008 Beverage Preference

Beer 42%

Spirits 23%

Other 4%

EXHIBIT 20.10

Frequencies of Conference Destination Rankings

Preference Rankings

Destination 1st 2nd 3rd 4th

Hawaii 3 5 1 1

Paris 3 1 3 3

Greece 2 2 4 2

Hong Kong 2 2 2 4

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496 • PART SIX Data Analysis and Presentation

communicates a cross-tabulation. Here, the comparison clearly communicates that wine preference increased at the expense of beer preference from 1992 to 2005, but has yielded some ground in 2008. In other words, the relative slice of pie for wine became larger, then slightly smaller.

Chapter 25 discusses how these and other graphic aids may improve the communication value of a written report or oral presentation.

Computer Programs for Analysis Statistical Packages Just 50 years ago, the thought of a typical U.S. company performing even basic statistical analy- ses, like cross-tabulations, on a thousand or more observations was unrealistic. The personal computer brought this capability not just to average companies, but to small companies and individuals with limited resources. Today, computing power is very rarely a barrier to complet- ing a research project.

In the 1980s and early 1990s, when the PC was still a relatively novel innovation, special- ized statistical software formerly used on mainframe computers made their way into the personal computing market. Today, most spreadsheet packages can perform a wide variety of basic statistical options. Excel’s basic data analysis tool will allow descriptive statistics including frequencies and measures of central tendency to be easily computed.10 Most of the basic statistical features are now menu driven, reducing the need to memorize function labels. Spreadsheet packages like Excel continue to evolve and become more viable for performing many basic statistical analyses.

Despite the advances in spreadsheet applications, commercialized statistical software packages remain extremely popular among researchers. They continue to become easier to use and more compatible with other data interface tools including spreadsheets and word processors. Like any specialized tool, statistical packages are more tailored to the types of analyses performed by statisti- cal analysts, including business researchers. Thus, any serious business or social science researcher should still become familiar with at least one general computer software package.

Two of the most popular general statistical packages are SAS (http://www.sas.com) and SPSS (http://www.spss.com). SAS revenues exceed $2.15 billion in 2008 and its software can be found on computers worldwide. SAS was founded in 1976, and its statistical software historically has been widely used in engineering and other technical fields. SPSS stands for Statistical Package for the Social Sciences, and was founded in 1968. SPSS is commonly used by university business and social science students. Business researchers have traditionally used SPSS more than any other statistical software tool. SPSS has been viewed as more “user-friendly” in the past. However, today’s versions of both SPSS and SAS are very user-friendly and give the user the option of using drop-down menus to conduct analysis rather than writing computer code.

Excel, SAS, and SPSS account for most of the statistical analysis conducted in business research. University students may also be exposed to MINITAB, which is sometimes preferred by economists. However, MINITAB has traditionally been viewed as being less user-friendly than other choices.

In the past, data entry was an issue as specific software required different types of data input. Today, however, all the major software packages, including SAS and SPSS, can work from data entered into a spreadsheet. The spreadsheets can be imported into the data windows or simply read by the program. Most conventional online survey tools will return data to the user in the form of either an SPSS data file, an Excel spreadsheet, or a plain text document.

Exhibit 20.12 shows a printout of descriptive statistics generated by SAS for two variables: EMP (number of employees working in an MSA, or Metropolitan Statistical Area) and SALES (sales vol- ume in dollars in an MSA) for 10 MSAs. The number of data elements (N), mean, standard devia- tion, and other descriptive statistics are displayed. SAS output is generally simple and easy to read.

As an example of SPSS output, the histograms shown in Exhibits 20.6 and 20.7 were created by SPSS. By clicking on “Charts” in the SPSS Tools menu, one can see the variety of charts that can be created. The key place to click to generate statistical results in tabular form is “Analyze.” Here, one can see the many types of analysis that can be created. In this chapter, the choices found by clicking on “Analyze” and then “Descriptive Statistics” are particularly relevant.

CHAPTER 20 Basic Data Analysis: Descriptive Statistics • 497

‘‘The thing to do is to supply light.’’—WOODROW WIlSON

EXHIBIT 20.12

SAS Computer Output of Descriptive Statistics

State = NY Variable N Mean

Standard Deviation

Minimum Value

Maximum Value

Std. Error of Mean Sum Variance C.V.

EMP 10 142.930 232.665 12.800 788.800 73.575 1429.300 54133.0 162.782

SALES 10 5807.800 11905.127 307.000 39401.000 3764.732 58078.000 141732049.1 204.985

Key: EMP = number of employees (000) SALES = Sales (000)

EXHIBIT 20.13

Examples of SPSS Output for Cross-Tabulation

CLASS * SMOKING Cross-Tabulation

Smoking

Count Smoker Nonsmoker Total

Class High School 7 9 16

Undergraduate 9 22 31

Graduate 15 10 25

Career 6 6 12

Total 37 47 84

Exhibit 20.13 shows an SPSS cross-tabulation of two variables, class status and smoking behav- ior. The data come from a sample intercepted on an urban university campus. It addresses the research question, “Does smoking on campus vary across groups?” More nonsmokers than smok- ers are found. However, the results show that graduate students, and to a lesser extent instructors, smoke more than the norm. The SPSS user can ask for any number of statistics and percentages to be included with this output by clicking on the corresponding options.

Computer Graphics and Computer Mapping Graphic aids prepared by computers have replaced graphic presentation aids drawn by artists. Computer graphics are extremely useful for descriptive analysis. As mentioned in Chapter 2, decision support systems can generate two- or three-dimensional computer maps to portray data about sales, demographics, lifestyles, retail stores, and other features. Exhibit 20.14 shows a com- puter graphic depicting how fast-food consumption varies from state to state. The chart shows the relative frequencies of eating fast-food burgers, chicken, tacos, or other types of fast food across several states. Computer graphics like these have become more common as common applications have introduced easy ways of generating 3D graphics and maps. Many computer maps are used by business executives to show locations of high-quality customer segments. Competitors’ locations are often overlaid for additional quick and easy visual reference. Scales that show miles, population densities, and other characteristics can be highlighted in color, with shading, and with symbols.

Many computer programs can draw box and whisker plots, which provide graphic represen- tations of central tendencies, percentiles, variabilities, and the shapes of frequency distributions. Exhibit 20.15 shows a computer-drawn box and whisker plot for 100 responses to a question measured on a 10-point scale. The response categories are shown on the vertical axis. The small box inside the plot represents responses for half of all respondents. Thus, half of respondents marked 4, 5, or 6. This gives a measure of variability called the interquartile range, but the term midspread is less complex and more descriptive. The location of the line within the box indicates the median.

box and whisker plots

Graphic representations of central tendencies, percentiles, variabilities, and the shapes of

frequency distributions.

interquartile range

A measure of variability.

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498 • PART SIX Data Analysis and Presentation

The dashed lines that extend from the top and bottom of the box are the whiskers. Each whisker extends either the length of the box (the midspread in our example is two scale points) or to the most extreme observation in that direction.

An outlier is a value that lies outside the normal range of the data. In Exhibit 20.15 outliers are indicated by either a 0 or an asterisk. Box and whisker plots are particularly useful for spotting outliers or comparing group categories (e.g., men versus women).

outlier

A value that lies outside the normal range of the data.

EXHIBIT 20.15

Computer-Drawn Box and Whisker Plot

EXHIBIT 20.14

A 3D Graph Showing Fast- Food Consumption Patterns around the United States

Fast Food Consideration

80–90 70–80 60–70 50–60 40–50 30–40 20–30 10–20 0–10

Louisiana Colorado Ohio Kansas Arizona Burger

Chicken

Tacos

Source: From “Graphic Displays of Data: Box and Whisker Plots,” Reports No. 17, Market Facts, Inc.

Mean

0.0

2.00

4.00

6.00

8.00

10.0

Median 75th percentile 25th percentile Standard deviation

5.40 5.00 6.00 4.00 1.62

Potential Outliers

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CHAPTER 20 Basic Data Analysis: Descriptive Statistics • 499

Interpretation An interpreter at the United Nations translates a foreign language into another language to explain the meaning of a foreign diplomat’s speech. In business research, the interpretation process explains the meaning of the data. After the statistical analysis of the data, inferences and conclusions about their meaning are developed.

A distinction can be made between analysis and interpretation. Interpretation is drawing infer- ences from the analysis results. Inferences drawn from interpretations lead to managerial implica- tions. In other words, each statistical analysis produces results that are interpreted with respect to insight into a particular decision. The logical interpretation of the data and statistical analysis are closely intertwined. When a researcher calculates a cross-tabulation of employee number of dependents with choice of health plan, an interpretation is drawn suggesting that employees with a different number of dependents may be more or less likely to choose a given health plan. This interpretation of the statistical analysis may lead to a realization that certain health plans are better suited for different family situations.

From a management perspective, however, the qualitative meaning of the data and their mana- gerial implications are an important aspect of the interpretation. Consider the crucial role played by interpretation of research results in investigating one new product, a lip stain that could color the lips a desired shade semipermanently and last for about a month at a time:

The lip stain idea, among lipstick wearers, received very high scores on a rating scale ranging from “excel- lent” to “poor,” presumably because it would not wear off. However, it appeared that even among routine wearers of lipstick the idea was being rated highly more for its interesting, even ingenious, nature than for its practical appeal to the consumer’s personality. They liked the idea, but for someone else, not them- selves. . . . [Careful interpretation of the data] revealed that not being able to remove the stain for that length of time caused most women to consider the idea irrelevant in relation to their own personal needs and desires. Use of the product seems to represent more of a “permanent commitment” than is usually associated with the use of a particular cosmetic. In fact, women attached overtly negative meaning to the product concept, often comparing it with hair dyes instead of a long-lasting lipstick.11

This example shows that interpretation is crucial. However, the process is difficult to explain in a textbook because there is no one best way to interpret data. Many possible interpretations of data may be derived from a number of thought processes. Experience with selected cases will help you develop your own interpretative ability.

Data are sometimes merely reported and not interpreted. Research firms may provide reams of computer output that do not state what the data mean. At the other extreme, some researchers tend to analyze every possible relationship between each and every variable in the study. Such an approach is a sign that the research problem was not adequately defined prior to beginning the research and the researcher really doesn’t know what business decision the research is addressing. Researchers who have a clear sense of the purpose of the research do not request statistical analyses of data that have little or nothing to do with the primary purpose of the research.

SUMMARY 1. Know what descriptive statistics are and why they are used. Descriptive analyses provide descriptive statistics including measures of central tendency and variation. Statistics such as the mean, mode, median, range, variance, and standard deviation are all descriptive statistics. These statistics provide a summary describing the basic properties of a variable.

2. Create and interpret simple tabulation tables. Statistical tabulation is another way of saying that we count the number of observations in each possible response category. In other words, tabulation is the same as tallying. Tabulation is an appropriate descriptive analysis for less-than interval variables. Frequency tables and histograms are used to display tabulation results.

3. Understand how cross-tabulations can reveal relationships. Cross-tabulation is when we combine two or more less-than interval variables to display the relationship. For example, a

interpretation

The process of drawing inferences from the analysis

results.

500 • PART SIX Data Analysis and Presentation

cross-tabulation of respondent gender with adult beverage preference (i.e., beer, spirits, wine) would give us two rows (male and female) and three columns (beer, spirits, wine), which would show the preferred beverage for each gender. The key to interpreting a cross-tabulation result is to compare actual observed values with hypothetical values that would result from pure chance. When observed results vary from these values, a relationship is indicated.

4. Perform basic data transformations. Data transformations are often needed to assist in data analysis and involve changing the mathematical form of data in some systematic way. Basic data transformations include reverse coding, summating scales, creating index numbers, and collapsing a variable based on a median split.

5. List different computer software products designed for descriptive statistical analysis. While spreadsheets have improved with respect to their ability to conduct basic statisti- cal analyses, business researchers still rely heavily on specialized statistical software. SAS and SPSS are two of the best known statistical packages. Each is available for even the most basic modern PC and can be used with a drop-down window interface, practically eliminating the need for writing computer code.

6. Understand a researcher’s role in interpreting the data. The interpretation process explains the meaning of the data. Interpretation is drawing inferences from the analysis results, providing meaning for the figures that are observed. Inferences drawn from interpretations lead to manage- rial implications.

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What are five descriptive statistics used to describe the basic properties of variables? 2. What is a histogram? What is the advantage of overlaying a normal distribution over a histogram? 3. A survey asks respondents to respond to the statement, “My work is interesting.” Interpret the frequency distribution shown here (taken

from an SPSS output): My work is interesting:

Category Label Code Abs. Freq. Rel. Freq. (Pct.) Adj. Freq. (Pct.) Cum. Freq. (Pct.)

Very true 1 650 23.9 62.4 62.4

Somewhat true 2 303 11.2 29.1 91.5

Not very true 3 61 2.2 5.9 97.3

Not at all true 4 28 1.0 2.7 100.0

• 1,673 61.6 Missing

Total 2,715 100.0 100.0

Valid cases 1,042 Missing cases 1,673

KEY TERMS AND CONCEPTS box and whisker plots, 498 contingency table, 487 cross-tabulation, 486 data transformation, 491 descriptive analysis, 484 elaboration analysis, 490 frequency table, 486

histogram, 485 importance-performance analysis, 491 index numbers, 494 interpretation, 500 interquartile range, 498 marginals, 487 median split, 493

moderator variable, 490 outlier, 499 quadrant analysis, 491 statistical base, 488 tabulation, 486

Chapter 20: Basic Data Analysis: Descriptive Statistics 501

4. Use the data in the following table to a. Prepare a frequency distribution of the respondents’ ages b. Cross-tabulate the respondents’ genders with cola preference c. Identify any outliers

Individual Gender Age Cola

Preference Weekly Unit

Purchases

James M 19 Coke 2

Parker M 17 Pepsi 5

Bill M 20 Pepsi 7

Laurie F 20 Coke 2

Jim M 18 Coke 4

Jill F 16 Coke 4

Tom M 17 Pepsi 12

Julia F 22 Pepsi 6

Amie F 20 Pepsi 2

Dawn F 19 Pepsi 3

5. Data on the average size of a soda (in ounces) at all 30 major league baseball parks are as follows: 14, 18, 20, 16, 16, 12, 14, 16, 14, 16, 16, 16, 14, 32, 16, 20, 12, 16, 20, 12, 16, 16, 24, 16, 16, 14, 14, 12, 14, 20. Compute descriptive statistics for this variable including a box and whisker plot. Comment on the results.

6. The following computer output shows a cross-tabulation of frequencies and provides frequency number (N) and row (R) percentages. a. Interpret this output including a conclusion about whether or not the row and column variables are related. b. Critique the way the analysis is presented. c. Draw a pie chart indicating percentages for having read a book in the past three months for those with and those without high

school diplomas.

Have You Read a Book in Past 3 Months?

Have High School Diploma?

Yes No Total

Yes 489 174 663

73.8 26.2

No 473 378 851

55.6 44.4

. . . . . . . . . . . . . . . . . .

Total 962 552 1,514

7. List and describe at least three basic data transformations. 8. What conditions suggest that a ratio variable should be transformed (recoded) into a dichotomous (two group) variable? 9. A data processing analyst for a research supplier finds that preliminary computer runs of survey results show that consumers love a client’s

new product. The employee buys a large block of the client’s stock. Is this ethical?

502 Part 6: Data Analysis and Presentation

Body on Tap A few years ago Vidal Sassoon, Inc., took legal action against Bristol-Myers over a series of TV com- mercials and print ads for a shampoo that had been

named Body on Tap because of its beer content.12 The prototype commercial featured a well-known high fashion model saying, “In shampoo tests with over 900 women like me, Body on Tap got higher ratings than Prell for body. Higher than Flex for condition- ing. Higher than Sassoon for strong, healthy-looking hair.”

The evidence showed that several groups of approximately 200 women each tested just one shampoo. They rated it on a six- step qualitative scale, from “outstanding” to “poor,” for 27 separate attributes, such as body and conditioning. It became clear that 900 women did not, after trying both shampoos, make product- to-product comparisons between Body on Tap and Sassoon or between Body on Tap and any of the other brands mentioned. In fact, no woman in the tests tried more than one shampoo.

The claim that the women preferred Body on Tap to Sassoon for “strong, healthy-looking hair” was based on combining the data for the “outstanding” and “excellent” ratings and discarding the lower four ratings on the scale. The figures then were 36 percent for Body on Tap and 24 percent (of a separate group of women) for Sassoon.

When the “very good” and “good” ratings were combined with the “outstanding” and “excellent” ratings, however, there was only a dif- ference of one percent between the two products in the category of “strong, healthy-looking hair.”

The research was conducted for Bristol-Myers by Market- ing Information Systems, Inc. (MISI), using a technique known as blind monadic testing. The president of MISI testified that this method typically is employed when what is wanted is an abso- lute response to a product “without reference to another specific product.” Although he testified that blind monadic testing was used in connection with comparative advertising, that was not the purpose for which Bristol-Myers retained MISI. Rather, Bristol-Myers wished to determine consumer reaction to the introduction of Body on Tap. Sassoon’s in-house research expert stated flatly that blind monadic testing cannot support comparative advertising claims.

Question Comment on the professionalism of the procedures used to make the advertising claim. Why do you believe the researchers performed the data transformations described?

CASE 20.1

Downy-Q Quilt The research for Downy-Q is an example of a com- mercial test that was conducted when an advertis- ing campaign for an established brand had run its

course.13 The revised campaign, “Fighting the Cold,” emphasized that Downy-Q was an “extra-warm quilt”; previous research had demonstrated that extra warmth was an important and deliverable product quality. The commercial test was requested to measure the campaign’s ability to generate purchase interest.

The marketing department had recommended this revised adver- tising campaign and was now anxious to know how effectively this commercial would perform. The test concluded that “Fighting the Cold” was a persuasive commercial. It also demonstrated that the new campaign would have greater appeal to specific market segments.

Method Brand choices for the same individuals were obtained before and after viewing the commercial. The commercial was tested in 30-second, color-moving, storyboard form in a theater test. Invited viewers were shown programming with commercial inserts. Qualified respondents were women who had bought quilts in outlets that carried Downy-Q. The results are shown in Case Exhibits 20.2-1 through 20.2-4.

Question Interpret the data in these tables. What recommendations and conclusions would you offer to Downy-Q management?

CASE 20.2

1. ’NET Go the website for the Chicago Cubs baseball team (http://chicago.cubs.mlb.com). Use either the schedule listing or the stats information to find their record in the most recent season. Create a data file with a variable indicating whether each game was won or lost and a variable indicating whether the game was played at home in Wrigley Field or away from home. Using computerized software like SPSS or SAS, a. Compute a frequency table and histogram for each variable. b. Use cross-tabulations to examine whether a relationship

exists between where the game is played (home or away) and winning.

c. Extra analysis: Repeat the analyses for the Houston Astros baseball team (http://houston.astros.mlb.com). What does this suggest for the relationship between playing at home and winning?

2. ’NET Go to http://www.spss.com and click on Solutions, and then select an industry. What services does the company provide for that particular industry?

RESEARCH ACTIVITIES

(Continued )

Chapter 20: Basic Data Analysis: Descriptive Statistics 503

CASE EXHIBIT 20.2-1

Shifts in Brand Choice before and after Showing of Downy-Q Quilt Commercial

Question: We are going to give away a sample of fabric softener. You can select the brand you most prefer. Which brand would you chose?

Brand Choice after Commercial (%)

Brand Choice before Commercial

Downy-Q (n 5 23)

Other Brand (n 5 237)

Downy-Q 78 19

Other brand 22 81

CASE EXHIBIT 20.2-2

Pre/Post Increment in Choice of Downy-Q

Improvement in score based on exposure to commercial.

“Fighting the Cold” Norm: All Quilt Commercials

Demographic Group Base Score Average Range

Total audience (260) +15 +10 6–19

By marital status Married Not married

(130) (130)

+17 +12

By age Under 35 35 and over

(130) (130)

+14 +15

By employment status Not employed Employed

(90) (170)

+13 +18

CASE EXHIBIT 20.2-3

Adjective Checklist for Downy-Q Quilt Commercial

Question: Which of these words do you feel come closest to describing the commercial you’ve just seen? (Check all that apply.)

Adjective “Fighting the Cold” (%) Norm: All Quilt Commercials (%)

Positive Appealing Clever Convincing Effective Entertaining Fast moving Genuine Imaginative Informative Interesting Original Realistic Unusual

19 5 18 11 20 12 7 7

24 13 7 8 3

23 24 24 40 14 21 4

21 18 17 20 3 8

(Continued )

Case 20.2 (Continued )

504 Part 6: Data Analysis and Presentation

CASE EXHIBIT 20.2-4

Product Attribute Checklist for Downy-Q

Question: Which of the following statements do you feel apply to Downy-Q? (Mark as many or as few as you feel apply.)

Attributes “Fighting the Cold” (%)

Extra warm 56

Lightweight 48

Pretty designs 45

Durable fabrics 28

Nice fabrics 27

Good construction 27

Negative Amateurish Bad Taste Dull Repetitious Silly Slow Unbelievable Unclear Unimportant Uninteresting

9 4

33 17 8 8 3 3

14 32

11 4

20 16 19 7 5 2

14 19

CASE EXHIBIT 20.2-3 (Continued)

Adjective Checklist for Downy-Q Quilt Commercial

Chapter 20: Basic Data Analysis: Descriptive Statistics 505

After studying this chapter, you should be able to

1. Implement the hypothesis-testing procedure

2. Use p-values to test for statistical significance

3. Test a hypothesis about an observed mean compared to some standard

4. Know the difference between Type I and Type II errors

5. Know when a univariate x2 test is appropriate and how to conduct one

C H A P T E R

E d Bond had worked for PrecisionMetals for six years, but had really only served as an analyst for the production facility. This was the first corpo- rate-level opportunity to showcase his research

skills. His corporate contacts are Rob Baer, who currently serves as Chief Operations Officer, and Kathy Hahn, the Chief Execu- tive Officer for PrecisionMetals. Rob and Kathy specifically asked to meet with Ed about the employee satisfaction survey con- ducted a month ago.

“Ed, we continue to worry about losing metalwork employ- ees at our Madison plant, but our Richmond plant seems to be improving in terms of turnover,” Rob stated. “What is your take on our employee satisfaction?” Ed replied, “We put together an index of three questions that asked about job satisfaction. We have analyzed the data from the Richmond plant, and our average satisfaction is 3.9.”

Kathy asked, “What does 3.9 mean? What am I supposed to take away from that?” Ed responded, “I’m sorry, I should have explained this better. We asked the employees on a scale with five categories, with 1 meaning ’Strongly Disagree,’ and 5 meaning ’Strongly Agree.’ When the scores are averaged for Richmond, our overall satisfaction is 3.9 on this 5.0 point scale.” Rob continued, “Is that good or bad? It sounds OK…I

guess. What about Madison?” Ed, realizing that he was not communicating the information well, responded, “The satisfac- tion score from Madison is 3.5. Historically, both plants have had a satisfaction score of 3.5.”

Kathy realized that Ed was getting flustered. It was time to reassure him. “Ed, we appreciate what you are doing. I’m sorry but I don’t know exactly what the scores mean. Is 3.9 good? Is 3.5 good? Is the difference between Richmond this year and the scores we have seen in the past significant? Is the difference between those scores enough to explain the difference in our

LEARNING OUTCOMES

Univariate Statistical Analysis

Chapter Vignette:

Well, Are They Satisfied or Not?

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turnover? I just want to know if the survey shows if our employ- ees are satisfied or not.”

Ed went back to the research section, with two things on his mind. “I’ve got to actually compare the Richmond satisfaction

score with the old scores,” he thought. And as he walked into his office and shut the door quietly he said to himself, “I can’t just speak about scores of 3.9 and 3.5. I’m here to help them under- stand what the scores really mean.” It was time to get to work!

univariate statistical analysis

Tests of hypotheses involving only one variable.

bivariate statistical analysis

Tests of hypotheses involving two variables.

multivariate statistical analysis

Statistical analysis involving three or more variables or sets of variables.

Introduction Empirical testing of business research data typically involves inferential statistics. This means that an inference or conclusion will be drawn about the population based on observations of a sample representing that population. Statistical analysis can be divided into several groups:

■ Univariate statistical analysis tests hypotheses involving only one variable. ■ Bivariate statistical analysis tests hypotheses involving two variables. ■ Multivariate statistical analysis tests hypotheses and models involving multiple (three or

more) variables or sets of variables.

The focus in this chapter is on univariate statistics. Thus, we examine statistical tests appropriate for drawing inferences about a single variable. In the chapter vignette, PrecisionMetals execu- tives are interested in the employee satisfaction at their plant and how it compares to the histori- cal score. This represents an opportunity to test hypotheses about a single variable—in this case employee job satisfaction. The survey data regarding job satisfaction will be analyzed and tested against the historical benchmark of 3.5.

Hypothesis Testing Descriptive research and causal research designs often conclude with hypothesis tests. Hypotheses are defined as formal statements of explanations stated in a testable form. Generally, hypotheses should be stated in concrete fashion so that the method of empirical testing seems almost obvious. Types of hypotheses tested commonly in business research include the following:

1. Relational hypotheses—examine how changes in one variable vary with changes in another. This is usually tested by assessing covariance in some way, very often with correlation and regression analysis.

2. Hypotheses about differences between groups—examine how some variable varies from one group to another. These types of hypotheses are very common in causal designs, but are also used to examine group differences in survey research. The tests are often t-tests or ANOVA.

3. Hypotheses about differences from some standard—examine how some variable differs from some preconceived standard. The preconceived standard sometimes represents the true value of the variable in a population. These tests can involve either a test of a mean for interval or ratio variables or a test of frequencies if the variable is ordinal or nominal. These tests typify univariate statistical tests.

The Hypothesis-Testing Procedure Process Hypotheses are tested by comparing the researcher’s educated guess with empirical reality. In other words, how does what we expected compare to the data we have gathered? The process can be described as follows:

1. First, the hypothesis is derived from the research objectives. The hypothesis should be stated as specifically as possible.

2. Next, a sample is obtained and the relevant variable is measured.

CHAPTER 21 Univariate Statistical Analysis • 507

508

SURVEY

THIS!

3. The measured value obtained in the sample is compared to the value either stated explicitly or implied in the hypothesis. If the value is consistent with the hypothesis, the hypothesis is supported. If the value is not consistent with the hypothesis, the hypothesis is not supported.

A univariate hypothesis consistent with the chapter vignette would be

H 1 : The average satisfaction at the Richmond plant is greater than 3.5.

If the average job satisfaction is 3.4, the hypothesis is obviously not supported. However, if the aver- age job satisfaction is 3.9, is the hypothesis supported? While we all know 3.9 is higher than 3.5, we are trying to determine if the results of our sample can be inferred to the population. In other words, is the observed value an accurate reflection of the population or possibly an artifact of our sample? Examining this issue is the role of hypothesis testing.

Univariate hypotheses are typified by tests comparing some observed sample mean against a benchmark value. The test addresses the question, “Is the sample mean truly different from the benchmark?” But how different is really different? If the observed sample mean is 3.55 and the benchmark is 3.50, would the hypothesis still be supported? Probably not! How about 3.60? When the observed mean is so close to the benchmark, we do not have sufficient confidence that a second set of data, using a new sample taken from the same population, would show the same results. In contrast, when the mean turns out well above 3.5, perhaps 3.9, then we could more easily trust that another sample would also not produce a mean equal to or less than 3.5.

In statistics classes, students are exposed to hypothesis testing as a contrast between a null and an alternative hypothesis. Perhaps most simply, the “null” hypothesis can be thought of as the oppo- site of the actual hypothesis. In other words, the null hypothesis is constructed such that disproving it allows us to conclude what we assume to be the truth. The null to the hypothesis presented in the Research Snapshot regarding the “Freshman 7.8” is:

H 0 : The average number of pounds gained in the freshman year of college is not equal to 7.8.

The alternative hypothesis states the opposite of the null and represents what we actually believe is the true situation. In this case, the alternative hypothesis is:

H 1 : The average number of pounds gained in the freshman year of college is equal to 7.8.

So, the researcher’s real hypothesis is generally stated in the form of an “alternative” hypothesis. While this terminology is common in statistical theory, we certainly understand it is confusing. Therefore, the use of the term null hypothesis will be avoided when at all possible. The reader should instead focus on what the findings should look like if the proposed hypothesis is true. If the hypothesis above is true, an observed sample’s mean should be approximately 7.8. We then test to see if this idea can be supported by the empirical evidence.

Hypothesis testing is a critical part of what business researchers do for the organization. It is particularly important to understand how data you gather com- pare to benchmarks set by your work group, your firm, or even your industry. Here is a short exercise that will help you understand the importance of this analysis.

1. Select two variables from the Qualtrics survey (job performance characteristics, customer satisfaction, etc.) that could serve as a possible benchmark for a firm.

2. Using frequency distributions of both variables, identify the mean and standard deviation of both variables. 3. Develop a hypothesis statement for both variables. 4. Conduct a hypothesis test for both variables, setting your benchmark value to the scale midpoint (4.0). 5. Notice and comment on the significance of these tests for both variables. What do the results tell you?

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Empirical evidence is provided by test results comparing the observed mean against some sampling distribution. The variance in observations also plays a role because with greater variance, there is more of a chance that the confidence interval includes 7.8. A statistical test’s significance level or p-value (typically .05) is the key indicator of whether or not a hypothesis can be supported.

Significance Levels and p-Values A significance level is a critical probability associated with a statistical hypothesis test that indicates how likely it is that an inference supporting a difference between an observed value and some sta- tistical expectation is true. The term p-value stands for probability-value and is essentially another name for an observed or computed significance level. Exhibit 21.1 discusses interpretations of p-values in different kinds of statistical tests. The probability in a p-value is that the statistical expec- tation (null) for a given test is true. So, low p-values mean there is little likelihood that the statistical expectation is true. This means the researcher’s hypothesis positing (suggesting) a difference between an observed mean and a population mean, or between an observed frequency and a population frequency, or for a relationship between two variables, is likely supported. Are you confused now? Hopefully the remainder of this chapter and those that follow will help clarify these relationships.

Traditionally, researchers have specified an acceptable significance level for a test prior to the analysis. Later, we will discuss this as an acceptable amount of Type I error. For most applications, the significance level is 0.05, but sometimes the acceptable amount of error is specified as 0.1 or 0.01.

significance level

A critical probability associated with a statistical hypothesis test that indicates how likely an inference supporting a difference between an observed value and some statistical expectation is true. The acceptable level of Type I error.

p-value

Probability value, or the observed or computed significance level; p-values are compared to significance levels to test hypotheses.

RESEARCH SNAPSHOT

There is a common belief that when college freshman students start their first semester away from their families, they gain 15 pounds in the first year. Commonly referred to as the “Freshman 15,” few research studies have examined if this extra 15 pounds actually appears. In fact, this belief is so prevalent that at an annual meeting of the Obesity Society, the current generation of college students was referred to as “Generation XL.”

Researchers at Purdue University conducted a study of freshman-year weight gain, using 907 freshman students. Their results were consistent with another study at Brown University. For both universities, freshman students gained between 6 and 8 pounds, with the Purdue average being 7.8 pounds. Male students were more likely to gain weight than female students. Clearly, students were gaining weight. Many of them placed the blame on their newfound freedom. It was just too easy to eat whatever and whenever they wanted. However, it appears that the belief that new stu- dents experience the “Freshman 15” was actually quite a bit higher than reality.

Is the weight gain experienced by the Purdue students typical? As a test, we asked our own students about their weight gain during their freshman year. Granted, it was certainly a subjective assessment, as students were not weighed before or after they started their university educa- tion. But it does allow us to conduct a hypothesis test: Given the results of the Purdue University study, do our students report the same 7.8 pounds of weight gain in their first year of school?

The test was conducted with 46 male and female students with the results shown below:

N Mean Std. Deviation Std. Error Mean

Students 46 5.63 2.51 0.369

Was the self-reported weight gain of these students supportive of the hypothesis? The univariate statistic testing this result sug- gests the answer to this question is no. The p-value for this test is less than 0.001, which supports the position that the mean number of pounds gained by our students is significantly less than 7.8 pounds. It certainly suggests that the “Freshman 15” should lose a few pounds!

Test Value 5 7.8

95% Confidence Interval of the

Difference

Lower UpperT df p-value

(two-tailed) Mean

Difference

Pounds 25.86 45 0.000 22.166 22.911 21.421

Source: Hellmich, Nancy, “Freshman 15 Drops Some Pounds,” USA Today (October 23, 2006), http://www.usatoday.com/news/health/2006-10-22- freshman-weight_x.htm, accessed August 18, 2011.

The “Freshman 7.8”

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If the p-value resulting from a statistical test is less than the prespecified significance level, then a hypothesis about differences is supported.

Consider an example where researchers have identified that a successful fast food franchise relies on households with an average of at least 1.4 children within a 10-minute drive of their location. The researchers then collected data regarding family size in an area being considered for a new store, and the sample shows the average family has 3.1 children. Exhibit 21.2 depicts this data and illustrates an important property of p-values. In this case, the comparison standard of 1.4 is shown as a light blue line, while the sample result is shown as a dark blue line (3.1). The normal curve illustrates what other sample results would likely be. What is most important to realize is that as the observed value gets further from 1.4, the p-value gets smaller, meaning that the chance of the mean actually equaling 1.4 also is less. With the observed mean of 3.1 and the observed standard deviation of 1.02, there is very little chance that the researcher would be wrong in concluding the actual number of children per family in this area is greater than 1.4.

Consider the test in the Research Snapshot “The ‘Freshman 7.8’.” The statistical test is whether the mean computed from the 46 observations is equal to 7.8. Given the risk associated with being wrong, the researcher uses an acceptable significance level of 0.05. After computing the appropriate test, the research observes a computed significance level or p-value that is less than 0.05 (in fact less than 0.001). Therefore, the hypothesis is rejected. The researcher can conclude that our students gain less then 7.8 freshman pounds.

In discussing confidence intervals, statisticians use the term confidence level, or confidence coefficient, to refer to the level of probability associated with an interval estimate. However, when discussing hypothesis testing, statisticians change their terminology and call this a significance level, a (the Greek letter alpha).

EXHIBIT 21.1

p-Values and Statistical Tests Test Statistic

Compare an Observed Frequency with a Predetermined Value

X 2—Low p-values Indicate That Observed Frequency Is Different Than Predetermined Value

Compare an Observed Mean with Some Predetermined Value

Z or t-test—Low p-values Indicate the Observed Mean Is Different Than Some Predetermined Value (Often 0)

Compare an Observed Proportion with Some Predetermined Value

Z or t-test for Proportions—Low p-values Indicate That the Observed Proportion Is Different Than the Predetermined Value

Compare Whether Two Observed Means Are Different from One Another.

Z or t-test—Low p-values Indicate the Means Are Different

Compare Whether Two Less-Than Interval Variables Are Related Using Cross-tabs

Compare Whether Two Interval or Ratio Variables Are Correlated to One Another

t-test for Correlation—Low p-values Indicate the Variables Are Related to One Another

Test Description

Bivariate Tests:

X 2—Low p-values Indicate the Variables Are Related to One Another

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510 • PART SIX Data Analysis and Presentation

An Example of Hypothesis Testing The example described here illustrates the conventional statistical approach to testing a univariate hypothesis with an interval or ratio variable. Suppose the Pizza-In restaurant is concerned about store image before deciding whether to expand. Pizza-In managers are most interested in how friendly customers perceive the service to be. A sample of 225 customers was obtained and asked to indicate their perceptions of service on a five-point scale, where 1 indicates “very unfriendly” service and 5 indicates “very friendly” service. The scale is assumed to be an interval scale, and experience has shown that the previous distribution of this attitudinal measurement assessing the service dimension was approximately normal.

Now, suppose Pizza-In believes the service has to be different from 3.0 before a decision about expansion can be made. In conventional statistical terminology, the null hypothesis for this test is that the mean is equal to 3.0:

H 0 : m 5 3.0

The alternative hypothesis is that the mean does not equal 3.0:

H 1 : m Þ 3.0

More practically, the researcher is likely to write the substantive hypothesis (as it would be stated in a research report or proposal) something like this:

H 1 : Customer perceptions of friendly service are not equal to three.

Note that the substantive hypothesis matches the “alternative” phrasing. In practical terms, research- ers often do not state null and alternative hypotheses. Only the substantive hypothesis implying what is expected to be observed in the sample is formally stated.

EXHIBIT 21.2

As the observed mean gets further from the standard (proposed population mean), the p-value decreases. The lower the p-value, the more confidence you have that the sample mean is different.

2.00 4.00 Children

Fr eq

ue nc

Mean � 3.0684 Std. Dev. � 1.01793 N � 68

Standard Sample Mean

p-Value

0. 5

0.10

0.001

0.0001

0.00001

0.000001

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CHAPTER 21 Univariate Statistical Analysis • 511

Next, the researcher must decide on a significance level. This level corresponds to a region of rejection on a normal sampling distribution as shown in Exhibit 21.1. The peak of the distribu- tion is the theoretical expected value for the population mean. In this case it would be 3. If the acceptable significance level is 0.05, then the 0.025 on either side of the mean that is furthest away from the mean forms the rejection zone (shaded dark blue in Exhibit 21.1). The values within the unshaded area are called acceptable at the 95 percent confidence level (or 5 percent significance level, or 0.05 alpha level), and if we find that our sample mean lies within this region we conclude that it does not differ from the expected value, 3 in this case. More precisely, we fail to reject the null hypothesis. In other words, the range of acceptance (1) identifies those acceptable values that reflect a difference from the hypothesized mean in the null hypothesis and (2) shows the range within which any difference is so small that we would conclude that any observed difference was actually due to random sampling error rather than to a false null hypothesis. H

1 would not be supported.

In our example, the Pizza-In restaurant hired research consultants who collected a sample of 225 interviews. The mean friendliness score on a five-point scale equaled 3.78. The sample stan- dard deviation was S 5 1.5. (If s is known, it is used in the analysis; however, this is rarely true and was not true in this case.1) Now we have enough information to test the hypothesis.

The researcher has decided that the acceptable significance level will be set at 0.05. This means that the researcher wishes to draw conclusions that will be erroneous 5 times in 100 (0.05) or fewer. From the table of the standardized normal distribution, the researcher finds that the Z score of 1.96 represents a probability of 0.025 that a sample mean will be above 1.96 standard errors from m. Likewise, the table shows that 0.025 of all sample means will fall below 21.96 standard errors from m. Adding these two “tails” together, we get 0.05.

The values that lie exactly on the boundary of the region of rejection are called critical values of m. Theoretically, the critical values are Z 5 21.96 and 11.96. Now we must transform these critical Z-values to the sampling distribution of the mean for this image study. The critical values are

Critical value 2 lower limit 5 m 2 ZS X or m 2 Z

S1n 5 3.0 2 1.96a 1.52225b 5 3.0 2 1.96(.1)

5 3.0 2 0.196

5 2.804

Critical value 2 upper limit 5 m 1 ZS X

or m 1 Z S1n

5 3.0 1 1.96a 1.52225b 5 3.0 1 1.96(.1)

5 3.0 1 0.196

5 3.196

Based on survey results, the sample mean (X ) is 3.78. The sample mean is contained in the region of rejection (see the dark shaded areas of Exhibit 21.3). Since the sample mean is greater than the critical value of 3.196, falling in one of the tails (regions of rejection), the researcher concludes that the sample result is statistically significant beyond the 0.05 level. A region of rejection means that the thought that the observed sample mean equals the predetermined value of 3.0 will be rejected when the computed value takes a value within the range. Here is another way to express this result: if we took 100 samples from this population and the mean were actually 3.0, fewer than five will show results that deviate this much.

What does this mean to the management of the Pizza-In? The results indicate that custom- ers believe the service is pretty friendly. The probability is less than 5 in 100 that this result (X 5 3.78) would occur because of random sampling error. This suggests that friendliness of the service personnel may not be a problem. However, a more useful comparison might be between the

critical values

The values that lie exactly on the boundary of the region of

rejection.

512 • PART SIX Data Analysis and Presentation

friendliness rating of Pizza-In and that of a key competitor. That analysis will have to wait until we cover bivariate tests in Chapter 22.

An alternative way to test the hypothesis is to formulate the decision rule in terms of the Z-statistic. Using the following formula, we can calculate the observed value of the Z-statistic given a certain sample mean, (X ):

Z obs

5 X 2 m

S X

5 3.78 2 m

S X

5 3.78 2 3.0

5 .78 .1

5 7.8

In this case, the Z-value is 7.8 and we find that we have met the criterion of statistical significance at the 0.05 level. This result produces a p-value of 0.000001. Once again, since the p-value is less than the acceptable significance level, the hypothesis is supported. The service rating is sig- nificantly higher than 3.0. This example used the conventional statistical terminology involving critical values and a statistical null hypothesis. Once again, it is rare that researchers have to look up tabled values for critical values since the statistical packages will provide an exact p-value for a given test. Thus, the p-value, or a confidence interval associated with the p-value, is the key to interpretation.

Type I and Type II Errors Hypothesis testing using sample observations is based on probability theory. We make an observa- tion of a sample and use it to infer the probability that some observation is true within the popula- tion the sample represents. Because we cannot make any statement about a sample with complete

EXHIBIT 21.3

A Hypothesis Test Using the Sampling Distribution of X under the Hypothesis m 5 3.0

3.0 X2.804 3.196

Hypothesized �

Critical Value–

Lower Limit

Critical Value–

Upper Limit

X from Sample

3.78

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CHAPTER 21 Univariate Statistical Analysis • 513

514

certainty, there is always the chance that an error will be made. When a researcher makes the observation using a census, meaning that every unit (person or object) in a population is measured, a population parameter results and the conclusions are certain. Business researchers very rarely use a census, having to rely on samples and sample statistics.

The researcher using sampling runs the risk of committing two types of errors. Exhibit 21.4 summarizes the state of affairs in the population and the nature of Type I and Type II errors. The four possible situations in the exhibit result because the null hypothesis (using the example above, μ 5 3.0) is actually either true or false and the observed statistics (X 5 3.78) will result in accep- tance or rejection of this null hypothesis.

RESEARCH SNAPSHOT

Although most attorneys and judges do not concern themselves with the statistical terminology of Type I and Type II errors, they do follow this logic. For example, our legal system is based on the concept that a person is inno- cent until proven guilty. Assume that the null hypothesis is that the individual is innocent. If we make a Type I error, we will send an innocent person to prison. Our legal system takes many precautions to avoid Type I errors. A Type II error

would occur if a guilty party were set free (the null hypoth- esis would have been accepted). Our society places such a high value on avoiding Type I errors that Type II errors are more likely to occur.

The Law and Type I and Type II Errors

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Ed it

EXHIBIT 21.4

Type I and Type II Errors in Hypothesis Testing

Decision

Actual State in the Population Accept H 0

Reject H 0

H 0 is true Correct—no error Type I error

H 0 is false Type II error Correct—no error

Type I Error With our example above the null hypothesis is that the mean is equal to 3.0. Suppose the true population mean is indeed equal to 3.0, but the observed sample mean leads to the conclusion that the mean is greater (or less) than 3.0. A Type I error has occurred. A Type I error occurs when a condition that is true in the population is rejected based on statistical observations. When a researcher sets an acceptable significance level for a, he or she is determining tolerance for a Type I error. Simply put, a Type I error occurs when the researcher concludes that there is a statistical difference when in reality one does not exist.

Type II Error Conversely, if our null hypothesis is indeed false, but we conclude that we should not reject the null hypothesis, we make what is called a Type II error. In this example, our null hypothesis that the mean is equal to 3.0 is not true. However, our sample data indicates the mean does not differ from 3.0. So, a Type II error is the probability of failing to reject a false null hypothesis. This incorrect decision is called beta (b). In practical terms, a Type II error means that our sample does not show a difference between an observed mean and a benchmark exists when in fact the difference does exist in the population. The Research Snapshot “The Law and Type I and Type II Errors” provides further clarification of the Type I and Type II conditions.

Type I error

An error caused by rejecting the null hypothesis when it is

true; has a probability of alpha. Practically, a Type I error occurs when the researcher concludes that a relationship or difference

exists in the population when in reality it does not exist.

Type II error

An error caused by failing to reject the null hypothesis when

the alternative hypothesis is true; has a probability of

beta. Practically, a Type II error occurs when a researcher

concludes that no relationship or difference exists when in

fact one does exist.

‘‘It is terrible to speak well and be wrong.’’ —SOPHOCLES

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Unfortunately, without increasing sample size the researcher cannot simultaneously reduce Type I and Type II errors. They are inversely related. Thus, reducing the probability of a Type II error increases the probability of a Type I error. In business problems, Type I errors generally are considered more serious than Type II errors. Thus more emphasis is placed on determining the significance level, a, than in determining b.2

Choosing the Appropriate Statistical Technique Numerous statistical techniques are available to assist the researcher in interpreting data. While gaining competence in these statistical techniques is challenging, perhaps the more difficult task is determining when to use each method. Choosing the right tool for the job is just as important to the researcher as to the mechanic. Making the correct choice can be determined by considering

1. The type of question to be answered 2. The number of variables involved 3. The level of scale measurement

Today, the researcher would only perform a paper and pencil calculation on the simplest of data sets. Virtually all business research hypotheses are tested by using a correct click-through sequence in a statistical software package. The mathematics of these packages is highly reliable. Therefore, if the researcher can choose the right statistic, know the right click-through sequence, and read the resulting output, the right statistical conclusion should be easy to reach.

Type of Question to Be Answered The type of question the researcher is attempting to answer is a consideration in the choice of statistical technique. For example, a researcher may be concerned simply with the central tendency or of the distribution of a variable. Comparison between different business divisions’ sales results with some target level will require a one-sample t-test. Comparison of two salespeople’s average monthly sales will require a t-test of two means, but a comparison of quarterly sales distributions will require a chi-square test.

The researcher should consider the method of statistical analysis before choosing the research design and before determining the type of data to collect. Once the data have been collected, the initial orientation toward analysis of the problem will be reflected in the research design.

Number of Variables The number of variables that will be simultaneously investigated is a primary consideration in the choice of statistical technique. A researcher who is interested only in the average number of times a prospective home buyer visits financial institutions to shop for interest rates can concen- trate on investigating only that single variable at a time. However, a researcher trying to measure multiple complex organizational variables cannot do the same. Simply put, univariate, bivariate, and multivariate statistical procedures are distinguished based on the number of variables involved in an analysis.

Level of Scale of Measurement The scale measurement level helps choose the most appropriate statistical techniques and empirical operations. Testing a hypothesis about a mean, as we have just illustrated, is appropriate for interval- scaled or ratio-scaled data. Suppose a researcher is working with a nominal scale that only identi- fies users versus nonusers of bank credit cards. Because of the type of scale, the researcher may use

CHAPTER 21 Univariate Statistical Analysis • 515

516

only the mode as a measure of central tendency. In other situations, where data are measured on an ordinal scale, the median may be used as the average or a percentile may be used as a measure of dispersion. For example, ranking brand preferences generally employs an ordinal scale. Nominal and ordinal data are often analyzed using frequencies or cross-tabulation.

Parametric versus Nonparametric Hypothesis Tests The terms parametric statistics and nonparametric statistics refer to the two major groupings of statistical procedures. The major distinction between them lies in the underlying assumptions about the data to be analyzed. Parametric statistics involve numbers with known, continuous distribu- tions. When the data are interval or ratio scaled and the sample size is large, parametric statistical procedures are appropriate. Nonparametric statistics are appropriate when the numbers do not conform to a known distribution.

Parametric statistics are based on the assumption that the data in the study are drawn from a pop- ulation with a normal (bell-shaped) distribution and/or normal sampling distribution. For example, if an investigator has two interval-scaled measures, such as gross national product (GNP) and industry sales volume, parametric tests are appropriate. Possible statistical tests might include product-moment correlation analysis, analysis of variance, regression, or a t-test for a hypothesis about a mean.

Nonparametric methods are used when the researcher does not know how the data are distrib- uted. Making the assumption that the population distribution or sampling distribution is normal generally is inappropriate when data are either ordinal or nominal. Thus, nonparametric statistics are referred to as distribution free.3 Data analysis of both nominal and ordinal scales typically uses nonparametric statistical tests.

Exhibit 21.5 illustrates the process of selecting an appropriate univariate statistical method. The exhibit illustrates how statistical techniques vary according to scale properties and the type of ques- tion being asked. While more univariate statistical tests exist than are shown in Exhibit 21.5, these basic options address the majority of univariate analyses in business research contexts.

RESEARCH SNAPSHOT

Having trouble learning statistical concepts? Do a little surfing and the concepts may become clear. Many sources exist that illustrate statistical problems and provide data for practice. Here are just a few:

STATLIB

http://lib.stat.cmu.edu/

StatLib is a system for distributing statistical software, data sets, and information by electronic mail, FTP, and the World Wide Web.

STAT-HELP

http://www.stat-help.com/

Stat-Help.com provides help with statistics via the Inter- net and contains spreadsheets for performing many basic calculations.

SURFSTAT.AUSTRALIA

http://surfstat.anu.edu.au/surfstat-home/surfstat-main.html

SurfStat.australia is an online text in introductory statistics from the University of Newcastle and the Australian government.

THE RICE VIRTUAL LAB IN STATISTICS

http://onlinestatbook.com/rvls.html

http://davidmlane.com/hyperstat/

The Rice Virtual Lab in Statistics provides hypertext materials such as HyperStat Online.

STATCRUNCH

http://www.statcrunch.com/

Stat-Crunch is a statistical software package via the World Wide Web.

GRAPHPAD

http://www.graphpad.com/quickcalcs/Statratio1.cfm

GraphPad software is a p-value calculator.

Living in a Statistical Web

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lit e/

Sh ut

te rs

to ck

parametric statistics

Involve numbers with known, continuous distributions; when

the data are interval or ratio scaled and the sample size is

large, parametric statistical procedures are appropriate.

nonparametric statistics

Appropriate when the variables being analyzed do not conform

to any known or continuous distribution.

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The t -Distribution A univariate t-test is appropriate for testing hypotheses involving some observed mean against some specified value. The t-distribution, like the standardized normal curve, is a symmetrical, bell-shaped distribution with a mean of 0 and a standard deviation of 1.0. When sample size (n) is larger than 30, the t-distribution and Z-distribution are almost identical. Therefore, while the t-test is strictly appropriate for tests involving small sample sizes with unknown standard devia- tions, researchers commonly apply the t-test for comparisons involving the mean of an interval or ratio measure. The precise height and shape of the t-distribution vary with sample size. More specifically, the shape of the t-distribution is influenced by its degrees of freedom (df ). The degrees of freedom are determined by the number of distinct calculations that are possible given a set of information. In the case of a univariate t-test, the degrees of freedom are equal to the sample size (n) minus one. In the Research Snapshot regarding the “Freshman 7.8,” one can see that the sample size is 46 (n 5 46) and the degrees of freedom for the univariate t-test are 45 (df 5 45).

Exhibit 21.6 illustrates t-distributions for 1, 2, 5, and an infinite number of degrees of freedom. Notice that the t-distribution approaches a normal distribution rapidly with increas- ing sample size. This is why, in practice, marketing researchers usually apply a t-test even with large samples. The practical effect is that the conclusion will be the same since the distribu- tions are so similar with large samples and the correspondingly larger numbers of degrees of freedom.

EXHIBIT 21.5

Univariate Statistical Choice Made Easy

Type of Variable?

Interval or Ratio

Nominal Nominal- Proportions

Z-test or t-test Kolmogorov-Smirnov

Test t -test of proportion

Ordinal

Is sample mean different from

hypothesized value?

Is number in each classification equal?

Is observed proportion different from a

hypothesized value?

Are rankings evenly distributed?

�2 test

t-test

A hypothesis test that uses the t-distribution. A univariate t-test is appropriate when the variable being analyzed is interval or ratio.

t-distribution

A symmetrical, bell-shaped distribution that is contingent on sample size; has a mean of 0 and a standard deviation equal to 1.

degrees of freedom (df )

The number of observations minus the number of constraints or assumptions needed to calculate a statistical term.

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CHAPTER 21 Univariate Statistical Analysis • 517

Another way to look at degrees of freedom is to think of adding four numbers together when you know their sum—for example,

4 2 1

1X 12

The value of the fourth number has to be 5. The values of the first three digits could change to any value (freely vary), but the fourth value would have to be determined for the sum to still equal 12. In this example there are three degrees of freedom. Degrees of freedom can be a difficult concept to understand fully. For most basic statistical analyses, the user only needs to remember the rule for determining the number of degrees of freedom for a given test. Today, with computerized software packages, even that number is provided automatically for most tests.

The calculation of t closely resembles the calculation of the Z-value. To calculate t, use the formula

t 5 X 2 m

S X

with n − 1 degrees of freedom. The Z-distribution and the t-distribution are

very similar, and thus the Z-test and t-test will pro- vide much the same result in most situations. How- ever, when the population standard deviation (s) is known, the Z-test is most appropriate. When s is unknown (the situation in most business research studies), and the sample size greater than 30, the Z-test also can be used. When s is unknown and the sample size is small, the t-test is more appropriate.

EXHIBIT 21.6

The t-Distribution for Various Degrees of Freedom

1 2 5

Norm al

Normal

n � 1 n � 2

n � 5

43210�1�2�3�4

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.00

Values of t

Re la

tiv e

Fr eq

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EN CK

O ph

ot og

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Ultra-luxury car makers have sales goals that may involve selling 1,000 cars per year or fewer worldwide. What questions are asked in marketing a car like this that might involve a univariate analysis?4

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518 • PART SIX Data Analysis and Presentation

Calculating a Confidence Interval Estimate Using the t-Distribution Suppose a business organization is interested in finding out how long newly hired MBA gradu- ates remain on their first jobs. On the basis of a small sample of 18 employees with MBAs, the researcher wishes to estimate the population mean with 95 percent confidence. The data from the sample are presented below.

Number of years on first job: 3 5 7 1 12 1 2 2 5 4 2 3 1 3 4 2 6 7

To find the confidence interval estimate of the population mean for this small sample, we use the formula

m 5 X 6 t c.l.

S X

Upper limit 5 X 1 t c.l. a S1nb

Lower limit 5 X 2 t c.l. a S1nb

where

m 5 population mean X 5 sample mean t c.l.

5 critical value of t at a specified confidence level S

X 5 standard error of the mean

S 5 sample standard deviation n 5 sample size

More specifically, the step-by-step procedure for calculating the confidence interval is as follows:

1. We calculate X from the sample. Summing our data values yields oX 5 70, and X 5 oX/n 5 70/18 5 3.89.

2. Since s is unknown, we estimate the population standard deviation by finding S, the sample standard deviation. For our example, S 5 2.81.

3. We estimate the standard error of the mean using the formula S X

5 S/1n. Thus, S

X 5 2.81/218 or S

X 5 0.66.

4. We determine the t-values associated with the desired confidence level. To do this, we go to Table A.3 in the appendix. Although the t-table provides information similar to that in the Z-table, it is somewhat different. The t-table format emphasizes the chance of error, or significance level (a), rather than the 95 percent chance of including the population mean in the estimate. Our example is a two-tailed test. Since a 95 percent confidence level has been selected, the significance level equals 0.05 (1.00 2 0.95 5 0.05). Once this has been deter- mined, all we have to do to find the t-value is look under the 0.05 column for two-tailed tests at the row in which degrees of freedom (df ) equal the appropriate value (n − 1). Below 17 degrees of freedom (n 2 1 5 18 2 1 5 17), the t-value at the 95 percent confidence level (0.05 level of significance) is t 5 2.12.

5. We calculate the confidence interval:

Lower limit 5 3.89 2 2.12a 2.81218b 5 2.49 Upper limit 5 3.89 1 2.12a 2.89218b 5 5.28

In our hypothetical example it may be concluded with 95 percent confidence that the popula- tion mean for the number of years spent on the first job by MBAs is somewhere between 2.49 and 5.28.

CHAPTER 21 Univariate Statistical Analysis • 519

One- and Two-Tailed t-Tests Univariate Z-tests and t-tests can be one- or two-tailed. A one-tailed univariate test is appropri- ate when the researcher has a directional hypothesis implying that an observed mean can only be greater than or less than a hypothesized value. Thus, only one of the “tails” of the bell-shaped normal curve is relevant. For instance, the following hypothesis could be appropriately examined with a one-tailed test:

H 1 : The number of pizza restaurants within postal code 49100 in Angers, France is greater than five.

A two-tailed test is one that tests for differences from the population mean that are either greater or less. Thus, the extreme values of the normal curve (or tails) on both the right and the left are considered. In practical terms, when a research question does not specify whether a difference should be greater than or less than, a two-tailed test is most appropriate. For instance, the following research question could be examined using a two-tailed test:

H 2 : The number of pizza restaurants within postal code 49100 in Angers, France is not equal to 5.

In the case of H 1 , where the hypothesis proposes “greater than five,” if the observed value is signifi-

cantly less than five, the hypothesis is still not supported. Practically, a one-tailed test can be deter- mined from a two-tailed test result by taking half of the observed p-value. When the researcher has any doubt about whether a one- or two-tailed test is appropriate, he or she should opt for the two-tailed test. Most computer software will assume a two-tailed test unless otherwise specified.

Univariate Hypothesis Test Using the t-Distribution The step-by-step procedure for a t-test is conceptually similar to that for hypothesis testing with the Z-distribution. Suppose a Pizza-In store manager believes that the average number of custom- ers who order take-out is 20 per day. The store gathers a sample of data by recording the number of take-out orders for each of the 25 days it was open during a given month. Does the number of take-out orders differ from 20 per day? The substantive hypothesis is

H 1 : m Þ 20

1. The researcher calculates a sample mean and standard deviation. In this case, X 5 22 and S (sample standard deviation) 5 5.

2. The standard error is computed (SX):

S X

5 S1n

5 5225

5 1

3. The researcher then finds the t-value associated with the desired level of confidence level or statistical significance. If a 95 percent confidence level is desired, the significance level is 0.05.

4. The critical values for the t-test are found by locating the upper and lower limits of the confi- dence interval. The result defines the regions of rejection. This requires determining the value of t. For 24 degrees of freedom (n 5 25, df 5 n 2 1), the t-value is 2.064. The critical values are

Lower limit 5 m 2 t c.l.

S X

5 20 2 2.064a 5225b 5 20 2 2.064(1)

5 17.936

Upper limit 5 m 1 t c.l.

S X

5 20 1 2.064a 5225b 5 20 1 2.064(1)

5 22.064

520 • PART SIX Data Analysis and Presentation

Finally, the researcher makes the statistical decision by determining whether the sample mean falls between the critical limits. For the pizza store sample, X 5 22. The sample mean is not included in the region of rejection. Even though the sample result is only slightly less than the critical value at the upper limit, the null hypothesis cannot be rejected. In other words, the pizza store man- ager’s assumption appears to be correct.

As with the Z-test, there is an alternative way to test a hypothesis with the t-statistic. This is by using the formula

t obs

5 X 2 m

S X

t obs

5 22 2 20

1 5

2 1

5 2

We can see that the observed t-value is less than the critical t-value of 2.064 at the 0.05 level when there are 25 2 1 5 24 degrees of freedom. As a result, the p-value is greater than 0.05 and the hypothesis is not supported. Again, we cannot conclude with 95 percent confidence that the mean is not 20.

The Chi-Square Test for Goodness of Fit A chi-square (2) test is one of the most basic tests for statistical significance and is particularly appropriate for testing hypotheses about frequencies arranged in a frequency or contingency table. Univariate tests involving two nominal or ordinal variables are examined with a x2 test. More generally, the x2 test is associated with goodness-of-fit (GOF). GOF can be thought of as how well some matrix (table) of numbers matches or fits another matrix of the same size. Most often, the test is between a table of frequency counts observed in the sample data and another table of expected values (central tendency) for those counts.

Please consider the following hypothesis:

H 1 : Papa John’s Pizza stores are more likely to be located in a standalone location than in a shopping

center.

A competitor may be interested in this hypothesis as part of the competitor analysis in a market- ing plan. A researcher for the competitor gathers a random sample of 100 Papa John’s locations in California (where the competitor is located). The sample is selected from phone directories and the locations are checked by having an assistant drive to each location. The following observations are recorded in a frequency table.

Location One-Way Frequency Table

Standalone 60 stores

Shopping Center 40 stores

Total 100 stores

These observed values (O i ) can be compared to the expected values for this distribution (E

i ) to

complete a x2 test. The x2 value will reflect the likelihood that the observed values come from a distribution reflected by the expected values. The higher the value of the x2 test, the less likely it is that the expected and observed values are the same.

In statistical terms, a x2 test determines whether the difference between an observed frequency distribution and the corresponding expected frequency distribution is due to sampling variation. Computing a x2 test is fairly straightforward and easy. Students who master this calculation should

chi-square (2) test

One of the basic tests for statistical significance that is particularly appropriate for testing hypotheses about frequencies arranged in a frequency or contingency table.

goodness-of-fit (GOF)

A general term representing how well some computed table or matrix of values matches some population or predetermined table or matrix of the same size.

CHAPTER 21 Univariate Statistical Analysis • 521

have little trouble understanding future significance tests since the basic logic of the x2 test under- lies these tests as well.

The steps in computing a x2 test are as follows:

1. Gather data and tally the observed frequencies for the categorical variable. 2. Compute the expected values for each value of the categorical variable. 3. Calculate the x2 value, using the observed frequencies from the sample and the expected

frequencies. 4. Find the degrees of freedom for the test. 5. Make the statistical decision by comparing the p-value associated with the calculated x2 against

the predetermined significance level (acceptable Type I error rate).

These steps can be illustrated with the pizza store location example.

■ The data for the location variable (standalone or shopping center) are provided in the fre- quency table above.

■ The next step asks, “What are the expected frequencies for the location variable? This is another way of asking the central tendency for each category, or how the observations would be distributed if there was no relationship between the categories and number of stores. Since the sample size is 100 and we are dealing with two categories, finding the expected values is easy. If no pattern exists in the locations, they should be distributed randomly and evenly across the two categories. We would expect that half (50) of the locations would be standalone and half (50) would be in a shopping center. This is another way of saying that the expected probability of being either type of location is 50 percent. The expected values also can be placed in a frequency table:

Location Expected Frequencies

Standalone 100/2 5 50 stores

Shopping Center 100/2 5 50 stores

Total 100 stores

■ The actual x2 value is computed using the following formula:

x2 5 o (O

i 2 E

i )2

E i

where

x2 5 chi-square statistic O

i 5 observed frequency in the ith cell

E i 5 expected frequency in the ith cell

Sum the squared differences:

x2 5 (O

1 2 E

1 )2

E i

1 (O

2 2 E

2 )2

E 2

Thus, we determine that the chi-square value equals 4:

x2 5 (60 2 50)2

50 1

(40 2 50)2

5 4

522 • PART SIX Data Analysis and Presentation

523

Alternatively, the calculation can be followed in tabular form:

Location O i

E i

(O i 2 E

i )

(O i 2 E

i )2

E i

Standalone 60 50 10 100/50 5 2.0

Shopping Center 40 50 210 100/50 5 2.0

Total 100 100 0 x2 5 4.0

■ Like many other probability distributions, the x2 distribution is not a single probability curve, but a family of curves. These curves vary slightly with the degrees of freedom. In this case, the degrees of freedom can be computed as

df 5 k 2 1 where

k 5 number of cells associated with column or row data.

Thus, the degrees of freedom equal 1 (df 5 2 2 1 5 1).

■ Now the computed x2 value needs to be compared with the critical chi-square values associ- ated with the 0.05 probability level with 1 degree of freedom. In Table A.4 of the appen- dix the critical x2 value is 3.84. Since the calculated x2 is larger than the tabular chi-square,

RESEARCH SNAPSHOT

Chi-square tests are used often in business research. Consider a business that sponsors a program to educate employees on retirement issues. They need to plan the number and types of activities that should be the focus of the training and devel- opment seminars. One question is whether or not an equal number of younger versus older employees will come to the sessions. They decide to observe the relative frequencies of younger versus older employees based upon the number of sign-ups they receive in the first week since the program was announced, with a cutoff set at 200. The results are shown in the bar chart below:

The x2 value can be computed as shown below:

The HR managers want to be sure a difference exists before investing resources into activities designed for younger or older employees only. Therefore, the acceptable level of Type I error is set at 0.01. Rather than referring to a critical value table, the p-value associated with a x2 value and the associated degrees of freedom can be found on any one of several statistical calcula- tors found on the Internet. In this case, the researcher uses the calculator found at http://faculty.vassar.edu/lowry/tabs .html#csq. By simply plugging in the observed value of 96.8 and the number of degrees of freedom as indicated, 1 in this case, the calculator returns a p-value. In this case, the p-value returned is less than 0.0001. Therefore, since the p-value is less than the acceptable level of risk, the researcher reaches the con- clusion that the older workers are much more likely to attend the retirement seminar. They can design the seminar to meet the needs associated with the number and type of attendees.

Interested in Retirement? It Often Depends on Your Age

Expected Observed

Younger Older

120

100

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Expected Observed O 2 E (O 2 E)2 (O 2 E)2/E

Younger 100 78 222 484 48.4

Older 100 122 22 484 48.4

96.8

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the conclusion is that the observed values do not equal the expected values. Therefore, the hypothesis is supported. More Papa John’s restaurants are located in standalone locations.

We discuss the chi-square test further in Chapter 22 as it is also frequently used to analyze contingency tables.

Hypothesis Test of a Proportion Researchers often test univariate statistical hypotheses about population proportions. The population proportion (p) can be estimated on the basis of an observed sample proportion ( p). Conducting a hypothesis test of a proportion is conceptually similar to hypothesis testing of the mean. Math- ematically, however, the formulation of the standard error of the proportion differs somewhat.

Consider the following example. A state legislature is considering a proposed right-to-work law. One legislator has hypothesized that more than 50 percent of the state’s labor force is unionized. In other words, the hypothesis to be tested is that the proportion of union workers in the state is greater than 0.5.

The researcher formulates the hypothesis that the population proportion (p) exceeds 50 percent (0.5):

H 1 : p . 0.5

Suppose the researcher conducts a survey with a random sample of 100 workers and calculates p 5 0.6. While we know that 0.6 is greater than 0.5, we need to test if the observed difference might be attributed to our sample. While the population proportion is unknown, a large sample allows use of a Z-test (rather than the t-test). If the researcher decides that the decision rule will be set at the 0.01 level of significance, the critical Z-value of 2.57 is used for the hypothesis test. Using the fol- lowing formula, we can calculate the observed value of Z given a certain sample proportion:

Z obs

5 p 2 p

Sp where

p 5 sample proportion p 5 hypothesized population proportion S

p 5 estimate of the standard error of the proportion

The formula for S p is

S p

5 A pqn or Sp 5 A p(1 2 p)n where

S p 5 estimate of the standard error of the proportion

p 5 proportion of successes q 5 1 2 p, proportion of failures

In our example,

S p

5 A (0.6)(0.4)100 5 A 0.24100 5 20.0024 5 0.04899

Z obs

can now be calculated:

Z obs

5 p 2 p

S p

5 0.6 2 0.5 0.04899

hypothesis test of a proportion

A test that is conceptually similar to the one used when the mean is the characteristic of interest but that differs in

the mathematical formulation of the standard error of the

proportion.

524 • PART SIX Data Analysis and Presentation

5 0.1

0.04899

5 2.04

The Z obs

value of 2.04 is less than the critical value of 2.57, so the hypothesis is not supported.

Additional Applications of Hypothesis Testing The discussion of statistical inference in this chapter has been restricted to examining the difference between an observed sample mean and a population or prespecified mean, a 2 test examining the difference between an observed frequency and the expected frequency for a given distribution, and Z-tests to test hypotheses about sample proportions when sample sizes are large. Other hypothesis tests for population parameters estimated from sample statistics exist but are not mentioned here. Many of these tests are no different conceptually in their methods of hypothesis testing. However, the formulas are mathematically different. The purpose of this chapter has been to discuss basic statistical concepts. Once you have learned the terminology in this chapter, you should have no problem generalizing to other statistical problems.

The key to understanding statistics is learning the basics of the language. For this chapter, we begin to adopt a more practical perspective by focusing on the p-values to determine whether a hypothesis is supported rather than discussing null and alternative hypotheses. In more cases than not, low p-values (below the specified ) support researchers’ hypotheses.5 It is hoped that some of the myths about statistics have been shattered and that they are becoming easier to use.

SUMMARY

1. Implement the hypothesis-testing procedure. Hypothesis testing can involve univariate, bivariate, or multivariate statistics. In this chapter, the focus is on univariate statistics. These are tests that involve one variable. Usually, this means that the observed value for one variable will be compared to some benchmark or standard. Statistical analysis is needed to test hypotheses when sample observations are used to draw an inference about some corresponding population. The research establishes an acceptable significance level, representing the chance of a Type I error, and then computes the statistic that applies to the situation. The exact statistic that must be computed depends largely on the level of scale measurement.

2. Use p-values to test for statistical significance. A p-value is the probability value associated with a statistical test. The probability in a p-value is the probability that the expected value for some test dis- tribution is true. In other words, for a t-test, the expected value of the t-distribution is 0. If a researcher is testing whether or not a variable is significantly different from 0, then the p-value that results from the corresponding computed t-value represents the probability that the true population mean is actu- ally 0. For most research hypotheses, a low p-value supports the hypothesis. If a p-value is lower than the researcher’s acceptable significance level (a), then the hypothesis is usually supported.

3. Test a hypothesis about an observed mean compared to some standard. Researchers often have to compare an observed sample mean with some specified value. The appropriate statistical test to compare an interval or ratio level variable’s mean with some value is either the Z- or t-test. The Z-test is most appropriate when the sample size is large or the population standard deviation is known. The t-test is most appropriate when the sample size is small or the population standard deviation is not known. In most practical applications the t-test and Z-test will result in the same conclusion. The t-test is used more often in practice.

4. Know the difference between Type I and Type II errors. A Type I error occurs when a researcher reaches the conclusion that some difference or relationship exists within a population when in fact none exists. In the context of a univariate t-test, the researcher may conclude that some mean value for a vari- able is greater than 0 when in fact the true value for that variable in the population being considered is 0. A Type II error is the opposite situation. When the researcher reaches the conclusion that no difference exists when one truly does exist in the population, the researcher has committed a Type II error. More attention is usually given to Type I errors. Type II errors are very sensitive to sample size.

CHAPTER 21 Univariate Statistical Analysis • 525

KEY TERMS AND CONCEPTS bivariate statistical analysis, 507 chi-square (2) test, 521 critical values, 512 degrees of freedom (df ), 517 goodness-of-fit (GOF), 521 hypothesis test of a proportion, 524

multivariate statistical analysis, 507 nonparametric statistics, 516 parametric statistics, 516 p-value, 509 significance level, 509 t-distribution, 517

t-test, 517 Type I error, 514 Type II error, 514 univariate statistical

analysis, 507

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is the purpose of a statistical hypothesis? 2. What is a significance level? How does a researcher choose a

significance level? 3. What is the difference between a significance level and a p-value? 4. How is a p-value used to test a hypothesis? 5. Distinguish between a Type I and Type II error. 6. What are the factors that determine the choice of the appropri-

ate statistical technique? 7. A researcher is asked to determine whether or not a productivity

objective (in dollars) of better than $75,000 per employee is pos- sible. A productivity test is done involving 20 employees. What conclusion would you reach? The sales results are as follows:

a. 28,000 105,000 58,000 93,000 96,000 b. 67,000 82,500 75,000 81,000 59,000 c. 101,000 60,500 77,000 72,500 48,000 d. 99,000 78,000 71,000 80,500 78,000

8. Assume you have the following data: H 1 : m ∞ 200, S = 30,

n = 64, and X = 218. Conduct a two-tailed hypothesis test at the 0.05 significance level.

9. If the data in question 8 had been generated with a sample of 25 (n = 25), what statistical test would be appropriate?

10. The answers to a researcher’s question will be nominally scaled. What statistical test is appropriate for comparing the sample data with hypothesized population data?

11. A researcher plans to ask employees whether they favor, oppose, or are indifferent about a change in the company retirement

program. Formulate a hypothesis for a chi-square test and the way the variable would be created.

12. Give an example in which a Type I error may be more serious than a Type II error.

13. Refer to the pizza store location 2 data on page 523. What statistical decisions could be made if the 0.01 significance level were selected rather than the 0.05 level?

14. Determine a hypothesis that the following data may address and perform a 2 test on the survey data. a. The X Factor should be broadcast before 9 p.m.

Agree 40

Neutral 35

Disagree 25

100

b. Political affiliations of a group indicate

15. A researcher hypothesizes that 15 percent of the people in a test- market will recall seeing a particular advertisement. In a sample of 1,200 people, 20 percent say they recall the ad. Perform a hypothesis test.

Republicans 102

Democrats 98

200

1. ’NET What is the ideal climate? Fill in the following blanks: The lowest temperature in January should be no lower than _____ degrees. At least _____ days should be sunny in January. a. List at least 15 places where you would like to live. Using

the Internet, find the average low temperature in January for each place. This information is available through various

weather related websites such as http://www.weather.com or through each community’s local news website. Record the data in a spreadsheet or statistical package such as SPSS. Using the benchmark (preferred population low temperature) you filled in above, test whether the sample places that you would like to live have an ideal January minimum temperature.

RESEARCH ACTIVITIES

5. Know the univariate 2 test and how to conduct one. A 2 test is one of the most basic tests for statistical significance. The test is particularly appropriate for testing hypotheses about frequen- cies arranged in a frequency or contingency table. The 2 test value is a function of the observed value for a given entry in a frequency table minus the statistical expected value for that cell. The observed statistical value can be compared to critical values to determine the p-value with any test. The 2 test is often considered a goodness-of-fit test because it can test how well an observed matrix represents some theoretical standard.

526 Part 6: Data Analysis and Presentation

Premier Motorcars Premier Motorcars is the new Fiat dealer in Delavan, Illinois. Premier Motorcars has been regularly advertis- ing in its local market area that the new Fiat 500 aver-

ages 30 miles to a gallon of gas and mentions that this figure may vary with driving conditions. A local consumer group wishes to ver- ify the advertising claim. To do so, it selects a sample of recent pur- chasers of the Fiat 500. It asks them to drive their cars until two tanks of gasoline have been used up and to record the mileage. The group then calculates and records the miles per gallon for each purchaser. The data in Case Exhibit 21.1-1 portray the results of the tests.

Questions 1. Formulate a statistical hypothesis appropriate for the consumer

group’s purpose. 2. Calculate the mean average miles per gallon. Compute the

sample variance and sample standard deviation. 3. Construct the appropriate statistical test for your hypothesis,

using a 0.05 significance level.

CASE 21.1

b. Using the same website, record how many days in January are typically sunny. Test whether or not the number of sunny days meets your standard.

c. For each location, record whether or not there was measur- able precipitation yesterday. Test the following hypothesis:

H 1 : Among places you would like to live, there is less than a

33.3 percent chance of rain/snow on a given day ( five days out of fifteen).

2. ETHICS Examine the statistical choices under Analyze in SPSS. Click on Compare Means. To compare an observed mean to some benchmark or hypothesized population mean, the avail- able choice is a one-sample t-test. A researcher is preparing a report and finds the following result testing a hypothesis that suggests the sample mean did not equal 14.

a. What is the p-value? Is the hypothesis supported? b. Write the 95% confidence interval which corresponds to an

a of 0.05. c. Technically, since the sample size is greater than 30, a Z-test

might be more appropriate. However, since the t-test result is readily available with SPSS, the research presents this result. Is there an ethical problem in using the one-sample t-test?

One-Sample Statistics

N Mean Std.

Deviation Std. Error

Mean

1997–2000 67 14.5337 16.02663 1.95796

Test Value = 14

95% Confidence Interval of the Difference

t df Sig. (two-tailed) Mean Difference Lower Upper

1997–2000 0.273 66 0.786 0.53373 –3.3755 4.4429

CASE EXHIBIT 21.1-1

Miles per Gallon Information

Purchaser Miles per

Gallon Purchaser Miles per

Gallon

1 30.9 13 27.0

2 24.5 14 26.7

3 31.2 15 31.0

4 28.7 16 23.5

5 35.1 17 29.4

6 29.0 18 26.3

7 28.8 19 27.5

8 23.1 20 28.2

9 31.0 21 28.4

10 30.2 22 29.1

11 28.4 23 21.9

12 29.3 24 30.9

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Chapter 21: Univariate Statistical Analysis 527

528

C H A P T E R

W hat if you went to trade in your car, knowing that it had an oil leak “which is not very noticeable and doesn’t require immediate attention,” but would require

$200 to have fixed in the near future?1 Would you feel a moral obligation to tell the car dealer? How about if you went to buy a car with the same issue? Would it be ethical for the dealer to sell you the car without mentioning the oil leak?

Ethical conduct, both of businesses and consum- ers, is an important issue in the business world. Recent research conducted in Flanders, a European region that includes parts of Belgium, France, and the Netherlands, investigated two aspects of ethical perceptions with relevance to business.2 First, is there a difference between women and men in their ethi- cal perceptions? Second, is there an ethical double standard—that consumers view an action performed by a customer as more ethical than the same action performed by a business? The researchers hypoth- esized that women would report higher ethical

standards than men and that the “double standard” would exist with respondents perceiving customer actions as more ethical than business behavior.

While we would need to talk to every man and woman to actually know if their ethical perceptions were different, as researchers we understand that a sample of the population has

After studying this chapter, you should be able to

1. Recognize when a particular bivariate statistical test is appropriate

2. Calculate and interpret a x2 test for a contingency table

3. Calculate and interpret an independent samples t-test comparing two means

4. Understand the concept of analysis of variance (ANOVA)

5. Interpret an ANOVA table

LEARNING OUTCOMES

Bivariate Statistical Analysis Differences Between Two Variables

Chapter Vignette:

Gender Differences and Double Standards in Ethical Perceptions

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to be used in most situations. In this case, business researchers asked 127 respondents to evaluate a series of ethical scenarios (short stories with ethical implications) including the car with the oil leak mentioned above (Scenario 2). These scenarios were split so that half of them had a consumer engaging in the act

while the other half had a business performing the act. The respondents were presented the scenarios and then asked to indicate how ethical they thought the act described was on a scale from 1 indicating “totally unethical” to 7 indicating “totally ethical.” Across the four scenarios, the results show:

Gender Scenario 1 Scenario 2 Scenario 3 Scenario 4

Male 5.59 4.38 5.83 6.24

Female 5.21 3.12 4.88 5.71

While all four scenarios indicate that men rated the activity as more ethical than women, to generalize these results from the sample to the population we need to perform statistical tests. These tests show that there is not a significant difference in

three of the four scenarios, with only Scenario 2 showing a statistically significant difference at the 0.05 level.

When testing for the presence of the proposed double standard, the results show:

Source Scenario 1 Scenario 2 Scenario 3 Scenario 4

Consumer 5.38 3.70 5.32 5.95

Corporate 3.36 1.67 3.38 4.97

In this case, there is a statistically significance difference on all four scenarios. In other words, people perceive the same act as less ethical when performed by a business than a consumer.

How do we do these statistical tests? How can we deter- mine if the results we see might be unique to the sample, or if

these results are likely to be found across the population? This chapter focuses on this question when we are examining differ- ences between two variables.

Introduction The Chapter Vignette is just one illustration of business researchers’ desire to test hypotheses stating that two groups differ. In business research, differences in behavior, characteristics, beliefs, opinions, emotions, or attitudes are commonly examined. For example, in the most basic experi- mental design, the researcher tests differences between subjects assigned to an experimental group and subjects assigned to the control group. The experiment illustration presented in Chapter 12 on self-efficacy is an example of this approach. A survey researcher may be interested in whether male and female consumers purchase a product in the same amount. Business researchers may also test whether or not business units in Europe are as profitable as business units in the United States. Such tests are bivariate tests of differences when they involve only two variables: a variable that acts like a dependent variable and a variable that acts as a classification variable. These bivariate tests of differences are the focus of this chapter.

What Is the Appropriate Test of Difference? Exhibit 22.1 illustrates that the type of measurement, the nature of the comparison, and the num- ber of groups to be compared influence the statistical choice. Often researchers are interested in testing differences in mean scores between groups or in comparing how two groups’ scores are

tests of differences

The investigation of hypotheses stating that two (or more) groups differ with respect to measures on a variable.

‘‘You got to be careful if you don’t know where you’re going, because you might not get there.’’ —YogI BERRA

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 529

EXHIBIT 22.1

Some Bivariate Hypotheses

Information

Golfer Hypothesis or Research Question

Level of Measurement Involved Statistic Used Comment ResultDolly Lori Mel

Average driver distance (meters)

135 150 185 Lori hits her drive further than Dolly

Golfer 5 Nominal; Drive distance 5 Ratio

Independent samples t-test to compare mean distance

Data for Lori and Dolly are used

Supported (t 5 2.07, df 5 56, p , .05)

s 30 25 30

Average 7-wood distance (meters)

140 145 150 Mel hits her driver futher than her 7-wood

Club 5 Nominal (7-wood or driver); 7-wood distance 5 Ratio

Paired-samples t-test to compare mean distances for Mel

Only the data for Mel are used (std of diff 5 30)

Supported (t 5 6.39, df 5 29, p , .05)

s 30 30 30

Sample size (number of balls hit)

28 drives 28 7-woods

30 drives 28 7-woods

29 drives 28 7-woods

A relationship exists between golfers and 7-wood distance

Golfer 5 Nominal; Distance 5 Ratio

One-way ANOVA to compare means for the three groups

All data for 7-wood distance are used (MSE 5 30)

Not supported (F 5 0.83, ns)

Number of drives in fairway

4 22 11 Mel drives the ball more accurately than Dolly

Golfer 5 Nominal; Accuracy 5 Nominal (right, fairway, left)

Cross-tabulation with x2 statistic

Resulting cross tabulation table is 2 rows 3 3 columns (rows 5 golfer and columns 5 accuracy (right, fairway, left))

Supported (x2 5 10.3, df 5 3, p , .05)

Drives missing right of fairway

16 7 9 A relationship exists between golfers and accuracy

Golfer 5 Nominal; Accuracy 5 Nominal (right, fairway, left

Cross-tabulation with x2 statistic

Cross-tabulation is now 3 rows 3 3 columns

Supported (x2 5 23.7, df 5 4, p , .05)

Drives missing left of fairway

8 1 9

distributed across possible response categories. We will focus our attention on these issues.3 The rest of the chapter focuses on how to choose the right statistic for two-group comparisons and perform the corresponding test. Exhibit 22.1 provides a frame of reference for the rest of the chapter by illustrating various possible comparisons involving a few golfers.

Construction of contingency tables for x2 analysis gives a procedure for comparing observed frequencies of one group with the frequencies of another group. This is a good starting point from which to discuss testing of differences.

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530 • PART SIX Data Analysis and Presentation

531

Cross-Tabulation Tables: The x2 Test for Goodness-of-Fit Cross-tabulation is one of the most widely used statistical techniques among business researchers. Cross- tabulations are intuitive, easily understood, and lend themselves well to graphical analysis using tools like bar charts. Cross-tabs are appropriate when the variables of interest are less-than interval in nature.

As we discussed in Chapter 20, a cross-tabulation, or contingency table, is a joint frequency distribution of observations on two or more variables. Researchers generally rely on two-variable cross-tabulations the most since the results can be easily communicated. Cross-tabulations are much like tallying. When two variables exist, each with two categories, four cells result. The x2 distribution provides a means for testing the statistical significance of a contingency table. In other words, the bivar- iate x2 test examines the statistical significance of relationships between two less-than interval variables.

The x2 test for a contingency table involves comparing the observed frequencies (O i ) with

the expected frequencies (E i ) in each cell of the table. The goodness- (or closeness-) of-fit of the

observed distribution with the expected distribution is captured by this statistic. Remember that the convention is that the row variable is considered the independent variable and the column variable is considered the dependent variable.

Recall that in Chapter 21 we used a x2 test to examine whether or not Papa John’s restaurants in California were more likely to be located in a standalone location or in a shopping center. The univariate (one-dimensional) analysis suggests that the majority of the locations (60 percent) are standalone units:

Location One-Way Frequency Table

Standalone 60 stores

Shopping Center 40 stores

Total 100 stores

SURVEY

THIS! our survey includes data that can be appropriately analyzed with the techniques discussed in this chap- ter. After reading the chapter, access the online data and answer the following three questions:

1. Is there a relationship between student gender and their major? Does one gender select certain majors more than another? Use cross-tabulations and the x2 test to examine the relationship between gender and major. What did you find?

2. Is there a difference between those respondents that are currently employed and those that are not currently employed regarding their goal achievement and life satisfaction? Respondents indicated whether or not they were currently employed. Use a t-test to examine the differences between the employed/not employed respondents on the six questions which ask: a. I am energetically pursuing my goals. b. I really can’t see any way around my problems. c. I am meeting the goals I set for myself. d. I am simply not being very successful these days. e. I know there are many ways to achieve my goals. f. My life could hardly be any better

What did you find?

3. Is there a difference among the various student classifications and their attitude regarding their goal achievement and life satisfaction (the questions identified in part 2 above)? one question asks the respondents to indicate their level as a student (lower-level undergrad, upper-level undergrad, etc.). Use ANoVA to examine any differences in attitudes across the student classification groups. What did you find?

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Recall that the x2 5 4.0 with 1 degree of freedom (p , 0.01). Is there any effect of location on Papa John’s restaurants? Suppose the researcher wishes to

examine the following hypothesis:

Standalone locations are more likely to be profitable than are shopping center locations.

While the researcher is unable to obtain the dollar figures for profitability of each unit, a press release indicates which Papa John’s units were profitable and which were not. Cross-tabulation using a x2 test is appropriate because

■ The independent variable (location) is less-than interval. ■ The dependent variable (profitable/not profitable) is less-than interval.

The data can be recorded in the following 2 3 2 contingency table:

Location Profitable Not Profitable Total

Standalone 50 10 60

Shopping Center 15 25 40

Totals 65 35 100

Several conclusions appear evident. One, it seems that more stores are profitable than not profitable (65 versus 35, respectively). Secondly, more of the profitable restaurants seem to be in standalone locations (50 of the 65). However, is the difference strong enough to be statistically significant?

Is the observed difference between standalone and shopping center locations the result of chance variation due to random sampling? Is the discrepancy more than sampling variation? The x2 test allows us to conduct tests for significance in the analysis of the R 3 C contingency table (where R 5 row and C 5 column). The formula for the x2 statistic is the same as that for one-way frequency tables (see Chapter 21):

x2 5 O(Oi 2 Ei )2E i

where x2 5 chi-square statistic O

i 5 observed frequency in the ith cell

E i 5 expected frequency in the ith cell

Again, as in the univariate x2 test, a frequency count of data that nominally identify or categorically rank groups is acceptable.

If the researcher’s hypothesis is true, the frequencies shown in the contingency table should not resemble a random distribution. In other words, if location has no effect on profitability, the profitable and unprofitable stores would be spread evenly across the two location categories. This is really the logic of the test in that it compares the observed frequencies with the theoretical expected values for each cell.

After obtaining the observations for each cell, the expected values for each cell must be obtained. The expected values are what we would find if there is no relationship between the two variables. In this case, that the location of the pizza store has no relationship with whether or not the store is profitable. The expected values for each cell can be computed easily using this formula:

E ij

5 R

i C

n where

R i 5 total observed frequency count in the ith row

C j 5 total observed frequency count in the jth column

n 5 sample size

532 • PART SIX Data Analysis and Presentation

Only the total column and total row values are needed for this calculation. Thus, the calcula- tion could be performed before the data are even tabulated. The following values represent the expected values for each cell:

Location Profitable Not Profitable Total

Standalone (60 3 65)/100 5 39 (60 3 35)/100 5 21 60

Shopping Center (65 3 40)/100 5 26 (40 3 35)/100 5 14 40

Totals 65 35 100

Notice that the row and column totals are the same for both the observed and expected contin- gency matrices. These values also become useful in providing the substantive interpretation of the relationship. Significant variation from the expected value indicates a relationship and tells us the direction.

The actual bivariate x2 test value can be calculated in the same manner as for the univariate test. The one difference is that the degrees of freedom are now obtained by multiplying the number of rows minus one (R 2 1) times the number of columns minus one (C 2 1):

x2 5 O(Oi 2 Ei )2E i

with (R 2 1)(C 2 1) degrees of freedom. The observed and expected values can be plugged into the formula as follows:

x2 5 (50 2 39)2

39 1

(10 2 21)2

21 1

(15 2 26)2

26 1

(25 2 14)2

14 5 3.102 1 5.762 1 4.654 1 8.643 5 22.16

The number of degrees of freedom equals 1:

(R 2 1)(C 2 1) 5 (2 2 1)(2 2 1) 5 1

From Table A.4 in the appendix, we see that the critical value at the 0.05 probability level with 1 df is 3.84. Thus, we are very confident that the observed values are not equal to the expected values. Before the hypothesis can be supported, however, the researcher must check and see that the deviations from the expected values are in the hypothesized direction. Since the difference between the standalone locations’ observed profitability and the expected values for that cell are positive, the hypothesis is supported. Location is associated with profitability. The Research Snapshot “Accurate Information? How About a Chi-Square Test?” provides another example of cross-tabs and a x2 test.

Thus, testing the hypothesis involves two key steps:

1. Examine the statistical significance of the observed contingency table. 2. Examine whether the differences between the observed and expected values are consistent

with the hypothesized prediction.

The examples provided both have 2-by-2 contingency tables (that is, two levels of two variables). However, cross-tabulations and the x2 test can be used regardless of the number of levels. For instance, if the Papa John’s locations were instead standalone, shopping center, and delivery only, we would have a 3-by-2 contingency table. Or, perhaps we want to look at the distribution of our male and female sales reps across our five product lines, which would give us a 2-by-5 contingency table. The number of cells is not limited. However, proper use of the x2 test requires that each expected cell frequency (E) have a value of at least 5. If this sample size requirement is not met, the researcher should take a larger sample or combine (collapse) response categories.

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 533

534

RESEARCH SNAPSHOT

When is a cross-tabulation with a x2 test appropriate? The answer to this question can be determined by answering these questions:

■ Are multiple variables expected to be related to one another? ■ Is the independent variable nominal or ordinal? ■ Is the dependent variable nominal or ordinal?

When the answer to all of these questions is yes, cross- tabulation with a x2 test will address the research question. one common application involves the effect of some workplace change. For

instance, this might involve the adoption of a new technology or the effect of training. For instance, consider the following contingency data represented in bar charts to the right.

The data show whether or not the adoption of a new information system produced accurate or inaccurate informa- tion. The 2-by-2 contingency table underlying this bar chart produces a x2 value of 5.97 with 1 degree of freedom. The p-value is less than 0.05; thus, the new technology does seem

to have changed accuracy. However, we must examine the actual cell counts to see exactly what this effect has been. In this case, the bar chart indicates that the new technology is associated with more incidences of accu- rate rather than inaccurate information.

Sources: For examples of research involving this type of analysis, see Gohmann, S. E., R. M. Barker, D. J. Faulds, and J. Guan, “Salesforce Automation, Perceived Information Accuracy and User Satisfaction,” Journal of Business and Industrial Marketing 20 (2005), 23–32; Makela, C. J. and S. Peters, “Consumer Education: Creating Consumer Awareness among Adolescents in Botswana,” International Journal of Consumer Studies 28 (September 2004), 379–387.

Accurate Information? How About a Chi-Square Test?

The t-Test for Comparing Two Means Cross-tabulations and the x2 test are appropriate when both variables are less-than interval level. However, researchers often want to compare one interval or ratio level variable across categories of respondents. The Chapter Vignette describes such a situation. The researchers are interested in comparing the ethical perceptions between genders. When a researcher needs to compare means for a variable grouped into two categories based on some less-than interval variable, a t-test is appropriate. One way to think about this is testing the way a dichotomous (two-level) independent variable is associated with changes in a continuous dependent variable. Several variations of the t-test exist.

Independent Samples t-Test Most typically, the researcher will apply the independent samples t-test, which tests the differences between means taken from two independent samples or groups. So, for example, if we measure the price for some designer jeans at 30 different retail stores, of which 15 are Internet-only stores (pure clicks) and 15 are traditional stores, we can test whether or not the prices are different based on store type with an independent samples t-test. The t-test for difference of means assumes the two samples (one Internet and one traditional store) are drawn from normal distributions and that the variances of the two populations are approximately equal (homoscedasticity).

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Technology and Accuracy

Inaccurate

New Technology Old Technology

independent samples t-test

A test for hypotheses which compares the mean scores for

two groups comprised of some interval- or ratio-scaled variable

using a less-than interval classificatory variable.

Independent Samples t-Test Calculation The t-test actually tests whether or not the differences between two means is zero. Not surprisingly, this idea can be expressed as the difference between two population means:

m 1

5 m 2 , which is equivalent to m

1 2 m

2 5 0

However, since this is inferential statistics, we test the idea by comparing two sample means (X

1 2 X

2 ).

A verbal expression of the formula for t is:

t 5 Sample mean 1 2 Sample mean 2

Variability of random means

In almost all situations, we will see from the calculation of the two sample means that they are not exactly equal. The question is actually whether the observed differences have occurred by chance, or likely exist in the population. The t-value is a ratio with information about the difference between means (provided by the sample) in the numerator and the standard error in the denomi- nator. To calculate t, we use the following formula:

t 5 X

1 2 X

S X

1 2 X

where

X 1 5 mean for group 1

X 2 5 mean for group 2

S X

1 2 X

5 pooled, or combined, standard error of difference between means

A pooled estimate of the standard error is a better estimate of the standard error than one based on the variance from either sample. The pooled standard error of the difference between means of independent samples can be calculated using the following formula:

S x

1 2 x

5 Da(n1 2 1)S 21 1 (n2 2 1)S22n 1

1 n 2

2 2 ba 1

n 1

1 1 n

where

S 2 1 5 variance of group 1

S 2 2 5 variance of group 2

n 1 5 sample size of group 1

n 2 5 sample size of group 2

Are business majors or sociology majors more positive about a career in business? A t-test can be used to test the difference between sociology majors and business majors on scores on a scale mea- suring attitudes toward business careers. We will assume that the attitude scale is an interval scale. The result of the simple random sample of these two groups of college students is shown below:

Business Students Sociology Students

X 1

5 16.5 X 2

5 12.2

S 1 5 2.1 S

2 5 2.6

n 1 5 21 n

2 5 14

A high score indicates a favorable attitude toward business. We can see in the sample that busi- ness students report a higher score (16.5) than sociology students (12.2). This particular t-test tests

pooled estimate of the standard error

An estimate of the standard error for a t-test of independent means that assumes the variances of both groups are equal.

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 535

whether the difference in attitudes between sociology and business students is significant. That is, is the sample result due to chance or do we expect this difference to exist in the population? A higher t-value is associated with a lower p-value. As the t-value gets higher and the p-value gets lower, the researcher has more confidence that the means are truly different. The relevant data computation is

S x

1 2 x

5 Da(n1 2 1)S 21 1 (n2 2 1)S22n 1

1 n 2

2 2 ba 1

n 1

1 1 n

5 Da(20)(2.1)2 1 (13)(2.6)233 ba 121 1 114b 5 0.797

The calculation of the t-statistic is

t 5 X

1 2 X

S x

1 2 x

t 5 16.5 2 12.2

0.797

5 4.3

0.797

5 5.395

In a test of two means, degrees of freedom are calculated as follows:

df 5 n 2 k

where n 5 n

1 1 n

k 5 number of groups

In our example df equals 33((21 1 14) 2 2). If the 0.01 level of significance is selected, reference to Table A.3 in the appendix yields the critical t-value. The t-value of 2.75 must be surpassed by the observed t-value if the hypothesis test is to be statistically significant at the 0.01 level. The calculated value of t, 5.39, far exceeds the critical value of t for statistical significance, so it is significant at a 5 0.01. The p-value is less than 0.01. In other words, this research shows that business students have significantly more positive attitudes toward business than do sociology students. The Research Snap- shot “Expert ‘T-eeze’ ” describes the situation when an independent samples t-test should be used.

Practically Speaking While it is good to understand the process involved, in practice computer software is used to com- pute the t-test results. Exhibit 22.2 displays a typical t-test printout. These particular results examine the following research question:

RQ: Does religion relate to price sensitivity?

This question was addressed in the context of restaurant and wine consumption by allowing 100 consumers to sample a specific wine and then tell the researcher how much they would be will- ing to pay for a bottle of the wine. The sample included 57 Catholics and 43 Protestants. Because no direction of the relationship is stated (no hypotheses is offered), a two-tailed test is appropriate. Although instructors still find some value in having students learn to perform t-test calculations, this procedure is usually computer generated and interpreted today. Using SPSS, the click-through sequence would be:

Analyze → Compare Means → Independent-Samples t-test

Then, the variable used to categorize the respondent as either Catholic or Protestant would be entered as the grouping variable and the variable with the amount the respondent was willing to pay as the test variable.

536 • PART SIX Data Analysis and Presentation

The interpretation of the t-test is made simple by focusing on either the p-value or the confi- dence interval and the group means. Here are the basic steps:

1. Examine the difference in means to find the “direction” of any difference. In this case, Catholics are willing to pay nearly $11 more than Protestants.

2. Compute or locate the computed t-test value. In this case, t 5 0.998. 3. Find the p-value associated with this t and the corresponding degrees of freedom. Here, the

p-value (two-tailed significance level) is 0.321. This suggests a 32 percent chance that the means are actually equal given the observed sample means. In other words, the difference we see may be due to this sample of 100 respondents rather than being found in the population. Assuming a 0.05 acceptable Type I error rate (a), the appropriate conclusion is that the means are not significantly different.

4. The difference can also be examined using the 95 percent confidence interval ( 2 10.603 , X

1 2 X

2 , 32.070). Since the confidence interval includes 0, we lack suf-

ficient confidence that the true difference between the population means is not 0.

A few points are worth noting about this particular result. First, strictly speaking, the t-test assumes that the two population variances are equal. A slightly more complicated formula exists that will compute the t-statistic assuming the variances are not equal.4 SPSS provides both results when an independent samples t-test is performed. The sample variances appear considerably different in this case (43.4, 64.0). Nonetheless, the conclusions are the same using either assumption. In business research, we often deal with values that have variances close enough to assume equal variance. This isn’t always the case in the physical sciences where variables may take on values of drastically different

EXHIBIT 22.2

Independent Samples t-Test Results

Group Statistics

rel N Mean Std.

Deviation Std. Error

Mean

price Catholic 57 61.00 43.381 5.746

Protestant 43 50.27 64.047 9.767

Independent Samples Test

Levene’s Test for Equality of Variances t-Test for Equality of Means

95% Confidence Interval of the

Difference

F Sig. t d.f. Sig.

(2-tailed) Mean

Difference Std. Error Difference Lower Upper

price Equal variances assumed

Equal variances

not assumed

.769 .383 .998

.947

69.829

.321

.347

10.734

10.374

10.752

11.332

−10.603

−11.868

32.070

33.336

2. Computed t-test value shown in this column (t 5 0.998).

3. p-value for t-value and associated degrees of freedom (t 5 0.998, 98 d.f.)

4. Confidence intervals for a 5 0.05 (100% − 95%). In this case, it includes 0.

1. Shows mean, standard deviation, and standard error for each group (Catholic and Protestant)

NOTE: Top row shows results assuming equal variances. Bottom row assumes variance is

different in each.

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CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 537

538

magnitude. Thus, the rule of thumb in business research is to use the equal variance assumption. In the vast majority of cases, the same conclusion will be drawn using either assumption.

Second, notice that even though the means appear to be not so close to each other, the statisti- cal conclusion is that they are the same. The substantive conclusion is that Catholics and Protestants would not be expected to pay different prices. Why is it that means do not appear to be similar, yet that is the conclusion? The answer lies in the variance. Respondents tended to provide very wide ranges of acceptable prices. Notice how large the standard deviations are compared to the mean for each group. Since the t-statistic is a function of the standard error, which is a function of the standard deviation, a lot of variance means a smaller t-value for any given observed difference. When this occurs, the researcher may wish to double-check for outliers. A small number of wild price estimates could be inflating the variance for one or both groups. An additional consideration would be to increase the sample size and test again.

Third, a t-test is used even though the sample size is greater than 30. Strictly speaking, a Z-test could be used to test this difference. Researchers often employ a t-test even with large samples. As samples get larger, the t-test and Z-test will tend to yield the same result. Although a t-test can be used with large samples, a Z-test should not be used with small samples. Also, a Z-test can be used in instances where the population variance is known ahead of time.

As another example, consider 11 sales representatives categorized as either young (1) or old (2) on the basis of their ages in years, as shown in Exhibit 22.3. The exhibit presents a SAS computer output that compares the mean sales volume for these two groups. We can see that the mean for the young group is 61,879 and that of the old group is 86,962, which appears considerably different. Again, though, this difference is not statistically significant at the 0.05 level as the p-value is 0.3218. In this case, the very small sample size (11 in total) drastically limits the statistical power.

When is an independent samples t-test appropriate? once again, we can find out by answering some simple questions:

■ Is the dependent variable interval or ratio? ■ Can the dependent variable scores be grouped based upon

some categorical variable? ■ Does the grouping result in scores drawn from

independent samples? ■ Are two groups involved in the research question?

When the answer to all questions is yes, an independent samples t-test is appropriate. often, business researchers may wish to examine how some process varies between novices and experts (or new employees and current employees). Consider the following example.

Researchers looked at the difference in decision speed for expert and novice salespeople faced with the same situation. Decision speed is a ratio dependent variable and the scores are grouped based on whether or not the salesperson is an expert or a novice. Thus, this categorical variable produces two groups. The results across 40 respondents, 20 experts, and 20 novices, are shown at the top right.

The average difference in decision time is 38 seconds. Is this significantly different from 0? The calculated t-test is 2.76 with 38 df. The one-tailed p-value is 0.0045; thus the conclusion is reached that experts do take less time to make a decision than do novices.

Source: Shepherd, D. G., S. F. Gardial, M. G. Johnson, and J. O. Rentz, “ Cognitive Insights into the Highly Skilled or Expert Salesperson,” Psychology & Marketing 23 (February 2006), 115–138. Reprinted with permission of John Wiley & Sons, Inc.

Expert “T-eeze”

RESEARCH SNAPSHOT

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Novices

Decision Time (seconds)

Experts

100

120

140

Series 1

Paired-Samples t-Test What happens when means need to be compared that are not from independent samples? Such might be the case when the same respondent is measured twice—for instance, when the respon- dent is asked to rate both how much he or she likes shopping on the Internet and how much he or she likes shopping in traditional stores. Since the liking scores are both provided by the same person, the assumption that they are independent is not realistic. Additionally, if one compares the prices the same retailers charge in their stores with the prices they charge on their websites, the samples cannot be considered independent because each pair of observations is from the same sampling unit.

A paired-samples t-test is appropriate in this situation. The idea behind the paired-samples t-test can be seen in the following computation:

t 5 d

s d /1n

where d is the difference between means, s d is the standard deviation of the observed differences,

and n is the number of observations. Researchers also can compute the paired-samples t-test using statistical software. For example, using SPSS, the click-through sequence would be:

Analyze → Compare Means → Paired-Samples t-test

A dialog box then appears in which the “paired variables” should be entered. When a paired- samples t-test is appropriate, the two numbers being compared are usually scored as separate variables.

Exhibit 22.4 displays a paired-samples t-test result. A sample of 143 young adult consumers was asked to rate how likely they would be to consider purchasing an engagement ring (or want their ring purchased) via (a) an Internet retailer and (b) a well-known jewelry store. Each respondent provided two responses, much as in a within-subjects experimental design. The bar chart depicts the means for each variable (Internet purchase likelihood and store purchase likelihood). The t-test results suggest that the average difference of –42.4 is associated with a t-value of –16.0. As can be seen using either the p-value (0.000 rounded to 3 decimals) or the confidence interval –47.6 , d , −37.1), which does not include 0, the difference is significantly different from 0. Therefore, the results suggest a higher likelihood to buy a wedding ring in a well-known brick-and-mortar retail store than via an Internet merchant. For those of you considering marriage, this might be a good tip!

Management researchers have used paired-samples t-tests to examine the effect of downsiz- ing on employee morale. For instance, job satisfaction for a sample of employees can be measured immediately after the downsizing. Some months later, employee satisfaction can be measured again. The difference between the satisfaction scores can be compared using a paired-samples t-test. Results suggest that the employee satisfaction scores increase within a few months of the downsiz- ing as evidenced by statistically significant paired-samples t-values.5

EXHIBIT 22.3

SAS t-Test Output

t-Test Procedure Variable: CR Sales

Age n Mean Standard Deviation

Standard Error Minimum Maximum Variances t d.f. Prob > |T |

1 6 61879.33333 22356.20845 9126.88388 41152.00000 103059.0000 Unequal 20.9758 5.2 0.3729

2 5 86961.80000 53734.45098 24030.77708 42775.00000 172530.0000 Equal 21.0484 9.0 0.3218

For H 0 : Variances are equal, F 5 5.78 with 4 and 5 d.f., Prob. . F 5 0.0815.

paired-samples t-test

An appropriate test for comparing the scores of two interval variables drawn from related populations.

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CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 539

The Z-Test for Comparing Two Proportions What type of statistical comparison can be made when the observed statistics are proportions? Suppose a researcher wishes to test the hypothesis that wholesalers in Asian and European markets differ in the proportion of sales they make to discount retailers. Testing whether the population proportion for group 1 (p

1 ) equals the population proportion for group 2 (p

2 ) is conceptually the

same as the t-test of two means. This section illustrates a Z-test for differences of proportions, which requires a sample size greater than 30.

The test is appropriate for a hypothesis of this form:

H 0 :p

1 5 p

which may be restated as

H 0 :p

1 2 p

2 5 0

Comparison of the observed sample proportions p 1 and p

2 allows the researcher to ask whether

the difference between two large (greater than 30) random samples occurred due to chance alone. The Z-test statistic can be computed using the following formula:

Z 5 ( p

1 2 p

2 ) 2 (p

1 2 p

2 )

S p

1 2 p

EXHIBIT 22.4

Example Results for a Paired-Samples t-Test

Internet

Shopping Intentions

Store

Paired-Samples Test

Paired Differences

95% Confidence Interval of the

Difference

Mean Std.

Deviation Std. Error

Mean Lower Upper t d.f. Sig.

(2-tailed)

Pair 1 int1 2 int2 242.388 31.660 2.648 247.622 237.154 216.011 142 0.000

1. The average difference between observations is shown here.

3. 95% confidence interval shown here.

2. The computed t-value, d.f., and p-value are shown here.

Z-test for differences of proportions

A technique used to test the hypothesis that proportions

are significantly different for two independent samples or

groups.

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540 • PART SIX Data Analysis and Presentation

where

p 1 5 sample proportion of successes in group 1

p 2 5 sample proportion of successes in group 2

p 1 2 p

2 5 hypothesized population proportion 1 minus hypothesized population proportion 2

S p

1 2 p2 5 pooled estimate of the standard error of differences in proportions

The statistic normally works on the assumption that the value of p 1 2 p

2 is zero, so this formula

is actually much simpler than it looks at first inspection. Readers also may notice the similarity between this and the paired-samples t-test.

To calculate the standard error of the differences in proportions, use the formula

S p

1 2 p2 5 Ap q A 1n1 1 1n2 B

where

p 5 pooled estimate of proportion of successes in a sample q 5 1 − p, or pooled estimate of proportion of failures in a sample

n 1 5 sample size for group 1

n 2 5 sample size for group 2

To calculate the pooled estimator, p, use the formula

p 5 n

1 p

1 1 n

2 p

n 1

1 n 2

Suppose the survey data are as follows:

Asian Wholesalers European Wholesalers

p 1 5 0.35 p

2 5 0.40

n 1 5 100 n

2 5 100

First, the standard error of the difference in proportions is

S p

1 2 p2 5 4Ap q 1n

1 1

1 n

2 B

5 A(0.375)(0.625)A 1100 1 1100 B 5 0.068

where

p 5 (100)(0.35) 1 (100)(0.40)

100 1 100 5 0.375

If we wish to test the two-tailed question of no difference, we must calculate an observed Z-value. Thus,

Z 5 ( p

1 2 p

2 ) 2 (p

1 2 p

2 )

S p

1 2 p

5 (0.35 2 0.40) 2 (0) 0.068

5 2 0.73 In this example the idea that the proportion of sales differs between the Asian and European

regions is not supported. The calculated Z-value is less than the critical Z-value of 1.96. Therefore, the p-value associated with the test is greater than 0.05.

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 541

Analysis of Variance (ANOVA) What Is ANOVA? So far, we have discussed tests for differences between two groups. However, what happens when we have more than two groups? For example, what if we want to test and see if employee turnover differs across our five production plants? When the means of more than two groups or popula- tions are to be compared, one-way analysis of variance (ANOVA) is the appropriate statistical tool. ANOVA involving only one grouping variable is often referred to as one-way ANOVA because only one independent variable is involved. Another way to define ANOVA is as the appropriate statistical technique to examine the effect of a less-than interval independent variable on an at-least interval dependent variable. Thus, a categorical independent variable and a continuous dependent variable are involved. An independent samples t-test can be thought of as a special case of ANOVA in which the independent variable has only two levels. When more levels exist, the t-test alone cannot handle the problem.

The statistical null hypothesis for ANOVA is stated as follows:

m 1

5 m 2

5 m 3

5 Á 5 m k

The symbol k is the number of groups or categories for an independent variable. In other words, all group means are equal. The substantive hypothesis tested in ANOVA is:6

At least one group mean is not equal to another group mean.

As the term analysis of variance suggests, the problem requires comparing variances to make inferences about the means.

The Papa John’s example considered locations that were standalone and shopping center, com- pared to the categorical variable of profitable or not profitable. However, if we knew the exact amount of profit or loss for each store, this becomes a good example of a t-test. Specifically, the independent variable could be thought of as “location,” meaning either standalone or shopping center. The dependent variable is the amount of profit/loss. Since only two groups exist for the independent variable, either an independent samples t-test or one-way ANOVA could be used. The results would be identical. This is shown in the Research Snapshot “More Than One-Way”.

However, assume further that location involved three group levels. Profit would now be com- pared based on whether the store was standalone, shopping center, or delivery only. The t-test would not be appropriate; one-way ANOVA would be the choice for this analysis.

Simple Illustration of ANOVA ANOVA’s logic is fairly simple. Look at the data table below that describes how much coffee respon- dents report drinking each day based on which shift they work (day shift, second shift, or nights).

Day 1

Day 3

Day 4

Day 0

Day 2

Second 7

Second 2

Second 1

Second 6

Night 6

Night 8

Night 3

Night 7

Night 6

analysis of variance (ANOVA)

Analysis involving the investigation of the effects

of one treatment variable on an interval-scaled dependent

variable—a hypothesis-testing technique to determine

whether statistically significant differences in means occur

between two or more groups.

542 • PART SIX Data Analysis and Presentation

543

RESEARCH SNAPSHOT

An independent samples t-test is a special case of one-way ANoVA. When the independent variable in ANoVA has only two groups, the results for an independent samples t-test and ANoVA will be the same.

The two sets of statistical results below demonstrate this fact. Both outputs are taken from the same data. The test considers whether men or women are more excited about a

new Italian restaurant in their town. Sex2 is dummy coded so that 0 5 men and 1 5 women. Excitement was measured on a scale ranging from 0 to 6.

More Than One-Way

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Independent Samples t-Test Results:

Group Statistics

Sex2 N Mean Std. Deviation Std. Error Mean

Excitement 0.00 69 2.64 2.262 0.272

1.00 73 2.32 2.140 0.250

Independent Samples Test

Levene’s Test for Equality of

Variances

t-Test for Equality of Means

95% Confidence Interval of the

Difference

F Sig. t df Sig.

(two-tailed) Mean

Difference Std. Error Difference Lower Upper

Excitement Equal variances assumed

1.768 0.186 0.873 140 0.384 0.323 0.369 20.408 1.053

Equal variances not assumed

0.872 138.265 0.385 0.323 0.370 20.409 1.054

In this case, we would conclude that men and women are equally excited—or unexcited as the case may be. The t of 0.873 with 140 df is not significant (p 5 0.384). ANOVA Results:

Descriptives

95% Confidence Interval for Mean

N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum

Excitement 0.00 69 2.64 2.262 0.272 2.09 3.18 0 7

1.00 73 2.32 2.140 0.250 1.82 2.81 0 7

Total 142 2.47 2.198 0.184 2.11 2.84 0 7

ANOVA

Sum of Squares df Mean Square F Sig.

Excitement Between Groups 3.692 1 3.692 0.763 0.384

Within Groups 677.695 140 4.841

Total 681.387 141

Notice that the F-ratio shown in the ANoVA table is associated with the same p-value as is the t-value above. This is no accident since the F and t are mathematical functions of one another. So, when two groups are involved, the researcher can skin the cat either way!©

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The following table displays the means for each group and the overall mean:

Shift Mean Std. Deviation N

Day 2.00 1.58 5

Second 4.00 2.94 4

Night 6.00 1.87 5

Total 4.00 2.63 14

Exhibit 22.5 plots each observation with a bar. The long vertical line illustrates the total range of observations. The lowest is 0 cups and the highest is 8 cups of coffee for a range of 8. The overall mean is 4 cups. Each group mean is shown with a different colored line that matches the bars cor- responding to the group. The day shift averages 2 cups of coffee a day, the second shift 4 cups, and the night shift 6 cups of coffee per day.

Here is the basic idea of ANOVA. Look at the dark double-headed arrow in Exhibit 22.5. This line represents the range of the differences between group means. In this case, the lowest mean is 2 cups and the highest mean is 6 cups. Thus, the middle vertical line corresponds to the total variation (range) in the data and the thick double-headed black line corresponds to the variance accounted for by the group differences. As the thick black line accounts for more of the total vari- ance, then the ANOVA model suggests that the group means are not all the same, and in particular, not all the same as the overall mean. This also means that the independent variable, in this case work shift, explains the dependent variable. Here, the results suggest that knowing when someone works explains how much coffee they drink. Night-shift workers drink the most coffee.

Partitioning Variance in ANOVA Total Variability An implicit question with the use of ANOVA is, “How can the dependent variable best be pre- dicted?” Absent any additional information, the error in predicting an observation is minimized by choosing the central tendency, or mean for an interval variable. For the coffee example, if no infor- mation was available about the work shift of each respondent, the best guess for coffee drinking

EXHIBIT 22.5

Illustration of ANOVA Logic

SST

Mean for the night shift.

The overall or “grand” mean, as well as mean for second shift.

Mean for the day shift.

Cups

SSB

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544 • PART SIX Data Analysis and Presentation

consumption would be 4 cups. The sum of squares total (SST) or variability that would result from using the grand mean, meaning the mean over all observations, can be thought of as

SST 5 Total of (Observed Value 2 Value Grand Mean)2

Although the term error is used, this really represents how much total variation exists among the measures.

Using the first observation, the error of observation would be

(1 cup 2 4 cups)2 5 9

The same squared error could be computed for each observation and these squared errors totaled to give SST.

Between-Groups Variance ANOVA tests whether “grouping” observations explains variance in the dependent variable. In Exhibit 22.5, the three colors reflect three levels of the independent variable, work shift. Given this additional information about which shift a respondent works, the prediction changes. Now, instead of guessing the grand mean, the group mean would be used. So, once we know that some- one works the day shift, the prediction would be that he or she consumes 2 cups of coffee per day. Similarly, the second and night-shift predictions would be 4 and 6 cups, respectively. Thus, the between-groups variance or sum of squares between groups (SSB) can be found by taking the total sum of the weighted difference between group means and the overall mean as shown:

SSB 5 Total of n group

(Group Mean 2 Grand Mean)2

The weighting factor (n group

) is the specific group sample size. Let’s consider the first observa- tion once again. Since this observation is in the day shift, we predict 2 cups of coffee will be con- sumed. Looking at the day shift group observations in Exhibit 22.5, the new error in prediction would be

(2 cups 2 4 cups)2 5 (2)2 5 4

The error in prediction has been reduced from 3 using the grand mean to 2 using the group mean. This squared difference would be weighted by the group sample size of 5, to yield a contri- bution to SSB of 20.

Next, the same process could be followed for the other groups yielding two more contribu- tions to SSB. Because the second shift group mean is the same as the grand mean, that group’s contribution to SSB is 0. Notice that the night-shift group mean is also 2 different than the grand mean, like the day shift, so this group’s contribution to SSB is likewise 20. The total SSB then represents the variation explained by the experimental or independent variable. In this case, total SSB is 40. The reader may look at the statistical results shown in Exhibit 22.6 to find this value in the sums of squares column.

Within-Group Error Finally, error within each group would remain. Whereas the group means explain the variation between the total mean and the group mean, the distance from the group mean and each indi- vidual observation remains unexplained. This distance is called within-group error or variance or the sum of squares error (SSE). The values for each observation can be found by

SSE 5 Total of (Observed Mean 2 Group Mean)2

Again, looking at the first observation, the SSE component would be

SSE 5 (1 cup 2 2 cups)2 5 1 cup

This process could be computed for all observations and then totaled. The result would be the total error variance—a name used to refer to SSE since it is variability not accounted for by the group means. These three components are used in determining how well an ANOVA model explains a dependent variable.

grand mean

The mean of a variable over all observations.

between-groups variance

The sum of differences between the group mean and the grand mean summed over all groups for a given set of observations.

within-group error or variance

The sum of the differences between observed values and the group mean for a given set of observations; also known as total error variance.

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 545

EXHIBIT 22.6

Interpreting ANOVA

Tests of Between-Subjects Effects (Dependent Variable: Coffee)

Source Type III Sum of Squares d.f.

Mean Square F Sig.

Corrected Model 40.000a 2 20.000 4.400 .039

Intercept 221.538 1 221.538 48.738 .000

Shift 40.000 2 20.000 4.400 .039

Error 50.000 11 4.545

Total 314.000 14

aR Squared 5 .444 (Adjusted R Squared 5 .343)

95% Confidence Interval

Shift Mean Std. Error Lower Bound Upper Bound

Day 2.000 .953 2.099 4.099

Second 4.000 1.066 1.654 6.346

Night 6.000 .953 3.901 8.099

2. This column shows the group means for each level of the independent variable.

1. This row shows overall F-value testing whether all group means are equal. The sum of squares column calculates the SST, SSE, and SSB (shift row).

The F-Test The F-test is the key statistical test for an ANOVA model. The F-test determines whether there is more variability in the scores of one sample than in the scores of another sample. The key ques- tion is whether the two sample variances are different from each other or whether they are from the same population. Thus, the test breaks down the variance in a total sample and illustrates why ANOVA is analysis of variance.

The F-statistic (or F-ratio) can be obtained by taking the larger sample variance and divid- ing by the smaller sample variance. Using Table A.5 or A.6 in the appendix is much like using the tables of the Z- and t-distributions that we have previously examined. These tables portray the F-distribution, which is a probability distribution of the ratios of sample variances. These tables indicate that the distribution of F is actually a family of distributions that change quite drastically with changes in sample sizes. Thus, degrees of freedom must be specified. Inspection of an F-table allows the researcher to determine the probability of finding an F as large as a calculated F.

Using Variance Components to Compute F-Ratios In ANOVA, the basic consideration for the F-test is identifying the relative size of variance com- ponents. The three forms of variation described briefly above are:

1. SSE—variation of scores due to random error or within-group variance due to individual dif- ferences from the group mean. This is the error of prediction.

2. SSB—systematic variation of scores between groups due to manipulation of an experimental variable or group classifications of a measured independent variable or between-groups variance.

F-test

A procedure used to determine whether there is more

variability in the scores of one sample than in the scores of

another sample.

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546 • PART SIX Data Analysis and Presentation

3. SST—the total observed variation across all groups and individual observations.

Thus, we can partition total variability into within-group variance (SSE) and between-groups variance (SSB).

The F-distribution is a function of the ratio of these two sources of variances:

F 5 f aSSB SSE b

A larger ratio of variance between groups to variance within groups implies a greater value of F. If the F-value is large, the results are likely to be statistically significant.

A Different but Equivalent Representation F also can be thought of as a function of the between-groups variance and total variance:

F 5 f a SSB SST 2 SSB

In this sense, the ratio of the thick black line to the middle line representing the total range of data presents the basic idea of the F-value. Appendix 22A explains the calculations in more detail with an illustration.

Practically Speaking Exhibit 22.6 displays the ANOVA result for the coffee-drinking example. Again, one advantage of living in modern times is that even a simple problem like this one need not be hand computed. Even though this example presents a small problem, one-way ANOVA models with more observa- tions or levels would be interpreted similarly.

The first thing to check is whether or not the overall model F is significant. In this case, the computed F 5 4.40 with 2 and 11 degrees of freedom. The p-value associated with this value is 0.039. Thus, we have high confidence in concluding that the group means are not all the same. Second, the researcher must remember to examine the actual means for each group to properly interpret the result. Doing so, the conclusion reached is that the night-shift people drink the most coffee, followed by the second-shift workers, and then lastly, the day-shift workers.

As there are three groups, we may wish to know whether or not group 1 is significantly dif- ferent than group 3 or group 2, and so on. In a later chapter, we will describe ways of examining specifically which group means are different from one another.

SUMMARY

1. Recognize when a particular bivariate statistical test is appropriate. Bivariate statistical techniques analyze scores on two variables at a time. Tests of differences investigate hypotheses stating that two (or more) groups differ with respect to a certain behavior, characteristic, or attitude. Both the type of measurement and the number of groups to be compared influence researchers’ choices of the type of statistical test. When both variables are less-than interval level, a contingency table and a x2 test are appropriate. When one variable is less-than interval with two levels and the other variable is interval or ratio level, a t-test is appropriate. When one vari- able is less-than interval with three or more levels and the other variable is interval or ratio level, ANOVA is the appropriate statistical technique.

2. Calculate and interpret a x2 test for a contingency table. A x2 test is used in conjunction with cross-classification or cross-tabs. Thus, when an independent variable is ordinal or nominal and a dependent variable is likewise ordinal or nominal, a x2 test can examine whether a rela- tionship exists between the row variable and column variable. A x2 test is computed by examin- ing the squared differences between observed cell counts and the expected value for each cell

total variability

The sum of within-group variance and between-groups variance.

CHAPTER 22 Bivariate Statistical Analysis: Differences Between Two Variables • 547

in a contingency table. Higher x2 values are generally associated with lower p-values, meaning there is a greater chance that the relationship between the row and column variable is statistically significant.

3. Calculate and interpret an independent samples t-test comparing two means. When a researcher needs to compare means for a variable grouped into two categories based on some less-than interval variable, a t-test is appropriate. An independent samples t-test examines whether a dependent variable like job satisfaction differs based on a grouping variable like biological sex. Statistically, the test examines whether the difference between the mean for men and women is different from 0. A paired-samples t-test examines whether or not the means from two variables that are not independent are different. A common situation calling for this test is when the two observations are from the same respondent. A simple before-and-after test calls for a paired- samples t-test so long as the dependent variable is continuous.

4. Understand the concept of analysis of variance (ANOVA). ANOVA is the appropriate sta- tistical technique to examine the effect of a less-than interval independent variable with three or more categories on an at-least interval dependent variable. Conceptually, ANOVA partitions the total variability into three types: total variation, between-groups variation, and within-group variation. As the explained variance represented by SSB becomes larger relative to SSE or SST, the ANOVA model is more likely to be significant, indicating that at least one group mean is different from another group mean.

5. Interpret an ANOVA table. An ANOVA table provides essential information. Most impor- tantly, the ANOVA table contains the model F-ratio. The researcher should examine this value along with the corresponding p-value. Generally, as F increases, p decreases, meaning that a statistically significant ANOVA model is more likely.

KEY TERMS AND CONCEPTS analysis of variance (ANOVA), 542 between-groups variance, 545 F-test, 546 grand mean, 545

independent samples t-test, 534 paired-samples t-test, 539 pooled estimate of the standard error, 535 tests of differences, 529

total variability, 547 within-group error or variance, 545 Z-test for differences of

proportions, 540

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What tests of difference are appropriate in the following

situations? a. Average contributions (in $) to a marketing campaign for

healthy living for men and women are to be compared. b. Average contributions (in $) to a marketing campaign for

healthy living for people who are 20–30 years old, 30–40 years old, and 40–60 years old are to be compared.

c. Human resource managers and chief executive officers have responded “yes,” “no,” or “not sure” to an attitude question. The HR and CEO responses are to be compared.

d. One-half of a sample received an incentive in a mail survey while the other half did not. A comparison of response rates is desired.

e. A researcher believes that married men will push the grocery cart when grocery shopping with their wives. How would the hypothesis be tested?

f. A manager wishes to compare the job performance of a salesperson before ethics training with the performance of that same salesperson after ethics training.

2. Perform a x2 test on the following data: a. Regulation is the best way to ensure safe products.

Agree Disagree No Opinion

Managers 58 66 8

Line Employees 34 24 10

Totals 92 90 18

b. Ownership of residence

Yes No

Male 25 20

Female 16 14

548 Part 6: Data Analysis and Presentation

3. Interpret the following computer cross-tab output including a x2 test. Variable COMMUTE is “How did you get to work last week?” Variable GENDER is “Are you male or female?” Comment on any particular problems with the analysis.

COMMUTE * GENDER Cross-Tabulation

GENDER

Female Male Total

COMMUTE At Home Count % within COMMUTE % within GENDER % of Total

6 37.5% 7.0% 4.2%

10 62.5% 17.9% 7.0%

16 100.0% 11.3% 11.3%

Bus Count % within COMMUTE % within GENDER % of Total

16 50.0% 18.6% 11.3%

16 50.0% 28.6% 11.3%

32 100.0% 22.5% 22.5%

Drive Count % within COMMUTE % within GENDER % of Total

32 65.3% 37.2% 22.5%

17 34.7% 30.4% 12.0%

49 100.0% 34.5% 34.5%

Passenger Count % within COMMUTE % within GENDER % of Total

24 72.7% 27.9% 16.9%

9 27.3% 16.1% 6.3%

33 100.0% 23.2% 23.3%

Walk Count % within COMMUTE % within GENDER % of Total

8 66.7% 9.3% 5.6%

4 33.3% 7.1% 2.8%

12 100.0%

8.5% 8.5%

Total Count % within COMMUTE % within GENDER % of Total

86 60.6%

100.0% 60.6%

56 39.4%

100.0% 39.4%

142 100.0% 100.0% 100.0%

x2 Tests

Value df Asymp. Sig. (two-sided)

Pearson Chi-Square 7.751a 4 0.101

Likelihood Ratio 7.725 4 0.102

N of Valid Cases 142

a1 cells (10.0%) have expected count less than 5. The minimum expected count is 4.73.

4. A store manager’s computer-generated list of all retail sales employees indicates that 70 percent are full-time employees, 20 percent are part-time employees, and 10 percent are fur- loughed or laid-off employees. A sample of 50 employees from the list indicates that there are 40 full-time employees, 6 part- time employees, and 4 furloughed/laid-off employees. Conduct a statistical test to determine whether the sample is representa- tive of the population.

5. Test the following hypothesis using the data summarized in the table below. Interpret your result:

H1: Internet retailers offer lower prices for Blu-ray players than do traditional in-store retailers.

Blu-ray Player

Retail Type Average

Price Standard Deviation n

Internet Retailers $109.95 $20.00 25

Multichannel Retailers $159.30 $45.00 25

6. How does an independent sample t-test differ from the following? a. one-way ANOVA b. paired-samples t-test c. a x2 test d. a Z-test for differences

7. Are t-tests or Z-tests used more often in business research? Why? 8. A sales force received some management-by-objectives training.

Are the before/after mean scores for salespeople’s job perfor- mance statistically significant at the 0.05 level? The results from

Chapter 22: Bivariate Statistical Analysis: Differences Between Two Variables 549

a sample of employees are as follows (use your computer and statistical software to solve this problem):

Name Skill

Before Skill After Name

Skill Before

Skill After

Ed 4.84 5.43 Kathy 4.00 5.00

Mark 5.24 5.51 Susie 4.67 4.50

Jason 5.37 5.42 Ron 4.95 4.40

Raj 3.69 4.50 Jen 4.00 5.95

Heidi 5.95 5.90 Matt 3.75 3.50

Donna 4.75 5.25 Doug 3.85 4.00

Rob 3.90 4.50 Bob 5.00 4.10

9. Conduct a Z-test to determine whether the following two samples indicate that the population proportions are significantly different at the 0.05 level:

Sample 1 Sample 2

Sample Proportion 0.77 0.68

Sample Size 55 46

10. In an experiment with wholesalers, a researcher manipulated perception of task difficulty and measured level of aspiration for performing the task a second time. Group 1 was told the task was very difficult, group 2 was told the task was somewhat difficult but attainable, and group 3 was told the task was easy. Perform an ANOVA on the resulting data:

Level of Aspiration (10-Point Scale)

Subjects Group 1 Group 2 Group 3

1 6 5 5

2 7 4 6

3 5 7 5

4 8 6 4

5 8 7 2

6 6 7 3

Cases 6 6 6

11. Interpret the following output examining group differences for purchase intentions. The three groups refer to consumers from three states: Illinois, Louisiana, and Texas.

Tests of Between-Subjects Effects

Dependent Variable: int2

Source Type III Sum of Squares df Mean Square F Sig.

Corrected Model 6681.746a 2 3340.873 3.227 0.043

Intercept 308897.012 1 308897.012 298.323 0.000

State 6681.746 2 3340.873 3.227 0.043

Error 148068.543 143 1035.444

Total 459697.250 146

Corrected Total 154750.289 145

aR Squared 5 0.043 (Adjusted R Squared 5 0.030)

Law

Dependent Variable: int2

95% Confidence Interval

State Mean Std. Error Lower Bound Upper Bound

IL 37.018 4.339 28.441 45.595

LA 50.357 4.965 40.542 60.172

TX 51.459 4.597 42.373 60.546

550 Part 6: Data Analysis and Presentation

RESEARCH ACTIVITIES 1. ETHICS/’NET How ethical is it to do business in different coun-

tries around the world? An international organization, Trans- parency International, keeps track of the perception of ethical practices in different countries and computes the corruption perceptions index (CPI). Visit the website and search for the latest CPI (http://www.transparency.org/policy_research/ surveys_indices/cpi). Using the data found here, test the fol- lowing research questions. a. Are nations from Europe and North America perceived to be

more ethical than nations from Asia, Africa, and South America?

b. Are there differences among the corruption indices in the past five years (between 2003 and 2008)?

2. ‘NET The Bureau of Labor Statistics of the United States Government maintains a website that contains current and his- toric data related to the United States economy. Navigate to the BLS website at http://www.bls.gov. In the “Latest Numbers” section, select the small dinosaur next to the Unemployment Rate link. Select a year and its 12-month unemployment data, and then select the most current year for comparison. What statistical tests are appropriate?

Three academic researchers investigated the idea that, in American sports, there are two segments with opposing views about the goal of competition (i.e.,

winning versus self-actualization) and the acceptable/desirable way of achieving this goal.7 Persons who believe in “winning at any cost” are proponents of sports success as a product and can be labeled new school (NS) individuals. The new school is founded on notions of the player before the team, loyalty to the highest bidder, and high- tech production and consumption of professional sports. On the other hand, persons who value the process of sports and believe that “how you play the game matters” can be labeled old school (OS) individuals. The old school emerges from old-fashioned American

notions of the team before the player, sportsmanship, and loyalty above all else, and competition simply for “love of the game.”

New school/old school was measured by asking agreement with ten attitude statements. The scores on these statements were com- bined. Higher scores represent an orientation toward old school values. For purposes of this case study, individuals who did not answer every question were eliminated from the analysis. Based on their summated scores across the 10 items, respondents were grouped into low score, middle score, and high score groups. Exhibit 22.1-1 shows the SPSS computer output of a cross- tabulation to relate the gender of the respondent (GENDER) with the new school/old school grouping (OLDSKOOL).

old School versus New School Sports Fans

CASE 22.1

CASE EXHIBIT 22.1-1

SPSS Output

OLDSKOOL * GENDER Cross-Tabulation

GENDER

Women Men Total

OLDSKOOL high Count

% within OLDSKOOL

% within GENDER

% of Total

34.6%

10.6%

3.3%

65.4%

9.2%

6.3%

100.0%

9.6%

low Count

% within OLDSKOOL

% within GENDER

% of Total

39.1%

52.9%

16.7%

60.9%

37.8%

25.9%

115

100.0%

42.6%

middle Count

% within OLDSKOOL

% within GENDER

% of Total

24.0%

36.5%

11.5%

76.0%

53.0%

36.3%

129

100.0%

47.8%

Total Count

% within OLDSKOOL

% within GENDER

% of Total

31.5%

100.0%

31.5%

185

68.5%

100.0%

68.5%

270

100.0%

(Continued )

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Chapter 22: Bivariate Statistical Analysis: Differences Between Two Variables 551

Chi-Square Tests

Value df Asymp. Sig. (two-sided)

Pearson Chi-Square 6.557a 2 .038

Likelihood Ratio 6.608 2 .037

N of Valid Cases 270

a0 cells (.0%) have expected count less than 5. The minimum expected count is 8.19.

Questions

1. Interpret the computer output. What do the results presented above indicate?

2. Is the analytical approach used here appropriate? 3. Describe an alternative approach to the analysis of the original

data. Which of these two analyses would you suggest using?

CASE EXHIBIT 22.1-1 (Continued)

SPSS Output

552 Part 6: Data Analysis and Presentation

553

APPENDIX 22A Manual Calculation of an

F-Statistic

Manual calculations are almost unheard of these days. However, understanding the calculations can be very useful in gaining a thorough understanding of ANOVA. The data in Exhibit 22A-1 are from a hypothetical packaged-goods company’s test-market experiment on pricing. Three pricing treatments were administered in four separate areas (12 test areas, A–L, were required). These data will be used to illustrate ANOVA.

Terminology for the variance estimates is derived from the calculation procedures, so an expla- nation of the terms used to calculate the F-ratio should clarify the meaning of the analysis of variance technique. The calculation of the F-ratio requires that we partition the total variation into two parts:

Total sum of squares (SST)

5 Within-group sum of squares

(SSE) 1

Between-groups sum of squares

(SSB) or

SST 5 SSE 1 SSB

SST is computed by squaring the deviation of each score from the grand mean and summing these squares:

SST 5 On i 5 1 Oc j 5 1

(X ij

2 X )2

EXHIBIT 22A-1

A Test-Market Experiment on Pricing

Sales in Units (thousands)

Regular Price, $.99

Reduced Price, $.89

Cents-Off Coupon, Regular Price

Test-Market A, B, or C 130 145 153

Test-Market D, E, or F 118 143 129

Test-Market G, H, or I 87 120 96

Test-Market J, K, or L 84 131 99

Mean X 1

5 104.75 X 2

5 134.75 X 3

5 119.25

Grand Mean X 5 119.58

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where

X 5 individual score—that is, the ith observation or test unit in the jth group X 5 grand mean n 5 number of all observations or test units in a group c 5 number of jth groups (or columns)

In our example,

SST 5 (130 2 119.58)2 1 (118 2 119.58)2 1 (87 2 119.58)2

1 (84 2 119.58)2 1 (145 2 119.58)2 1 (143 2 119.58)2

1 (120 2 119.58)2 1 (131 2 119.58)2 1 (153 2 119.58)2

1 (129 2 119.58)2 1 (96 2 119.58)2 1 (99 2 119.58)2

5 5,948.93

SSE, the variability that we observe within each group, or the error remaining after using the groups to predict observations, is calculated by squaring the deviation of each score from its group mean and summing these scores:

SSE 5 On i 5 1 Oc j 5 1

(X ij

2 X j )2

where

X 5 individual score X

j 5 group mean for the jth group

n 5 number of observations in a group c 5 number of jth groups

In our example,

SSE 5 (130 2 104.75)2 1 (118 2 104.75)2 1 (87 2 104.75)2

1 (84 2 104.75)2 1 (145 2 134.75)2 1 (143 2 134.75)2

1 (120 2 134.75)2 1 (131 2 134.75)2 1 (153 2 119.25)2

1 (129 2 119.25)2 1 (96 2 119.25)2 1 (99 2 119.25)2

5 4,148.25

SSB, the variability of the group means about a grand mean, is calculated by squaring the deviation of each group mean from the grand mean, multiplying by the number of items in the group, and summing these scores:

SSB 5 Oc j 5 1

n j (X

j 2 X )2

where

X j 5 group mean for the jth group

X 5 grand mean n

j 5 number of items in the jth group

In our example,

SSB 5 4(104.75 2 119.58)2 1 4(134.75 2 119.58)2

1 4(119.25 2 119.58)2

5 1,800.68

The next calculation requires dividing the various sums of squares by their appropriate degrees of freedom. These divisions produce the variances, or mean squares. To obtain the mean square between groups, we divide SSB by c – 1 degrees of freedom:

MSB 5 SSB

c 2 1

554 Part 6: Data Analysis and Presentation

In our example,

MSB 5 1,800.68 3 2 1

5 1,800.68

2 5 900.34

To obtain the mean square within groups, we divide SSE by cn – c degrees of freedom:

MSE 5 SSE

cn 2 c In our example,

MSE 5 4,148.25 12 2 3

5 4,148.25

9 5 460.91

Finally, the F-ratio is calculated by taking the ratio of the mean square between groups to the mean square within groups. The between-groups mean square is the numerator and the within- groups mean square is the denominator:

F 5 MSB MSE

In our example,

F 5 900.34 460.91

5 1.95

There will be c – 1 degrees of freedom in the numerator and cn – c degrees of freedom in the denominator:

c 2 1 cn 2 c

5 3 2 1

3(4) 2 3 5

2 9

In Table A.5 in the text appendix, the critical value of F at the 0.05 level for 2 and 9 degrees of freedom indicates that an F of 4.26 would be required to reject the null hypothesis.

In our example, we conclude that we cannot reject the null hypothesis. It appears that all the price treatments produce approximately the same sales volume.

The information produced from an analysis of variance is traditionally summarized in table form. Exhibits 22A-2 and 22A-3 summarize the formulas and data from our example.

EXHIBIT 22A-2

ANOVA Summary TableSource of Variation Sum of Squares

Degrees of Freedom Mean Square F-Ratio

Between groups SSB 5 Oc j 5 1

n j (X

j 2 X)2 c 2 1 MSB 5

SSB c 2 1

—

Within groups SSE 5 On i 5 1 Oc j 5 1

(X ij

2 X j )2 cn 2 c MSE 5

SSE cn 2 c

F 5 MSB MSE

Total SST 5 On i 5 1 Oc j 5 1

(X ij

2 X j )2 cn 2 1 — —

where c 5 number of groups n 5 number of observations in a group

EXHIBIT 22A-3

Pricing Experiment ANOVA Table

Source of Variation Sum of Squares

Degrees of Freedom Mean Square F-Ratio

Between groups 1,800.68 2 900.34 —

Within groups 4,148.25 9 460.91 1.953

Total 5,948.93 11 — —

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Chapter 22: Bivariate Statistical Analysis: Differences Between Two Variables 555

556

APPENDIX 22B ANOVA for Complex Experimental Designs

To test for statistical significance in a randomized block design, or RBD (see Chapter 12), another version of analysis of variance is utilized. The linear model for the RBD for an individual observa- tion is*

Y ij 5 m + a

j + b

i + ́

where

Y ij 5 individual observation on the dependent variable

m 5 grand mean

a j 5 jth treatment effect

b i 5 ith block effect

´ ij 5 random error or residual

The statistical objective is to determine whether significant differences exist among treatment means and block means. This is done by calculating an F-ratio for each source of effects.

The same logic that applies in single-factor ANOVA—using variance estimates to test for dif- ferences among means—applies in ANOVA for randomized block designs. Thus, to conduct the ANOVA, we partition the total sum of squares (SS

total ) into non-overlapping components.

SS total

5 SS treatments

1 SS blocks

1 SS error

The sources of variance are defined as follows.

Total sum of squares:

SS total

5 Or i 5 1 Oc j 5 1

(Y ij

2 Y )2

where

Y ij 5 individual observation

Y 5 grand mean

r 5 number of blocks (rows)

c 5 number of treatments (columns)

*We assume no interaction effect between treatments and blocks.

Treatment sum of squares:

SS treatments

5 Or i 5 1 Oc j 5 1

(Y j 2 Y )2

where

Y j 5 jth treatment mean

Y 5 grand mean

Block sum of squares:

SS blocks

5 Or i 5 1 Oc j 5 1

(Y i 2 Y )2

where

Y i 5 ith block mean

Y 5 grand mean

Sum of squares error:

SS error

5 Or i 5 1 Oc j 5 1

(Y ij

2 Y i 2 Y

j 2 Y )2

The SS error

may also be calculated in the following manner:

SS error

5 SS total

2 SS treatments

2 SS blocks

The degrees of freedom for SS treatments

are equal to c – 1 because SS treatments

reflects the dispersion of treatment means from the grand mean, which is fixed. Degrees of freedom for blocks are r – 1 for similar reasons. SS

error reflects variations from both treatment and block means. Thus, df 5

(r – 1)(c – 1). Mean squares are calculated by dividing the appropriate sum of squares by the corresponding

degrees of freedom. Exhibit 22B-1 is an ANOVA table for the randomized block design. It summarizes what has

been discussed and illustrates the calculation of mean squares.

EXHIBIT 22B-1

ANOVA Table for Randomized Block Designs

Source of Variation Sum of Squares Degrees of Freedom Mean Squares

Between blocks SS blocks

r 2 1 SSblocks r 2 1

Between treatments SS treatments

c 2 1 SStreatments c 2 1

Error SS error

(r 2 1)(c 2 1) SSerror (r 2 1)(c 2 1)

Total SS total

rc 2 1 —

F-ratios for treatment and block effects are calculated as follows:

F treatment

5 Mean square treatment

Mean square error

F blocks

5 Mean square blocks

Mean square error

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Chapter 22: Bivariate Statistical Analysis: Differences Between Two Variables 557

Factorial Designs There is considerable similarity between the factorial design (see Chapter 12) and the one-way analysis of variance. The sum of squares for each of the treatment factors (rows and columns) is similar to the between-groups sum of squares in the single-factor ANOVA model. Each treatment sum of squares is calculated by taking the deviation of the treatment means from the grand mean. Determining the sum of squares for the interaction is a new calculation because this source of vari- ance is not attributable to the treatment sum of squares or the error sum of squares.

ANOVA for a Factorial Experiment In a two-factor experimental design the linear model for an individual observation is

Y ijk

5m+ b i +a

j +I

ij +´

ijk

where

Y ijk 5 individual observation on the dependent variable

m 5 grand mean b

i 5 ith effect of factor B—row treatment

a j 5 jth effect of factor A—column treatment

I ij 5 interaction effect of factors A and B

´ ijk 5 random error or residual

Partitioning the Sum of Squares for a Two-Way ANOVA Again, the total sum of squares can be allocated into distinct and overlapping portions:

Sum of Sum of Sum of squares Sum of Sum of squares = squares rows + columns + squares + squares total (treatment B) (treatment A) interaction error

SS total

5 SSR treatment B

1 SSC treatment A

1 SS interaction

1 SS error

Sum of squares total:

SS total

5 Or i 5 1 Oc j 5 1 On k 5 1

(Y ijk

2 Y )2

where

Y ijk 5 individual observation on the dependent variable

Y 5 grand mean j 5 level of factor A i 5 level of factor B k 5 number of an observation in a particular cell r 5 total number of levels of factor B (rows) c 5 total number of levels of factor A (columns) n 5 total number of observations in the sample

Sum of squares rows (treatment B):

SSR treatment B

5 Or i 5 1

(Y i 2 Y )2

where

Y j 5 mean of ith treatment—factor B

558 Part 6: Data Analysis and Presentation

Sum of squares columns (treatment A):

SSC treatment A

5 Oc j 5 1

(Y j 2 Y )2

where

Y j 5 mean of jth treatment—factor A

Sum of squares interaction:

SS interaction

5 Or i 5 1 Oc j 5 1 On k 5 1

(Y ij

2 Y i 2 Y

j 2 Y )2

The above is one form of calculation. However, SS interaction

generally is indirectly computed in the following manner:

SS interaction

5 SS total

2 SSR treatment B

2 SSC treatment A

2 SS error

Sum of squares error:

SS error

5 Or i 5 1 Oc j 5 1 On k 5 1

(Y ijk

2 Y ij )2

where

Y ij 5 mean of the interaction effect

These sums of squares, along with their respective degrees of freedom and mean squares, are sum- marized in Exhibit 22B-2.

EXHIBIT 22B-2

ANOVA Table for Two-Factor Design

Source of Variation

Sum of Squares Degrees of Freedom

Mean Square F-Ratio

Treatment B SSR treatment B

r 2 1 MSR treatment B

5 SSR

treatment B

r 2 1

MSR treatment B

MS error

Treatment A SSC treatment A

c 2 1 MSC treatment A

5 SSC

treatment A

c 2 1

MSC treatment A

MS error

Interaction SS interaction

(r 2 1)(c 2 1) MS interaction

5 SS

interaction

(r 2 1)(c 2 1)

MS interaction

MS error

Error SS error

rc(n 2 1) MS error

5 SS

error

rc(n 2 1)

Total SS total

rcn 2 1

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Chapter 22: Bivariate Statistical Analysis: Differences Between Two Variables 559

After studying this chapter, you should be able to

1. Apply and interpret simple bivariate correlations

2. Interpret a correlation matrix

3. Understand simple (bivariate) regression

4. Understand the least-squares estimation technique

5. Interpret regression output including the tests of hypotheses tied to specific parameter coefficients

C H A P T E R

D o you “bring work home?” Do your family experiences and demands affect your work responsibilities? When you think about the stress you may have faced in a particular work

situation, it is easy to see how this may, in fact, be the case. For many years, there was a belief that employees universally

separated their work roles from their home/family roles. This belief generally centered on the idea that “what happens at home” doesn’t matter in the workplace. As an employee, you were simply there to perform your job, and your family was not part of those responsibilities. Likewise, the responsibilities you have with your family were not affected by work—you left those challenges and stresses “at work.” Our understanding of the work and family interface has changed substantially in recent years.

The idea that work roles and family roles could be at odds with one another is nowadays referred to as work-family con- flict (WFC).1 It is typically defined as conflict that results when the demands and responsibilities of one role “spill over” into the other role. For example, it is easy to see how a manager, simultaneously facing a project deadline and a family reunion in the same week, may allow some of his or her frustrations and stress to affect one (or both) of these work and family roles.

LEARNING OUTCOMES

Bivariate Statistical Analysis Measures of Association

Chapter Vignette:

Bringing Your Work to Your Home (and Bringing Your Home to Work)

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Introduction When business researchers develop and implement a research survey, it is often conducted with several goals in mind. Most important, however, is the goal of answering a particular research question, using the data to justify the result. Finding the “answer” is critically important in this regard. This chapter is designed to familiarize you with how bivariate business statistics are used to help accomplish this task. With bivariate analysis we often times distinguish between the independent variable (the cause) and the dependent variable (the effect). The mathematical symbol X is commonly used for an independent variable, and Y typically denotes a dependent variable.

The Basics Business research involves many different professional disciplines. For management, the ques- tions regarding conflict, employee satisfaction, and employee turnover are often key dependent variables of interest. As the chapter vignette outlines, this question can be a complex one, with many different work and family independent variables affecting work-family conflict. In market- ing, sales volume is often the dependent variable managers want to predict. Independent variables are typically marketing mix elements such as price, number of salespeople, and the amount of advertising, that are related to sales volume. In finance, earnings reports are heavily linked with stock prices.

Exhibit 23.1 shows that measurement characteristics influence which measure of associa- tion is most appropriate. The chi-square (x2) test provides information about whether two or more less-than interval variables are interrelated. For example, a x2 test between the color of the package and the product chosen for purchase provides information about the independence or interrelationship of the two variables. This chapter also describes simple correlation (Pear- son’s product-moment correlation coefficient, r) and bivariate or simple regression analysis. Correlation analysis is most appropriate for interval or ratio variables. While regression can accommodate less-than interval independent variables, the dependent variable must be con- tinuous. Over the years, psychological statisticians have developed several other techniques that demonstrate empirical association. These techniques are for advanced students who have specific needs.4

Simple Correlation Coefficient The most popular technique for indicating the relationship of one variable to another is correla- tion. A correlation coefficient is a statistical measure of covariation, or association between two variables. Covariance is the extent to which a change in one variable corresponds systematically to a change in another. Correlation can be thought of as a standardized covariance.

measure of association

A general term that refers to a number of bivariate statistical techniques used to measure the strength of a relationship between two variables.

There are numerous ways that WFC can be created for an employee. Work demands can include inflexible schedules, project timelines, and even an abusive supervisor. With regard to family demands, any number of family demands can “spill over” into the work role, to include child or elder care and dual career relationships.2

Researchers have begun to examine and explore the many different work and family characteristics (i.e. independent

variables) that can predict WFC (a dependent variable), with the goal of providing insights into the causes and consequences of this phenomenon.3 Think about your own work or school and how it interferes with your family obligations. What would be some of the demands on you that lead to WFC? How have these demands affected your job satisfaction and/or family harmony? It is not hard to see that “bringing home work” is a common problem we face in today’s society.

correlation coefficient

A statistical measure of the covariation, or association, between two at-least interval variables.

covariance

The extent to which two variables are associated systematically with each other.

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 561

562

SURVEY

THIS! The Survey This! feature contains many variables that you might think would be related to a particular outcome for a person, such as satisfaction. Based upon the variables list, do the following: (1) Choose three variables (independent variables) that you think would predict satisfaction (dependent variable); (2) conduct a bivariate correlation analy- sis for all of your selected variables—do they show the correct sign (positive or negative)? Are they significantly related? (3) Using those same independent and dependent variables, conduct a simple regression analysis. What do you find?

EXHIBIT 23.1

Bivariate Analysis—Common Procedures for Testing Association

Association for 2 Variables

Nominal

Example: Does nationality relate to color

preference?

Statistical Choice: Cross-tabulation with

�2 test

Ordinal

Example: Does importance ranking

relate to brand choice?

Statistical Choice: Cross-tabulation with �2 test or Spearman rank correlation

Interval/Ratio

Example: Does number of radio spots relate

to unit sales?

Statistical Choice: Pearson’s r or simple

regression

What is the measurement

level?

When correlations estimate relationships between continuous variables, the Pearson product-moment correlation is appropriate. The correlation coefficient, r, ranges from 21.0 to 11.0. If the value of r equals 11.0, a perfect positive relationship exists. Perhaps the two variables are one and the same! If the value of r equals 21.0, a perfect negative relationship exists. The implication is that one variable is a mirror image of the other. As one goes up, the other goes down in proportion and vice versa. No correlation is indicated if r equals 0.

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A correlation coefficient indicates both the magnitude of the linear relationship and the direc- tion of that relationship. For example, if we find that r 5 20.92, we know we have a very strong inverse relationship—that is, the greater the value measured by variable X, the lower the value measured by variable Y.

The formula for calculating the correlation coefficient for two variables X and Y is as follows:

r xy

5 r yx

5 On i 5 1

(X i 2 X )(Y

i 2 Y )

AOn i 5 1

(X i 2 X )2On

i 5 1

(Y i 2 Y )2

where the symbols X and Y represent the sample averages of X and Y, respectively. An alternative way to express the correlation formula is

r xy

5 r yx

5 s

xy2s2 x s2

where

s2 x 5 variance of X

s2 y 5 variance of Y

s xy 5 covariance of X and Y

with

s xy

5 On i 5 1

(X i 2 X )(Y

i 2 Y )

If associated values of X i and Y

i differ from their means in the same direction, their covariance will

be positive. If the values of X i and Y

i tend to deviate in opposite directions, their covariance will

be negative. The Pearson correlation coefficient is a standardized measure of covariance. Covariance coef-

ficients retain information about the absolute scale ranges so that the strength of association for scales of different possible values cannot be compared directly. Researchers find the correlation coefficient useful because they can compare two correlations without regard for the amount of variance exhibited by each variable separately.

Exhibit 23.2 illustrates the correlation coefficients and scatter diagrams for several sets of data. Notice that in the no-correlation condition, the observations are scattered rather evenly about the space. In contrast, when correlations are strong and positive, the observations lie mostly in quadrants II and IV formed by inserting new axes though X and Y . If correlation was strong and negative, the observations would lie mostly in quadrants I and III.

An Example The correlation coefficient can be illustrated with a simple example. As you might expect, research- ers today do not need to calculate correlation manually. However, the calculation process helps illustrate exactly what is meant by correlation and covariance. Consider an investigation made to determine whether the average number of hours worked in manufacturing industries is related to unemployment. A correlation analysis of the data is carried out in Exhibit 23.3.

The correlation between the two variables is 20.635, indicating an inverse (negative) rela- tionship. When number of hours goes up, unemployment comes down. This makes intuitive sense. If factories are increasing output, regular workers will typically work more overtime and new employees will be hired (reducing the unemployment rate). Both variables are probably related to overall economic conditions.

inverse (negative) relationship

Covariation in which the association between variables is in opposing directions. As one goes up, the other goes down.

‘‘Statistics are like a bikini. What they reveal is suggestive, but what they conceal is vital.’’—AARON LEVENSTEIN

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 563

Correlation, Covariance, and Causation Recall that concomitant variation is one condition needed to establish a causal relationship between two variables. When two variables covary, they display concomitant variation. This sys- tematic covariation does not in and of itself establish causality. Remember that the relationship would also need to be nonspurious and that any hypothesized “cause” would have to occur before any subsequent “effect.” For example, work experience displays a significant correlation with job performance.5 However, in a retail context, workers with more experience often get assigned to newer stores. Thus, the researcher would need to sort out to what extent age of the store may also be responsible for causing store performance.

Coefficient of Determination If we wish to know the proportion of variance in Y that is explained by X, we can calculate the coefficient of determination (R2) by squaring the correlation coefficient:

R2 5 Explained variance

Total variance

The coefficient of determination, R2, measures that part of the total variance of Y that is accounted for by knowing the value of X. In the example about hours worked and unemployment, r 5 20.635; therefore, R2 5 0.403. About 40 percent of the variance in hours worked can be explained by the variance in unemployment. As can be seen, R-squared really is just r squared!

coefficient of determination (R2)

A measure obtained by squaring the correlation

coefficient; the proportion of the total variance of a variable

accounted for by another value of another variable.

Y Y Y

X X X

r � .30 r � .80 r ��1.0

r � 0 r � �.60 r � �1.0

Low Positive Correlation

High Positive Correlation

Perfect Positive Correlation

No Correlation Moderate Negative Correlation

Perfect Negative Correlation

EXHIBIT 23.2

Scatter Diagram to Illustrate Correlation Patterns

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564 • PART SIX Data Analysis and Presentation

Correlation Matrix A correlation matrix is the standard form for reporting observed correlations between two variables. Although any number of variables can be displayed in a correlation matrix, each entry represents the bivariate relationship between a pair of variables. Exhibit 23.4 shows a correlation matrix that relates some measures of salesperson job performance to characteristics of the sales force.6

Note that the main diagonal consists of correlations of 1.00. Why is this? Simply put, any variable is correlated with itself perfectly. Had this been a covariance matrix, the diagonal would display the variance for any given variable.

correlation matrix

The standard form for reporting correlation coefficients for more than two variables.

EXHIBIT 23.3

Correlation Analysis of Number of Hours Worked in Manufacturing Industries with Unemployment Rate

Unemployment Rate (X

i )

Number of Hours Worked (Y

i ) X

i 2 X (X

i 2 X )2 Y

i 2 Y (Y

i 2 Y )2 (X

i 2 X)(Y

i 2 Y )

5.5 39.6 .51 .2601 2.71 .5041 2.3621

4.4 40.7 2.59 .3481 .39 .1521 2.2301

4.1 40.4 2.89 .7921 .09 .0081 2.0801

4.3 39.8 2.69 .4761 2.51 .2601 .3519

6.8 39.2 1.81 3.2761 21.11 1.2321 22.0091

5.5 40.3 .51 .2601 2.01 .0001 2.0051

5.5 39.7 .51 .2601 2.61 .3721 2.3111

6.7 39.8 1.71 2.9241 2.51 .2601 2.8721

5.5 40.4 .51 .2601 .09 .0081 .0459

5.7 40.5 .71 .5041 .19 .0361 .1349

5.2 40.7 .21 .0441 .39 .1521 .0819

4.5 41.2 2.49 .2401 .89 .7921 2.4361

3.8 41.3 21.19 1.4161 .99 .9801 21.1781

3.8 40.6 21.19 1.4161 .29 .0841 2.3451

3.6 40.7 21.39 1.9321 .39 .1521 2.5421

3.5 40.6 21.49 2.2201 .29 .0841 2.4321

4.9 39.8 2.09 .0081 2.51 .2601 .0459

5.9 39.9 .91 .8281 2.41 .1681 2.3731

5.6 40.6 .61 .3721 .29 .0841 .1769

X 5 4.99

Y 5 40.31

a(Xi 2 X)2 5 17.8379 a(Yi 2 Y)2 5 5.5899

a(Xi 2 X)(Yi 2 Y) 5 26.3389 r 5

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CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 565

Performance (S) was measured by identifying the salesperson’s actual annual sales volume in dollars. Notice that the performance variable has a 0.45 correlation with the workload vari- able (WL), which was measured by recording the number of accounts in a sales territory. Notice also that the salesperson’s perception of job-related tension ( JT) as measured by an attitude scale has a 20.48 correlation with performance (S). Thus, when perceived job tension is high, perfor- mance is low.

Just as in hypothesis testing, researchers are also concerned with statistical significance in corre- lation analysis. The procedure for determining statistical significance is the t-test of the significance of a correlation coefficient. Typically it is hypothesized that r 5 0, and then a t-test is performed. The logic behind the test is similar to that for the significance tests already considered. Statistical programs usually indicate the p-value associated with each correlation and/or star significant cor- relations using asterisks. The Research Snapshot “What Makes Attractiveness?” displays the way correlation matrices are often reported.

Regression Analysis Regression analysis is another technique for measuring the linear association between a depen- dent and an independent variable. Although simple regression and correlation are mathemati- cally equivalent in most respects, regression is a dependence technique where correlation is an interdependence technique. A dependence technique makes a distinction between dependent and independent variables, specifying the cause and the effect. An interdependence technique does not make this distinction and simply is concerned with how variables relate to one another.

Thus, with simple regression, a dependent (or criterion) variable,Y, is linked to an independent (or predictor) variable, X. Regression analysis attempts to predict the values of Y, a continuous, interval-scaled dependent variable from specific values of the independent variable.

EXHIBIT 23.4

Pearson Product-Moment Correlation Matrix for Salesperson Examplea

Variables S JS GE SE OD VI JT RA TP WL

Performance (S) 1.00

Job satisfaction (JS) .45b 1.00

Generalized self-esteem (GE) .31b .10 1.00

Specific self-esteem (SE) .61b .28b .36b 1.00

Other-directedness (OD) .05 2.03 2.44b 2.24c 1.00

Verbal intelligence (VI) 2.36b 2.13 2.14 2.11 2.18d 1.00

Job-related tension (JT) 2.48b 2.56b 2.32b 2.34b .26b 2.02 1.00

Role ambiguity (RA) 2.26c 2.24c 2.32b 2.39b .38b 2.05 2.44b 1.00

Territory potential (TP) .49b .31b .04 .29b .09 2.09 2.38b 2.26b 1.00

Workload (WL) .45b .11 .29c .29c 2.04 2.12 2.27c 2.22d .49b 1.00

aNumbers below the diagonal are for the sample; those above the diagonal are omitted. bp , .001 cp , .01 dp , .05

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566 • PART SIX Data Analysis and Presentation

567

RESEARCH SNAPSHOT

What are the things that make someone attractive? Many people are interested in this question. Among these are com- panies that hire people to sell fashion. The correlation matrix below was computed with SPSS. The correlations show how different characteristics related to each other. Variables include a measure of fit, meaning how well the person matches a fashion retail concept, attractiveness, weight (how overweight

someone appears), age, man- ner of dress (how modern), and personality (warm versus cold). Thus, a sample of con- sumers rated a model shown in a photo graph on those characteristics. The results reveal the following:

What Makes Attractiveness?

Correlations

Fit Attract Weight Age Modern Cold

Fit Pearson Correlation Sig. (two-tailed) N

0.831** 0.000

20.267* 0.036

0.108 0.404

20.447** 0.000

20.583 0.000

Attract Pearson Correlation Sig. (two-tailed) N

20.831** 0.000

20.275 0.030

0.039 0.766

20.428** 0.001

20.610** 0.000

Weight Pearson Correlation Sig. (two-tailed) N

20.267* 0.036

20.275* 0.030

0.082 0.528

0.262* 0.040

0.058 0.653

Age Pearson Correlation Sig. (two-tailed) N

0.108 0.404

0.039 0.755

0.082 0.528

20.019 0.082

0.104 0.423

Modern Pearson Correlation Sig. (two-tailed) N

20.447 0.000

20.428** 0.001

0.262* 0.040

20.019 0.882

0.603** 0.000

Cold Pearson Correlation Sig. (two-tailed) N

20.583** 0.000

20.610** 0.000

0.058 0.653

0.104 0.423

0.603** 0.000

*Correlation is significant at the 0.05 level (two-tailed). **Correlation is significant at the 0.01 level (two-tailed).

Thus, if the model seems to “fit” the store concept, she seems attractive. If she is judged as being overweight, she is seen as less attractive. Age is unrelated to attractiveness or fit. Modernness and perceived coldness also are associated with

lower attractiveness. Using these correlations, a retailer can help determine what employees should look like!

Correlations can be found using SPSS by navigating as shown below:

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The Regression Equation The discussion here concerns simple (bivariate) linear regression. Simple regression investigates a straight-line relationship of the type

Y 5 a 1 bX,

where Y is a continuous dependent variable and X is an independent variable that is usually continuous, although dichotomous nominal or ordinal variables can be included in the form of a dummy variable. Alpha (a) and beta (b) are two parameters that must be estimated so that the equation best represents a given set of data. These two parameters determine the height of the regression line and the angle of the line relative to horizontal. When these parameters change, the line changes. Regression techniques have the job of estimating values for these parameters that make the line fit the observations the best.

The result is simply a linear equation, or the equation for a line, where a represents the Y inter- cept (where the line crosses the Y-axis) and b is the slope coefficient. The slope is the change in Y associated with a change of one unit in X. Slope may also be thought of as rise over run, that is, how much Y rises (or falls if negative) for every one unit change in the X-axis.

Parameter Estimate Choices The estimates for α and b are the key to regression analysis. In most business research, the estimate of b is more important. The explanatory power of regression rests with b because this is where the direction and strength of the relationship between the independent and dependent variable is explained.

A Y-intercept term is sometimes referred to as a constant because a represents a fixed point. An estimated slope coefficient is sometimes referred to as a regression weight, regression coefficient, parameter estimate, or sometimes even as a path estimate. The term path estimate is a descriptive term adapted because of the way hypothesized causal relationships are often represented in diagrams:

X Y

For all practical purposes, these terms are used interchangeably. Parameter estimates can be presented in either raw or standardized form. One potential prob-

lem with raw parameter estimates is due to the fact that they reflect the measurement scale range. So, if a simple regression involved distance measured with miles, very small parameter estimates may indicate a strong relationship. In contrast, if the very same distance is measured with centime- ters, a very large parameter estimate would be needed to indicate a strong relationship.

Exhibit 23.5 provides an illustration. Suppose a researcher was interested in how much space was allocated to a specific snack food on a shelf and how it related to sales. Fifteen observations are taken from 15 different stores. The upper line represents a typical distance showing shelf space measured in centimeters. The lower line is the same distance shown in miles. The top frame shows hypothetical regression results if the independent variable is measured in centimeters. The bot- tom frame shows the very same regression results if the independent variable is measured in miles. Even though these two regression lines are the same, the parameter coefficients do not seem comparable.

simple (bivariate) linear regression

A measure of linear association that investigates straight-line relationships between a con-

tinuous dependent variable and an independent variable that is usually continuous, but can be a categorical dummy variable.

12.5 cm

Estimated Regression Line: Ŷ 5 22 1 .07X1

0.000078 miles

Estimated Regression Line: Ŷ 5 0.00014 1 .0000004X1

EXHIBIT 23.5

The Advantage of Standardized Regression Weights

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568 • PART SIX Data Analysis and Presentation

Thus, researchers often explain regression results by referring to a standardized regression coefficient (). A standardized regression coefficient provides a common metric allowing regres- sion results to be compared to one another no matter what the original scale range may have been. Due to the mathematics involved in standardization, the standardized Y-intercept term is always 0.7 The regression equation for the shelf space example would then become:

YN 5 0 1 0.16X 1

Even if the distance measures for the 15 observations were converted to some other metric (feet, meters, and so on), the standardized regression weight would still be 0.16.

Researchers use shorthand to label regression coefficients as either “raw” or “standardized.” The most common shorthand is as follows:

■ B 0 or b

0 5 raw (unstandardized) Y-intercept term; what was referred to as a above

■ B 1 or b

1 5 raw regression coefficient or estimate

■ b 1 5 standardized regression coefficients

Raw Regression Estimates (b 1 )

Raw regression weights have the advantage of retaining the scale metric—which is also their key disadvantage. Where should the researcher focus then? Should the standardized or unstandardized coefficients be interpreted? The answer to this question is fairly simple.

■ If the purpose of the regression analysis is forecasting, then raw parameter estimates must be used. This is another way of saying that the researcher is interested primarily in prediction.

Thus, when the researcher above wants to predict how much will be sold based on the amount of shelf space, raw regression coefficients must be used. For instance, the forecast for 14 centimeter of shelf space can be found as follows:

YN 5 22 1 0.07(14) 5 23.0

The same result can be found by using the equation representing the distance in miles.

Standardized Regression Estimates (b) Standardized regression estimates have the advantage of a constant scale. No matter what range of values the independent variables take on, b will not be affected. When should standardized regres- sion estimates be used?

■ Standardized regression estimates should be used when the researcher is testing explanatory hypotheses. In other words, standardized estimates are appropriate when the researcher’s pur- pose is to explain the relationship rather than to make an actual prediction.

Visual Estimation of a Simple Regression Model As mentioned above, simple regression involves finding a best-fit line, given a set of observations plotted in two-dimensional space. Many ways exist to estimate where this line should go. Estima- tion techniques involve terms such as instrumental variables, maximum likelihood, visual estima- tion, and ordinary least squares (OLS). We focus on the latter two in this text.

Suppose a researcher is interested in forecasting sales for a construction distributor (wholesaler) in Florida. The distributor believes a reasonable association exists between sales and building permits issued by counties. Using bivariate linear regression on the data in Exhibit 23.6, the researcher will be able to explain sales potential (Y ) in various counties based on the number of building permits (X ).

The data are plotted in a scatter diagram in Exhibit 23.7. In the diagram the vertical axis indicates the value of the dependent variable, Y, and the horizontal axis indicates the value of the independent variable, X. Each single point in the diagram represents an observation of X and Y at a given point in time. The values are simply points in a Cartesian plane.

standardized regression coefficient (b)

The estimated coefficient indicating the strength of relationship between an independent variable and dependent variable expressed on a standardized scale where higher absolute values indicate stronger relationships (range is from 21 to 1).

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 569

EXHIBIT 23.6

Relationship of Sales Potential to Building Permits Issued

Dealer

Y Dealer’s Sales Volume

(Thousands)

X Building Permits

1 77 86

2 79 93

3 80 95

4 83 104

5 101 139

6 117 180

7 129 165

8 120 147

9 97 119

10 106 132

11 99 126

12 121 156

13 103 129

14 86 96

15 99 108

EXHIBIT 23.7

The Best-Fit Line or Knocking Out the Pins Actual Y

for Dealer 7

Actual Y for Dealer 3

b1 (raw parameter/slope coefficient) � 0.546

b0 (Y intercept) � 31.5

Y for Dealer 3

Y = 99.8

100

110

120

130

140

0 20 40 60 80 100 120 140 160 180 200 BP

Sa le

s ($

00 0)

Y for Dealer 7

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570 • PART SIX Data Analysis and Presentation

One way to determine the relationship between X and Y is to simply visually draw the best-fit straight line through the points in the figure. That is, try to draw a line that goes through the center of the plot of points. If the points are thought of as bowling pins, the best-fit line can be thought of as the path that would on average knock over the most bowling pins. For any given value of the independent variable, a prediction can be made by selecting the dependent variable that goes along with that value. For example, if we want to forecast sales if building permits are 150, we simply follow the dotted lines shown in the exhibit to yield a prediction of about 112. The better one can estimate where the best-fit line should be, the smaller will be the error in prediction.

Errors in Prediction Any method of drawing a line can be used to perform regression. However, some methods will obviously have more errors than others. Consider our bowling ball line above. One person may be better at guessing where it should be than another. We would know who was better by determin- ing the total error of prediction.

Let’s consider error by first thinking about what value of sales would be the best guess if we had no information about any other variable. In that case, our univariate best guess would be the mean sales of 99.8. If the spot corresponding to 156 building permits (X 5 156) were predicted with the mean, the resulting error in prediction would be represented by the distance of the gray and light blue vertical line.

Once information about the independent variable is provided, we can then use the predic- tion provided by the best-fit line. In this case, our best-fit line is the “bowling ball” line shown in Exhibit 23.7. The error in prediction using this line would be indicated by the vertical line extending up from the regression line to the actual observation. Thus, it appears that at least for this observation, our prediction using the regression line has reduced the error in prediction that would result from guessing with the mean. Statistically, this is the goal of regression analysis. We would like an estimation technique that would place our line so that the total sum of all errors over all observations is minimized. In other words, no line fits better overall. Although with good guess work, visual estimation may prove somewhat accurate, we have more accurate methods of establishing a regression line.

Ordinary Least-Squares (OLS) Method of Regression Analysis The researcher’s task is to find the best means for fitting a straight line to the data. Ordinary least squares (OLS) is a relatively straightforward mathematical technique that guarantees that the result- ing straight line will produce the least possible total error in using X to predict Y. The logic is based on how much better a regression line can predict values of Y compared to simply using the mean as a prediction for all observations no matter what the value of X may be.

Unless the dependent and independent variables are perfectly related, no straight line can con- nect all observations. More technically, the procedure used in the least-squares method generates a straight line that minimizes the sum of squared deviations of the actual values from this predicted regression line. With the symbol e representing the deviations of the observations from the regres- sion line, no other line can produce less error. The deviations are squared so that positive and nega- tive misses do not cancel each other out. The OLS criterion is as follows:

On i 5 1

e 2 i

is minimum

where

e i 5 Y

i 2 YN

i (the residual)

Y i 5 actual observed value of the dependent variable

YN i 5 estimated value of the dependent variable (pronounced “Y-hat”)

n 5 number of observations

i 5 number of the particular observation

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 571

The general equation for any straight line can be represented as Y 5 b 0 1 b

1 X. If we think of

this as the true hypothetical line that we try to estimate with sample observations, the regression equation will represent this with a slightly different equation:

Y i 5 b

0 1 b

1 X

1 1 e

The equation means that the predicted value for any value of X (X i ) is determined as a function of

the estimated slope coefficient, plus the estimated intercept coefficient plus some error. The raw parameter estimates can be found using the following formulas:

b 1

naOXiYib 2 aOXibaOYib naOXi 2b 2 aOXib2

and

b 0

5 Y 2 b 1 X

where

Y i 5 ith observed value of the dependent variable

X i 5 ith observed value of the independent variable

Y 5 mean of the dependent variable

X 5 independent variable

X 5 mean of the independent variable

n 5 number of observations

b 0 5 intercept estimate

b 1 5 slope estimate (regression weight)

The careful reader may notice some similarity between the correlation calculation and the equation for b

1 . In fact, the standardized regression coefficient from a simple regression equals the

Pearson correlation coefficient for the two variables. Once the estimates are obtained, a predicted value for the dependent variable can be found for any value of X

i with this equation:

YN i 5 b

0 1 b

i X

Appendix 23A demonstrates the arithmetic necessary to calculate the parameter estimates.

Statistical Significance of Regression Model As with ANOVA, the researcher needs a way of testing the statistical significance of the regression model. Also like ANOVA, an F-test provides the answer to this question.

The overall F-test for regression can be illustrated with Exhibit 23.7. Once again, examine the colored line showing the predicted value for X 5 156, which represents the small vertical line located at the upper right of the exhibit.

1. The total vertical line including the gray and light blue segments represents the total deviation of the observation from the mean:

Y i 2 Y

2. The gray portion represents how much of the total deviation is explained by the regression line:

YN i 2 Y

3. The light blue portion represents how much of the total deviation is not explained by the regression line (also equal to e

i ):

Y i 2 YN

572 • PART SIX Data Analysis and Presentation

These three components are mathematically related because the total deviation is a sum of what is explained by the regression line and what is not explained by the regression line. This can be expressed mathematically as:

(Y i 2 Y) 5 (YN

i 2 Y ) 1 (Y

i 2 YN

i )

Deviation Deviation Total explained by unexplained by deviation 5 the regression 1 the regression (SST) (SSR) (SSE)

Just as in ANOVA, the total deviation represents the total variation to be explained. Thus, the parti- tioning of the variation into components allows us to form a ratio of the explained variation versus the unexplained variation. The corresponding abbreviation for this partitioning is:

SST 5 SSR 1 SSE

An F-test (regression), or an analysis of variance, can be applied to a regression to test the relative magnitudes of the SSR (sum of squares – regression) and SSE (sum of squared errors) with their appropriate degrees of freedom. The equation for the F-test is:

F (k 2 1)(n 2 k)

5 SSR/(k 2 1)

SSE/(n 2 k) 5

MSR MSE

where

MSR is an abbreviation for mean squared regression

MSE is an abbreviation for mean squared error

k is the number of independent variables (always 1 for simple regression)

n is the sample size

Once again, researchers today need not calculate this by hand. Regression programs will pro- duce an “ANOVA” table, which will provide the F-value and a p-value (significance level), and will generally show the partitioned variation in some form. For the sales example, the following table is obtained:

ANOVA

df SS MS F p-value

Regression (SSR) 1 3398.48911 3398.489 91.29854 0.0000003

Residual (SSE) 13 483.910892 37.22391

Total 14 3882.4

Thus, building permits explain a significant portion of the variation in sales as evidenced by the very low p-value.

The coefficient of determination, commonly referred to as R2, reflects the proportion of variance explained by the regression line. In this example, R2 can be found with this formula:

R2 5 SSR SST

5 3398.5 3882.4

5 0.875

F-test (regression)

A procedure to determine whether more variability is explained by the regression or unexplained by the regression.

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 573

The coefficient of determination may be interpreted here as stating that 87.5 percent of the variation in sales is explained by associating the variable with building permits.

What is an “acceptable” R2 value? This question is asked frequently. However, guidelines for R2 values are neither simple nor straightforward. Indeed, good and bad values for the coefficient of determination depend on so many factors that a single precise guideline is considered inappro- priate. The focus should be on the F-test of significance. However, in practice, do not expect to often see a simple regression result with an R2 anywhere near the value in this example. They will normally be considerably lower.8

Interpreting Regression Output Exhibit 23.8 displays output for the building permit problem. Most statistical computer software programs provide similar output for regression analysis. Interpreting simple regression output is a two-step process.

1. Interpret the overall significance of the model.

a. The output will include the “model F” and a significance value. When the model F is significant (low p-value), the independent variable explains a significant portion of the variation in the dependent variable.

b. The coefficient of determination or R2 can be interpreted. As mentioned earlier, this is the percentage of total variation in the dependent variable accounted for by the independent variable. Another way to think of this is as the extent to which the variances of the inde- pendent and dependent variable overlap.

2. The individual parameter coefficient is interpreted.

a. The t-value associated with the slope coefficient can be interpreted. In this case, the t-value of 9.555 is associated with a very low p-value (0.000 to three decimal places). Therefore, the slope coefficient is significant. For simple regression, the p-value for the model F and for the t-test of the individual regression weight will be the same.

EXHIBIT 23.8

Simple Regression Results for Building Permit Example R R Square

Adjusted R Square

Std. Error of the Estimate

.936(a) .875 .866 6.10114

a Predictors: (Constant), Permits

ANOVA(b)

Sum of Squares df Mean Square F Sig.

Regression 91.299 .000(a) Residual

3398.489 37.224

Total

3398.489 483.911

3882.400

1 13 14

a Predictors: (Constant), Permits b Dependent Variable: Sales

Coefficients(a)

Model Unstandardized

Coefficients: Standardized Coefficient:

B Std. Error Beta (�) t Sig.

1.000 (Constant) 4.304 0.001 Permits

31.502 0.546

7.319 0.057 0.936 9.555 0.000

a Dependent Variable: Sales

1. Is model significant?

2. Interpret parameter estimates?

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574 • PART SIX Data Analysis and Presentation

b. A t-test for the intercept term (constant) is also provided. However, this is seldom of inter- est since the explanatory power rests in the slope coefficient.

c. If a need to forecast sales exists, the estimated regression equation is needed. Using the raw coefficients, the estimated regression line is

YN 5 31.5 1 0.546X

d. The regression coefficient (slope) indicates that for every building permit issued, sales increase 0.546. Moreover, the standardized regression coefficient of 0.936 would allow the researcher to compare the explanatory power of building permits versus some other potential independent variable. For simple regression, b

1 equals r.

Plotting the OLS Regression Line To draw a regression line on the scatter diagram, only two predicted values of Y need to be plotted. The data for two dealers is used to illustrate how this is done:

Dealer 7 (actual Y value 5 129):Y 7

N 5 31.5 1 0.546(165) 5 121.6

Dealer 3 (actual Y value 5 80):Y 3

N 5 31.5 1 0.546(95) 5 83.4

Using the data for Dealer 7 and Dealer 3, we can draw a straight line connecting the points 121.6 and 83.4. Exhibit 23.9 shows the regression line.

To determine the error (residual) of any observation, the predicted value of Y is first calculated. The predicted value is then subtracted from the actual value. For example, the actual observation

EXHIBIT 23.9

OLS Regression Line

Actual Y for Dealer 7

Actual Y for Dealer 3

160

150

140

130

120

110

100

70 80 11010090 120 130 140 150 160 170 180 190

Y for Dealer 3

Y for Dealer 7

‘‘Forecasting is like trying to drive a car blindfolded and following directions given by a person who is looking out the back window.’’—ANONYMOUS

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CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 575

576

America is obsessed with weight control. Thin seems to stay in and the fight to get thin is a multibillion dollar business. Recall in an earlier Research Snapshot correlations between factors related to attractiveness were discussed. What if the following hypothesis were tested?

H1: Perceptions that a female model is overweight are related negatively to perceptions of attractiveness.

Using the scales from the earlier Snapshot, this can be tested with a simple regression. The results can be summarized as shown here:

Size and Weight

Model Sum of Squares df Mean Square F Sig.

1 Regression 9.228 1 9.227 4.914 0.030

Residual 112.660 60 1.877

Total 121.8870968 61

Model Unstandardized Coefficients B

Std. Error Standardized Coefficients  t Sig.

1 (Constant) 4.413 0.952 4.636 0.00002

3113 20.582 0.262 20.275 22.216 0.030

N C/

D ig

ita l V

is io

n/ G

et ty

Im ag

The results support the hypothesis. The b 5 20.275 is both in the expected direction (negative) and significant (p , 0.05).

Therefore, if respondents perceived someone as overweight, they likewise saw the person as less attractive.

for dealer 9 is 97, and the predicted value is 96.5; thus only a small margin of error, e 5 0.5, is involved in this regression line:

e 9

5 Y 9

2 YN 9

5 97 2 96.5 5 0.5

where

Y 9

N 5 31.5 1 .546(119)

Simple Regression and Hypothesis Testing The explanatory power of regression lies in hypothesis testing. Regression is often used to test relational hypotheses. For example, from the chapter vignette, simple regression could be used to test a hypothesis relating WFC to job satisfaction.

H1: Employees experiencing higher levels of WFC will report lower job satisfaction.

In the sales example, the regression addresses a hypothesis linking permits to sales.

H1: Sales are positively related to the number of building permits.

RESEARCH SNAPSHOT

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The outcome of the hypothesis test involves two conditions that must both be satisfied.

1. The regression weight must be in the hypothesized direction. Positive relationships require a positive coefficient and negative relationships require a negative coefficient.

2. The t-test associated with the regression weight of the coefficient must be significant.

If both of these conditions are satisfied, the hypothesis would be supported.

SUMMARY

1. Apply and interpret simple bivariate correlations. This chapter covers two approaches for studying relationships among two at-least interval variables. A bivariate correlation is an index that displays how much two variables covary. Another way to think of correlation is as a standardized measure of covariance. When two variables display a correlation of 1.0, they are perfectly cor- related. That means that they have no unique variance. Essentially, they are one and the same. When two variables are correlated 21.0 they are perfectly negatively correlated. In this sense they are mirror images of one another. Thus, correlations can range between 21.0 and 1.0. Correla- tions near 0 indicate a lack of relationship between two variables.

2. Interpret a correlation matrix. A correlation matrix presents all possible bivariate correlations among a set of variables. The statistical significance of each variable can be tested with a t-test. Low p-values for this test indicate significant correlations. Patterns of strong correlations among variables indicate variables that share variance in common.

3. Understand simple (bivariate) regression. Simple linear regression investigates a straight-line relationship between one dependent variable and one independent variable. The regression can be done intuitively by plotting a scatter diagram of the X and Y points and drawing a line to fit the observed relationship. OLS estimation mathematically determines the best-fitting regression line for the observed data. The line determined by this method may be used to forecast values of the dependent variable, given a value for the independent variable. The line’s goodness-of-fit may be evaluated with a variant of the analysis of variance (ANOVA) technique or by calculating the coefficient of determination.

4. Understand the least-squares estimation technique. OLS is an estimation technique that minimizes the least-squared error for all observations. Regression models are evaluated based on how much variance they explain. Models with a high SSR relative to SST or SSE explain more variance in the dependent variable. SSR represents the proportion of total deviation from the mean among observations that can be explained by the regression line. SSE, the sum of squares error, represents the amount of deviation from the mean for observations that is not accounted for by the regression line. OLS fits the line to minimize SSE.

5. Interpret regression output including the tests of hypotheses tied to specific parameter coefficients. Regression results are interpreted in a two-step process. First, the model’s significance is evaluated. The model F-ratio, which is a ratio of SSR to SSE, is a key statistic. A significant F-ratio means that the independent variable explains a significant portion of the variance in the dependent variable. Second, the individual parameter coefficients are evaluated. When the regression is run for forecasting purposes, the raw parameter coefficients are most useful. When the regression is run for explanatory purposes, the standardized regression weight (b) is most useful.

KEY TERMS AND CONCEPTS coefficient of determination (R2), 564 correlation coefficient, 561 correlation matrix, 565

covariance, 561 F-test (regression), 573 inverse (negative) relationship, 563

measure of association, 561 simple (bivariate) linear regression, 568 standardized regression coefficient (b), 569

CHAPTER 23 Bivariate Statistical Analysis: Measures of Association • 577

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. What is covariance? 2. How are covariance and correlation different? 3. How does a researcher determine if a correlation coefficient is

significant? 4. The management of a regional bus line thought the company’s

cost of gas might be correlated with its passenger/mile ratio. The data and a correlation matrix follow. Comment.

Total Gas

Year Expenditures Passengers/Miles

1 56.5 8.37

2 59.4 8.93

3 63.0 9.15

4 65.6 9.79

5 89.0 11.20

Year Expenditures Miles

Year (r) 1.00000 0.87016 0.95127

p-value 0.00000 0.05510 0.01280

Price (r) 0.87016 1.00000 0.97309

p-value 0.05510 0.00000 0.00530

Mile (r) 0.95127 0.97309 1.00000

p-value 0.01280 0.00530 0.00000

5. Interpret the following data: a. YN 5 5.0 1 .30X

Where the dependent variable equals turnover intentions for line managers and the independent variable equals number of employees supervised.

b. YN 5 250 2 4.0X 1

’NET Where the dependent variable is the number of hits on a new banner ad and the independent variable is the number of weeks the ad has run.

6. What are some different terms used to refer to the slope coef- ficient estimated in regression analysis?

7. The following ANOVA summary table is the result of a regression of sales on year of sales. Is the relationship statistically significant at the 0.95 significance level? Fill in the value for Sum of Squares in the SST row. Comment.

Source of Variation

Sum of Squares d.f.

Mean Square F-Value p-Value

SSR 605,370,750 1 605,370,750 3.12 0.115

SSE 1,551,381,712 8 193,922,714

SST 9

8. Address the following questions about regression analysis: a. Define simple linear regression. b. When is it most appropriate to rely on raw parameter

coefficients and when is it most appropriate to rely on standardized parameter coefficients?

c. Why is the Y-intercept estimate equal to 0 for standardized estimates?

d. What are the steps in interpreting a regression model? 9. The following table gives a basketball team’s season-ticket sales,

percentage of games won, and number of active alumni for the years 2002–2011.

Year Season-Ticket

Sales Percentage of Games Won

Number of Active Alumni

2002 4,995 40 NA

2003 8,599 54 3,450

2004 8,479 55 3,801

2005 8,419 58 4,000

2006 10,253 63 4,098

2007 12,457 75 6,315

2008 13,285 36 6,860

2009 14,177 27 8,423

2010 15,730 63 9,000

2011 15,805 70 9,500

a. Compute a correlation matrix for the variables. A software statistical package is recommended. Interpret the correlation between each pair of variables.

b. Estimate a regression model for sales 5 percentage of games won.

c. Estimate a regression model for sales 5 number of active alumni.

d. If sales is the dependent variable, which of the two independent variables do you think explains sales better? Explain.

10. Are the different forms of consumer installment credit in the following table highly correlated?

Debt Outstanding (millions of dollars)

Year Gas

Cards

Travel and Entertainment

Cards

Bank Credit Cards

Retail Cards

Total Credit Cards

Total Installment

Credit

1 $39 $61 $828 $9,400 $11,228 $79,428

2 1,119 76 1,312 10,200 12,707 87,745

3 1,298 110 2,639 10,900 14,947 98,105

4 1,650 122 3,792 11,500 17,064 102,064

5 1,804 132 4,490 13,925 20,351 111,295

6 1,762 164 5,408 14,763 22,097 127,332

7 1,832 191 6,838 16,395 25,256 147,437

8 1,823 238 8,281 17,933 28,275 156,124

9 1,893 273 9,501 18,002 29,669 164,955

10 1,981 238 11,351 19,052 32,622 185,489

11 2,074 284 14,262 21,082 37,702 216,572

578 Part 6: Data Analysis and Presentation

11. A manufacturer of disposable washcloths/wipes told a retailer that sales for this product category closely correlated with sales of disposable diapers. The retailer thought he would check this out for his own sales-forecasting purposes. The researcher says,

“Disposable washcloths/wipes sales can be predicted with knowl- edge of disposable diaper sales.” Is this the right thing to say?

12. Explain how OLS determines where a regression line should be placed among a plot of observations.

1. ’NET The Federal Reserve Bank of St. Louis maintains a data- base called FRED (Federal Reserve Economic Data). Navigate to the FRED database at http://www.stls.frb.org/fred/index. html. Randomly select a five-year period between 1990 and 2010 and then find the correlation between average U.S. employment in retail trade and U.S. employment in wholesale trade. Which statistical test is appropriate?

2. ’NET/ETHICS Go to http://www.transparency.org. Find the corruption perception indices for 2010. Then, go to http://www. geert-hofstede.com/hofstede_dimensions.php. Create a data set that includes the corruption perception indices for at least 15 countries and the score for one of the Hofstede cultural value dimensions. Conduct a regression and interpret the relationship between corruption perceptions and your chosen cultural dimension.

RESEARCH ACTIVITIES

CarCare is considering expanding its operations beyond the United States. The company wants to know whether it should target countries with con-

sumers who tend to have a positive attitude toward their current cars. It has gathered data on U.S. and German car owners. The data are included in the “car” data set that can be viewed on the website at www.cengage.com/marketing/zikmund/ (car.sav or car.xls) or available from your instructor. Using the data, conduct a correla- tion and simple regression analysis using spending as the dependent

variable and attitude toward the current car as the independent variable.

Questions 1. Test the following hypothesis: Attitude toward one’s car is

related positively to spending for car-care products. 2. Would you recommend CarCare do more research to identify

nations with relatively favorable attitudes toward the cars they own?

International Operations at CarCare Inc.

CASE 23.1

Chapter 23: Bivariate Statistical Analysis: Measures of Association 579

580

APPENDIX 23A Arithmetic Behind OLS

With simple arithmetic, we can solve for the parameter estimates using the OLS equations. Here, the data from Exhibit 23.6 are used. The different pieces of the equations are calculated and shown in Exhibit 23a.1. To estimate the relationship between the distributor’s sales to a dealer and the number of building permits, we insert values from the table as shown below:

b 1

5 nA o XiYiB 2 A o XiB A o YiB

nA o X 2i B 2 A o XiB2

b 1

5 15(193,345) 2 2,806,875

15(245,759) 2 3,515,625

5 0.546

b 0

5 Y 2 b 1 X

5 99.8 2 0.546(125) 5 31.5

EXHIBIT 23A-1

Least-Squares Computation Y Y 2 X X 2 XY

1 77 5,929 86 7,396 6,622

2 79 6,241 93 8,649 7,347

3 80 6,400 95 9,025 7,600

4 83 6,889 104 10,816 8,632

5 101 10,201 139 19,321 14,039

6 117 13,689 180 32,400 21,060

7 129 16,641 165 27,225 21,285

8 120 14,400 147 21,609 17,640

9 97 9,409 119 14,161 11,543

10 106 11,236 132 17,424 13,992

11 99 9,801 126 15,876 12,474

12 121 14,641 156 24,336 18,876

13 103 10,609 129 16,641 13,287

14 86 7,396 96 9,216 8,256

15 99 9,801 108 11,664 10,692

oY 5 1,497 oY 2 5 153,283 oX 5 1,875 oX 2 5 245,759 oXY 5 193,345

Y 5 99.8 X 5 125

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The formula YN 1 5 31.5 1 0.546X

1 is the regression equation used for the prediction of the

dependent variable. Suppose the wholesaler is considering opening a new dealership in an area where the number of building permits equals 89. We would need to compute a predicted value for X 5 89. Sales in this area may be forecasted as

YN 5 31.5 1 0.546(X)

5 31.5 1 0.546(89)

5 31.5 1 48.6

5 80.1

Thus, the distributor may expect sales of 80.1 (or $80,100) in this new area. Calculation of the correlation coefficient gives an indication of how accurate the predictions

are. In this example the correlation coefficient is r 5 0.94 and the coefficient of determination is R2 5 0.88.

Chapter 23: Bivariate Statistical Analysis: Measures of Association 581

582

After studying this chapter, you should be able to

1. Understand what multivariate statistical analysis involves and know the two types of multivariate analysis

2. Interpret results from multiple regression analysis

3. Interpret results from multivariate analysis of variance (MANOVA)

4. Interpret basic exploratory factor analysis results

5. Know what multiple discriminant analysis can be used to do

6. Understand how cluster analysis can identify market segments

C H A P T E R

A s humans, we long to relive the past. This yearning to hold on to the past is a common psychological experience.1 The psychology of consumption is of interest to many people who are not psychologists,

however. The fact is, nostalgia sells, and business researchers are very interested in understanding exactly what nostalgia is, who is most prone to react to it, and how it contributes to business success.

When a boomer or Gen Xer walks through the toy store, he or she is likely to feel right at home. Toy compa- nies like Hasbro have realized that adults buy toys for kids to enjoy. Grown-up consumers like to buy things they feel good about. Thus, the toy shelves are filled with throwback versions of G.I. Joe, Barbie, and even the Teenage Mutant Ninja Turtles.2 The game shelves are filled with classic ver- sions of familiar games like Risk, Stratego, and Monopoly.3

Not to be outdone, other marketers are also counting on nostalgic consumers. Appliance companies have turned to retro designs with classic 1950s versions of toasters, blenders, and even ovens.4 Advertisers are also using nos- talgia to produce more effective sales appeals. Among others, Coca-Cola has used nostalgic advertising to help consumers relive the past.5

This trend has stayed strong recently, as pointed out by Janet Hsu, President of Sanrio Global Consumer Products, who stated that, “Inspirational products will be a major trend in 2009 as consumers gravitate towards purchasing products that provide

LEARNING OUTCOMES

Multivariate Statistical Analysis

Chapter Vignette:

Cowabunga Never Goes Out of Style

P Ph

ot o/

Ta m

m ie

A rr

oy o

Introduction If only business problems were really as simple as most textbook examples! Most coursework involves solving problems that have a definite answer. They are usually relatively well-defined problems in which the information provided in the problem can be used to produce one correct solution.

Unfortunately, in the real world, most business problems are ill-defined. Not only do they not have a definite answer, but generally information needs to be generated and massaged before any solution can be obtained. Therefore, most business research studies involve many variables that must be organized for meaning. As researchers become increasingly aware of the complex nature of busi- ness problems, they gain a greater appreciation for more sophisticated approaches to data analysis. This chapter provides an introduction to some forms of what are known as multivariate data analysis.

What Is Multivariate Data Analysis? The preceding chapters have addressed univariate and bivariate analyses. Research that involves three or more variables, or that is concerned with underlying dimensions among multiple variables, will involve multivariate statistical analysis. Multivariate statistical methods analyze multiple variables or even multiple sets of variables simultaneously. How do we know when someone has experienced nostalgia and whether or not the experience has altered behavior? Nostalgia itself is a latent con- struct that involves multiple indicators that together represent nostalgia. As such, the measurement and outcomes of nostalgia lend themselves well to multivariate analysis.9 Likewise, many other business problems involve multivariate data analysis including most employee motivation research, customer psychographic profiles, and research that seeks to identify viable market segments.

The “Variate” in Multivariate Another distinguishing characteristic of multivariate analysis is the variate. The variate is a math- ematical way in which a set of variables can be represented with one equation. A variate is formed as a linear combination of variables, each contributing to the overall meaning of the variate based upon an empirically derived weight. Mathematically, the variate is a function of the measured variables involved in an analysis:

V k

5 f(X 1 , X

2 , . . . , X

m )

V k is the kth variate. Every analysis could involve multiple sets of variables, each represented by

a variate. X 1 to X

m represent the measured variables.

Here is a simple illustration. Recall that constructs are distinguished from variables by the fact that multiple variables are needed to measure a construct. Let’s assume we measured nostalgia with five questions on our survey. With these five variables, a variate of the following form could be created:

V k

5 L 1 X

1 1 L

2 X

2 1 L

3 X

3 1 L

4 X

4 1 L

5 X

‘‘The essence of mathematics is not to make simple things complicated, but to make complicated things simple.’’ —S. GuddER

comfort and emotional connection. Evergreen and nostalgic products will continue to sell well . . . .”6

How can organizations better integrate nostalgia into their business plans? Researchers are working on numerous issues related to nostalgia:

■ How can nostalgia be measured? ■ What emotions is nostalgia associated with?7

■ Can market segments be defined based on the type and amount of nostalgia experienced?

■ What happens to consumers when they experience nostalgia?

■ What makes a nostalgic consumer different from one who does not experience nostalgia?

■ What are the positive outcomes for the business when con- sumer nostalgia increases?

Nostalgia is a complex experience involving multiple thoughts and feelings. The complexity makes nostalgia somewhat diffi- cult to study. Multivariate research procedures can help address these questions as they consider the effects of multiple variables simultaneously. However, it seems that nostalgic thoughts mean good vibes for the marketer.8 Cowabunga!

variate

A mathematical way in which a set of variables can be represented with one equation.

CHAPTER 24 Multivariate Statistical Analysis • 583

584

V k represents the score for nostalgia, X

1 to X

5 represent the observed scores on the five scale items

(survey questions) that are expected to indicate nostalgia, and L 1 to L

5 are parameter estimates much

like regression weights that suggest how highly related each variable is to the overall nostalgia score. While this equation might appear a little intimidating, don’t worry! We do not have to manu-

ally calculate these scores. We’ll rely on the computer to do the heavy lifting. However, this type of relationship is common to multivariate procedures.

Classifying Multivariate Techniques Exhibit 24.1 presents a very basic classification of multivariate data analysis procedures. Two basic groups of multivariate techniques are dependence methods and interdependence methods.

Dependence Techniques When hypotheses involve distinction between independent and dependent variables, dependence techniques are needed. For instance, when we hypothesize that nostalgia is related positively to purchase intentions, nostalgia takes on the character of an independent variable and purchase intentions take on the character of a dependent variable. Predicting the dependent variable “sales” on the basis of numerous independent variables is a problem frequently investigated with depen- dence techniques. Multiple regression analysis, multiple discriminant analysis, multivariate analysis of variance, and structural equations modeling are all dependence methods.

Interdependence Techniques When researchers examine questions that do not distinguish between independent and dependent variables, interdependence techniques are used. No one variable or variable subset is to be pre- dicted from or explained by the others. The most common interdependence methods are factor analysis, cluster analysis, and multidimensional scaling. A manager might utilize these techniques to

dependence techniques

Multivariate statistical techniques that explain or

predict one or more dependent variables.

interdependence techniques

Multivariate statistical techniques that give meaning

to a set of variables or seek to group things together; no distinction is made between dependent and independent

variables.

SuRVEY

THIS! Our survey includes data that can best be analyzed with multivariate techniques. Take a look at the survey questions that deal with sat- isfaction with the business school experience.

When reading the chapter, consider these questions and how they fit with the tech- niques described. When you have finished the chapter: 1. Run a factor analysis on the eight

questions from “Teacher’s Knowledge of Topics” through “Your Overall Academic Performance.” a. How many factors are retained? b. What would you “name” these factors? c. Create a summated scale for each factor.

2. Run a multiple regression analysis with the “Overall Experience” question as the dependent measure and the summed scale(s) as the independent measure(s). Also include sex of the respondent as an independent variable (dummy variable) in your regression. Interpret the results: a. Is the overall model significant? b. Which of the independent variables are significant? c. How much variance in “Overall Experience” is explained by the predictor variables? d. Which of the independent variables is most important in determining satisfaction with the “Overall Experience”?

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determine which employee motivation items tend to group together (factor analysis), to identify profitable customer market segments (cluster analysis), or to provide a perceptual map of cities being considered for a new plant (multidimensional scaling).

Influence of Measurement Scales As in other forms of data analysis, the nature of the measurement scales will determine which mul- tivariate technique is appropriate for the data. Exhibit 24.2 and Exhibit 24.3 show that selection of a multivariate technique requires consideration of the types of measures used for both independent

EXHIBIT 24.1

Which Multivariate Approach Is Appropriate?All multivariate

methods

Does at least one dependent variable

exist?

Yes No

Interdependence methods

Dependence methods

One dependent variable

Multiple independent

and dependent variables

Several dependent variables

Metric Nonmetric

Multiple regression analysis

Multiple discriminant

analysis

Metric Nonmetric

Conjoint analysis

Metric

How many variables are dependent?

Dependence methods

Multivariate analysis of variance

Structural Equation Modeling

EXHIBIT 24.2

Which Multivariate Dependence Technique Should I Use?

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CHAPTER 24 Multivariate Statistical Analysis • 585

and dependent sets of variables. These exhibits refer to nominal and ordinal scales as nonmetric and interval and ratio scales as metric.

Analysis of Dependence Multivariate dependence techniques are variants of the general linear model (GLM). Simply, the GLM is a way of modeling some process based on how different variables cause fluctuations from the average dependent variable. Fluctuations can come in the form of group means that differ from the overall mean as in ANOVA or in the form of a significant slope coefficient as in regression. The basic idea can be thought of as follows:

YN i 5 m 1 DX 1 DF 1 DXF

Here, m represents a constant, which can be thought of as the overall mean of the dependent variable, DX and DF represent changes due to main effect independent variables (such as experi- mental variables) and blocking independent variables (such as covariates or grouping variables), respectively, and DXF represents the change due to the combination (interaction effect) of those variables. Realize that Y

i in this case could represent multiple dependent variables, just as X and F

could represent multiple independent variables. Multiple regression analysis, n-way ANOVA, and MANOVA represent common forms that the GLM can take.

Multiple Regression Analysis Multiple regression analysis is an extension of simple regression analysis allowing a metric depen- dent variable to be predicted by multiple independent variables. Chapter 23 illustrated simple linear regression analysis with an example explaining a construction dealer’s sales volume with the number of building permits issued. Thus, one dependent variable (sales volume) is explained by one independent variable (number of building permits). Yet reality is more complicated and several additional factors probably affect construction equipment sales. Other plausible indepen- dent variables include price, seasonality, interest rates, advertising intensity, consumer income, and

Metric Nonmetric

Factor analysis

Cluster analysis

Metric multidimensional

scaling

Nonmetric multidimensional

scaling

Are inputs metric?

Interdependence methods

EXHIBIT 24.3

Which Multivariate Interdependence Technique Should I Use?

multiple regression analysis

An analysis of association in which the effects of two or more independent variables

on a single, interval-scaled dependent variable are

investigated simultaneously.

general linear model (GLM)

A way of explaining and predicting a dependent

variable based on fluctuations (variation) from its mean. The

fluctuations are due to changes in independent variables.

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586 • PART SIX Data Analysis and Presentation

other economic factors in the area. The simple regression equation can be expanded to represent multiple regression analysis:

Y i 5 b

0 1 b

1 X

1 1 b

2 X

2 1 b

3 X

3 1 Á 1 b

n X

n 1 e

Thus, as a form of the GLM, dependent variable predictions (Ŷ ) are made by adjusting the constant (b

0 ), which would be equal to the mean if all slope coefficients are 0, based on the slope coefficients

associated with each independent variable (b 1 , b

2 , . . . , b

n ).10

Less-than interval (nonmetric) independent variables can be used in multiple regression. This can be done by implementing dummy variable coding. A dummy variable is a variable that uses a 0 and a 1 to code the different levels of dichotomous variable (for instance, residential or commercial building permit). Multiple dummy variables can be included in a regression model. For example, dummy cod- ing is appropriate when data from two countries are being compared. Suppose the average labor rate for automobile production is included in a sample taken from respondents in the United States and in South Korea. A response from the United States could be assigned a 0 and responses from South Korea could be assigned a 1 to create a country variable appropriate for use with multiple regression.

A Simple Example Assume that a toy manufacturer wishes to explain store sales (dependent variable) using a sample of stores from Canada and Europe. Several hypotheses are offered:

H1: Competitor’s sales are related negatively to our firm’s sales. H2: Sales are higher in communities that have a sales office than when no sales office is present. H3: Grammar school enrollment in a community is related positively to sales.

Competitor’s sales is how much the primary competitor sold in the same stores over the same time period. Both the dependent variable and the competitor’s sales are ratio variables measured in euros (Canadian sales were converted to euros). The presence of a sales office is a categorical variable that can be represented with dummy coding (0 5 no office in this particular community, 1 5 office in this community). Grammar school enrollment is also a ratio variable simply represented by the number of students enrolled in elementary schools in each community (in thousands).11 A sample of 24 communities is gathered and the data are entered into a regression program to produce the following results:

Regression equation: Ŷ 5 102.18 1 0.387X 1 1 115.2X

2 1 6.73X

Coefficient of multiple determination (R2) 5 0.845

F-value 5 14.6; p , 0.05

Note that all the signs in the equation are positive. Thus, the regression equation indicates that sales are positively related to X

1 , X

2 , and X

3 . The coefficients show the effect on the depen-

dent variable of a one-unit increase in any of the independent variables. The value or weight, b 1 ,

associated with X 1 is 0.387. Thus, a one-unit increase ($1,000) in competitors’ sales volume (X

1 )

in the community is actually associated with an increase of $387 in the toy manufacturer’s sales (0.387 3 $1,000 5 $387). The value of b

2 5 115.2, which indicates that an increase of $115,200

(115.2 thousand) in toy sales is expected with each additional unit of X 2 . Thus, it appears that

having a company sales office in a community is associated with a very positive effect on sales. Grammar school enrollments also may help predict sales. An increase of one unit of enrollment (1,000 students) indicates a sales increase of $6,730.

Because the effect associated with X 1 is positive, H1 is not supported; as competitor sales

increase, our sales increase as well. The effects associated with H2 and H3 are also positive, which is in the hypothesized direction. Thus, if the coefficients are statistically significant, H2 and H3 will be supported.

Regression Coefficients in Multiple Regression Recall that in simple regression, the coefficient b

1 represents the slope of X on Y. Multiple regres-

sion involves multiple slope estimates, or regression weights. One challenge in regression models is to understand how one independent variable affects the dependent variable, considering the

dummy variable

The way a dichotomous (two group) independent variable is represented in regression analysis by assigning a 0 to one group and a 1 to the other.

CHAPTER 24 Multivariate Statistical Analysis • 587

effect of other independent variables. When the independent variables are related to each other, the regression weight associated with one independent variable is affected by the regression weight of another. Regression coefficients are unaffected by each other only when independent variables are totally independent.

Conventional regression programs can provide standardized parameter estimates, b 1 , b

2 , and

so on, that can be thought of as partial regression coefficients.The correlation between Y and X 1 ,

controlling for the correlation that X 2 has with the Y, is called partial correlation. Consider a stan-

dardized regression model with only two independent variables:12

Y 5 b 1 X

1 1 b

2 X

2 1 e

The coefficients b 1 and b

2 are partial regression coefficients, which express the relationship

between the independent variable and dependent variable taking into consideration that the other variable also is related to the dependent variable. As long as the correlation between independent variables is modest, partial regression coefficients adequately represent the relationships. When the correlation between two independent variables becomes high, the regression coefficients may not be reliable, as illustrated in the Research Snapshot “Too Much of a Good Thing!”.

When researchers want to know which independent variable is most predictive of the depen- dent variable, the standardized regression coefficient (b) is used. One huge advantage of b is that it provides a constant scale. In other words, the bs are directly comparable. Therefore, the greater the absolute value of the standardized regression coefficient, the more that particular independent vari- able is responsible for explaining the dependent variable. For example, suppose in the toy example above, the following standardized regression coefficients were found:

b 1

5 0.10 b

2 5 0.30

b 3

5 0.10

The resulting standardized regression equation would be

Y 5 0.10X 1

1 0.30X 2

1 0.10X 3

1 e i

Using standardized coefficients, the researcher concludes that the relationship between competi- tor’s sales (X

1 ) and company sales (Y ) is the same strength as the relationship between grammar

school enrollment (X 3 ) and company sales. Perhaps more important, though, the conclusion can

also be reached that the relationship between having a sales office in the area (X 2 ) and sales is three

times as strong as the other two relationships. Thus, management may wish to place more emphasis on locating sales offices in major markets.

R 2 in Multiple Regression The coefficient of multiple determination in multiple regression indicates the percentage of variation in Y explained by the combination of all independent variables. For example, a value of R2 5 0.845 means that 84.5 percent of the variance in the dependent variable is explained by the independent variables. If two independent variables are truly independent (uncorrelated with each other), the R2 for a multiple regression model is equal to the separate R2 values that would result from two separate simple regression models. More typically, the independent variables are at least moderately related to one another, meaning that the model R2 from a multiple regression model will be less than the separate R2 values resulting from individual simple regression models. This reduction in R2 is pro- portionate to the extent to which the independent variables exhibit multicollinearity.

Statistical Significance in Multiple Regression Following from simple regression, an F-test is used to test statistical significance by comparing the variation explained by the regression equation to the residual error variation. The F-test allows for testing of the relative magnitudes of the sum of squares due to the regression (SSR) and the error sum of squares (SSE).

F 5 (SSR)/k

(SSE)/(n 2 k 2 1) 5

MSR MSE

partial correlation

The correlation between two variables after taking into account the fact that

they are correlated with other variables too.

588 • PART SIX Data Analysis and Presentation

589

RESEARCH SNAPSHOT

Researchers often test hypotheses by examining regression coefficients. Thus, we are often looking for correlations, some- times in all the wrong places. Financial data can be problematic to analyze. Consider the case of a financial manager trying to analyze gross margin (dependent variable = margin per employee) using the following independent variables:

■ Average sales per square foot per quarter ■ Average labor costs per week ■ Years of experience for the manager ■ Job performance rating for the previous year (100-point scale)

Regression results can be obtained in SPSS by clicking on ANALYZE, REGRESSION, and then LINEAR. The VIF column (which is the selection that calculates variance inflation factors) must be requested by clicking on STATISTICS and then checking COLLINEARITY dIAGNOSTICS. After doing so, the following results are obtained. For the overall model,

Too Much of a Good Thing!

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ANOVA(b)

Model Sum of Squares df Mean Square F Sig.

1 Regression 142566.5332 4 35641.6333 13.56899 .0000008

Residual 91934.43848 35 2626.698242

Total 234500.9717 39

A Predictors: (constant), performance, experience, labor, sales B Dependent Variable: margin

Coefficients(a)

Model Unstandardized Coefficients B Std. Error

Standardized Coefficients Beta t Sig. VIF

1 (Constant) 171.242614 235.9374392 0.725797 0.47279

Sales 0.090784631 0.030835442 2.339759409 2.944165 0.00572 56.3836

Labor 20.070267446 0.035014493 21.587938574 22.00681 0.05254 55.8971

Experience 20.488078747 0.955764142 20.054331204 20.51067 0.61279 1.0105

Performance 21.856084354 3.034080822 20.0068978263 20.61175 0.54466 1.1351

a Dependent Variable: margin

The F of 13.57 is highly significant (,0.001), so the variables explain a large portion of the variance in the dependent variable.

The model R2 is 0.61 also supporting this conclusion. The results for the independent variable tests show the following:

Even though the model results appear strong, only one inde- pendent variable is significant at a Type I error rate of 0.050— sales. However, the b coefficients do not make sense. The b coefficients for both sales and labor are beyond the range that b should theoretically take (21.0 to 1.0). Nothing can be cor- related with something more than perfectly (which would be a correlation of 1.0 or 21.0). Notice also that the two VIF factors for sales and labor are in the 50s. Generally, when multiple VIF

factors approach 5 or greater, problems with multicollinearity can be expected. The high correlation between sales and labor is a problem illustrating multicollinearity. Multicollinearity (sometimes just referred to as collinearity) in regression analysis refers to how strongly interrelated the independent variables in a model are.

As often occurs with financial data, they can be difficult to use as independent variables. In this case, the researcher may wish to rerun the model after dropping one of the offending variables. ©

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where k 5 number of independent variables n 5 number of observations MSR 5 mean squared regression MSE 5 mean squared error

Degrees of freedom for the F-test (df ) are:

df for the numerator 5 k df for the denominator 5 n 2 k 2 1

For our toy sales example,

df (numerator) 5 3 df (denominator) 5 24 2 3 2 1 5 20

In the toy example, we have 24 observations (different communities) and 3 independent variables (competitor sales, sales office, and school enrollment). A table of critical F-values shows that for 3 and 20 df, and a 0.05 Type I error rate, a value of 3.10 or more is necessary for the regression model to be considered significant, meaning that it explains a significant portion of the total varia- tion in the dependent variable. In practice, statistical programs will report the p-value associated with the F-test directly. Similarly, the programs report the statistical test for each individual inde- pendent variable. Independent variables with p-values below the acceptable Type I error rate are considered significant predictors of the dependent variable.

Steps in Interpreting a Multiple Regression Model Multiple regression models often are used to test some proposed theoretical model. For instance, a researcher may be asked to develop and test a model explaining business unit performance. Why do some business units outperform others? Multiple regression models can be interpreted using these steps:

1. Examine the model F-test. If the test result is not significant, the model should be dismissed and there is no need to proceed to further steps.

2. Examine the individual statistical tests for each parameter estimate. An independent variable with significant results can be considered a significant explanatory variable. If an indepen- dent variable is not significant, the model should be run again with nonsignificant predictors deleted. Often, it is best to eliminate predictor variables one at a time, and then rerun the reduced model.

3. Examine the model R2. No cutoff values exist that can distinguish an acceptable amount of explained variation across all regression models. However, the absolute value of R2 is more important when the researcher is more interested in prediction than in explanation. In other words, the regression is run for pure forecasting purposes. When the model is more oriented toward explanation of which variables are most important in explaining the dependent vari- able, cutoff values for the model R2 are not really appropriate.

4. Examine collinearity diagnostics. Multicollinearity in regression analysis refers to how strongly interrelated the independent variables in a model are. When multicollinearity is too high, the individual parameter estimates become difficult to interpret. Most regression programs can compute variance inflation factors (VIF) for each variable. As a rule of thumb, VIF above 5.0 suggests problems with multicollinearity.13

Exhibit 24.4 illustrates these steps. The regression model explains business unit profitability for a sample of 28 business units for a Fortune 500 company. The independent variables are hours (average hours spent in training for the workforce), budget (the percentage of the promotional budget used), and state (a dummy variable indicating whether the business unit is in Arizona and coded 0, or in Ohio and coded 1). In this case, the researcher is using a maximum acceptable Type I error rate of 0.05. The conclusion reached from this analysis is that hours spent in training seem to pay off in increased business unit profitability as evidenced by the significant, positive regression coefficient (b 5 0.55, p , 0.05).

multicollinearity

The extent to which independent variables in a

multiple regression analysis are correlated with each

other; high multicollinearity can make interpreting

parameter estimates difficult or impossible.

590 • PART SIX Data Analysis and Presentation

ANOVA (n-Way) and MANOVA As discussed above, regression is a form of the GLM with a single continuous dependent measure and continuous independent measure(s). An ANOVA or MANOVA model also represents a form of the GLM. ANOVA can be extended beyond one-way ANOVA to predict a continuous depen- dent variable with multiple categorical independent variables. Multivariate analysis of variance (MANOVA), is a multivariate technique that predicts multiple continuous dependent variables with multiple independent variables. The independent variables are categorical, although a continuous control variable can be included in the form of a covariate. Statistical programs usually refer to any ANOVA with only one dependent variable as univariate analysis of variance or simply as ANOVA.

n-Way (Univariate) ANOVA The interpretation of an n-way ANOVA model follows closely from the regression results described above. The steps involved are essentially the same with the addition of interpreting differences between means:

multivariate analysis of variance (MANOVA)

A multivariate technique that predicts multiple continuous dependent variables with multiple categorical independent variables.

EXHIBIT 24.4

Interpreting Multiple Regression Results

The SAS System

The REG Procedure Model: MODEL1

Dependent Variable: Paid

Number of Observations Read 28 Number of Observations Used 28

Analysis of Variance

Sum of Mean Source DF Squares Square F Value Pr > F

Model 3 1770668 590223 19.38 <.0001 Error 24 731035 30460 Corrected Total 27 2501703

Root MSE 174.52738 R-Square 0.7078 Dependent Mean 654.03571 Adj R-Sq 0.6713 Coeff Var 26.68469

Parameter Estimates

Parameter Standard Standardized Variable DF Estimate Error t Value Pr > |t| Estimate VIF

Intercept 1 −109.90538 217.46253 −0.51 0.6179 0 Hours 1 0.99438 0.27688 3.59 0.0015 0.55433 1.96 Budget 1 6.60121 3.54784 1.86 0.0751 0.28210 1.89 State 1 −66.36397 84.82434 −0.78 0.4416 −0.11073 1.65

3. The model R2 is interpreted. The IVs explain over 70% of variation in the dependent variable.

2. Interpret individual parameter estimates. In this case, only hours is significant based on a p-value below .05 (0.0015).

4. VIFs are checked for multicollinearity problems. None are indicated here.

1. Interpret model F-test. Test is significant (p < .05).

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CHAPTER 24 Multivariate Statistical Analysis • 591

1. Examine the overall model F-test result. If significant, proceed. 2. Examine individual F-tests for each individual independent variable. 3. For each significant categorical independent variable, interpret the effect by examining the

group means (see Chapter 12). 4. For each significant continuous variable (covariate), interpret the parameter estimate (b). 5. For each significant interaction, interpret the means for each combination. A graphical repre-

sentation as illustrated in Chapter 12 can greatly assist in this interpretation.

Interpreting MANOVA Compared to ANOVA, a MANOVA model produces an additional layer of testing. The first layer of testing involves the multivariate F-test, which is based on a statistic called Wilke’s Lambda (Λ). This test examines whether or not an independent variable explains significant variation among the dependent variables within the model. If this test is significant, then the F-test results from individual univariate regression models nested within the MANOVA model are interpreted. The rest of the interpretation results follow from the one-way ANOVA or multiple regression model results above. The Research Snapshot “How to Get MANOVA Results” provides an example of how to run and interpret MANOVA.

Discriminant Analysis Researchers often need to produce a classification of sampling units. This process may involve using a set of independent variables to decide if a sampling unit belongs in one group or another. A physi- cian might record a person’s blood pressure, weight, and blood cholesterol level and then categorize that person as having a high or low probability of a heart attack. A researcher interested in retailing failures might be able to group firms as to whether they eventually failed or did not fail on the basis of independent variables such as location, financial ratios, or management changes. A bank might want to discriminate between potentially successful and unsuccessful sites for electronic fund trans- fer system machines. A human resource manager might want to distinguish between applicants to hire and those not to hire. The challenge is to find the discriminating variables to use in a predictive equation that will produce better than chance assignment of the individuals to the correct group.

Discriminant analysis is a multivariate technique that predicts a categorical dependent vari- able (rather than a continuous, interval-scaled variable, as in multiple regression) based on a linear combination of independent variables. In each problem above, the researcher determines which variables explain why an observation falls into one of two or more groups. A linear combination of independent variables that explains group memberships is known as a discriminant function. Discriminant analysis is a statistical tool for determining such linear combinations. The researcher’s task is to derive the coefficients of the discriminant function (a straight line).

We will consider an example of the two-group discriminant analysis problem where the dependent variable, Y, is measured on a nominal scale. (Although n-way discriminant analysis is possible, it is beyond the scope of this discussion.) Suppose a personnel manager for an electrical wholesaler has been keeping records on successful versus unsuccessful sales employees. The per- sonnel manager believes it is possible to predict whether an applicant will succeed on the basis of age, sales aptitude test scores, and mechanical ability scores. As stated at the outset, the problem is to find a linear function of the independent variables that shows large differences in group means. The first task is to estimate the coefficients of the applicant’s discriminant function. To calculate the individuals’ discriminant scores, the following linear function is used:

Z i 5 b

1 X

1i 1 b

2 X

2i 1 Á 1 b

n X

where

Z i 5 ith applicant’s discriminant score

b n 5 discriminant coefficient for the nth variable

X ni 5 ith applicant’s value on the nth independent variable

Using scores for all the individuals in the sample, a discriminant function is determined based on the criterion that the groups be maximally differentiated on the set of independent variables.

discriminant analysis

A statistical technique for predicting the probability that

an object will belong in one of two or more mutually exclusive

categories of the dependent variable, based on several

independent variables.

592 • PART SIX Data Analysis and Presentation

593

RESEARCH SNAPSHOT

A department store developer gathered data looking at the effect of nostalgia on customer impressions. A field experiment was set up in which a key department was either given a mod- ern design or a retro design. It was hoped that the retro design would create feelings of nostalgia. Several hundred consumers were interviewed. Since two related dependent variables are involved (Y

1 5 interest and Y

2 5 excitement), MANOVA is the

appropriate technique.

MANOVA can be conducted using SPSS by clicking on ANALYZE, then GENERAL LINEAR MOdEL, and then MuLTIVARI- ATE. (If only one dependent variable were involved, the choice would be uNIVARIATE.) This opens a dialog box as shown here:

How to Get MANOVA Results

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The dialog box includes places to enter dependent variables, fixed factors (between-subjects categorical independent vari- ables), and covariates. In this case, the fixed factors are:

1. Experimental variable (0 5 modern, 1 5 retro) 2. Respondent sex (0 5 male, 1 5 female)

Respondent age is included as a covariate or control variable (years).

SPSS provided output that can be summarized briefly:

1. Multivariate Results: a. Wilke’s Lambda 5 0.964 b. Overall multivariate F 5 9.6 with 2 and 510 df c. The p-value associated with this result is less than

0.001. Thus the multivariate results are significant, so the research proceeds to interpret the individual univar- iate ANOVA results for each dependent variable (SPSS provides these results automatically).

2. The univariate model F statistics for each dependent variable are both significant (p , 0.001) so the researcher moves on to the next step.

3. The individual effects associated with Y 1 (interest) are inter-

preted. For example, for the experimental variable, the result is: a. F 5 0.4, with 1 and 511 df for interest (p 5 0.531).

b. Age is not significant. c. The interaction is not significant.

4. The individual effects associated with Y 2 (excitement) are

interpreted. For example, for the experimental variable, the result is: a. F 5 13.4, with 1 and 511 df for excitement

(p , 0.001). b. Sex and age are both significant predictors too

(p , 0.001). c. The interaction of sex and the retro/modern

experimental variable is also significant. 5. After carefully reviewing the means for each experimental

cell as well as the covariate results, the researcher reaches the following conclusions: a. The retro look produced more excitement but not

necessarily more interest. b. Women are more interested and more excited about

shopping. c. The effect of the retro condition was stronger for

men than for women. That is, the difference in means between the retro and modern condition is larger for men than for women.

d. Younger consumers are more excited about shopping.

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Returning to the example with three independent variables, let us suppose the personnel manager finds the standardized weights in the equation to be:

Z 5 b 1 X

1 1 b

2 X

2 1 b

3 X

5 0.069X 1

1 0.013X 2

1 0.0007X 3

This means that age (X 1 ) is much more important than sales aptitude test scores (X

2 ). Mechanical

ability (X 3 ) has relatively minor discriminating power.

In the computation of the linear discriminant function, weights are assigned to the variables to maximize the ratio of the difference between the means of the two groups to the standard devia- tion within groups. The standardized discriminant coefficients, or weights, provide information about the relative importance of each of these variables in discriminating between the two groups.

A major goal of discriminant analysis is to perform a classification function. The purpose of classi- fication in our example is to predict which applicants will be successful and which will be unsuccess- ful based on their age, sales aptitude test score, and mechanical ability, and to group them accordingly. To determine whether the discriminant analysis can be used as a good predictor of applicant success, current employees with known characteristics are used in constructing the model. Each observation (current employee) is placed into one of the groups based on the independent variables. Some will be classified successfully, but some will not. This information is provided in the “confusion matrix,” which is similar to the cross-tabulations we discussed earlier. Suppose the personnel manager has 40 successful and 45 unsuccessful employees in the sample. The confusion matrix shows that the number of correctly classified employees (72 out of 85) is much higher than would be expected by chance:

Confusion Matrix

Predicted Group

Actual Group Successful Unsuccessful

Successful 34 6 40

Unsucessful 7 38 45

EXHIBIT 24.5

Multivariate Dependence Techniques Summary

Technique Purpose

Number of Dependent Variables

Number of Independent

Variables

Type of Measurement

Dependent Independent

Multiple regression To investigate simultaneously the effects of several independent variables on a dependent variable

1 2 or more Interval Interval

Discriminant Analysis

To predict the probability that an object or individual will belong in one of two or more mutually exclusive categories, based on several independent variables

1 2 or more Nominal Interval

MANOVA To determine simultaneously whether statistically significant mean differences occur between groups on several variables

2 or more 1 or more Interval Nominal

Again, similar to cross-tabs and x2, tests can be performed to determine whether the rate of correct classification is statistically significant.

Exhibit 24.5 summarizes multivariate dependence techniques.

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594 • PART SIX Data Analysis and Presentation

Analysis of Interdependence Suppose we wished to identify the factors that are associated with pleasant shopping experiences,14 identify factors that would allow better flexibility and control of logistics programs,15 or identify groups of students each associated with a unique learning style.16 Each of these are problems that have been addressed through the use of a multivariate interdependence technique. Rather than attempting to predict a variable or set of variables from a set of independent variables, we use tech- niques like factor analysis, cluster analysis, and multidimensional scaling to better understand the relationships and structure among a set of variables or objects.

Factor Analysis Factor analysis is a prototypical multivariate, interdependence technique. Factor analysis is a technique of statistically identifying a reduced number of factors from a larger number of measured variables. The factors themselves are not measured, but instead, they are identified by forming a variate using the measured variables. Factors are usually latent constructs like attitude or satisfaction, or an index like social class. A researcher need not distinguish between independent and dependent variables to conduct factor analysis. Factor analysis can be divided into two types:

1. Exploratory factor analysis (EFA)—performed when the researcher is uncertain about how many factors may exist among a set of variables. The discussion here concentrates primarily on EFA.

2. Confirmatory factor analysis (CFA)—performed when the researcher has strong theoretical expectations about the factor structure (number of factors and which variables relate to each factor) before performing the analysis. CFA is a good tool for assessing construct validity because it provides a test of how well the researcher’s “theory” about the factor structure fits the actual observations. Many books exist on CFA alone and the reader is advised to refer to any of those sources for more on CFA.

Exhibit 24.6 illustrates factor analysis graphically. Suppose a researcher is asked to examine how feelings of nostalgia in a restaurant influence customer loyalty. Three hundred fifty customers at themed restaurants around the country are interviewed and asked to respond to the following Likert scales (1 = Strongly Disagree to 7 = Strongly Agree):

X 1 —I feel a strong connection to the past when I am in this place.

X 2 —This place evokes memories of the past.

X 3 —I feel a yearning to relive past experiences when I dine here.

X 4 —This place looks like a page out of the past.

X 5 —I am willing to pay more to dine in this restaurant.

X 6 —I feel very loyal to this establishment.

X 7 —I would recommend this place to others.

X 8 —I will go out of my way to dine here.

Factor analysis can summarize the information in the eight variables in a smaller number of variables. How many dimensions, or groups of variables, are likely present in this case? More than one technique exists for estimating the variates that form the factors. However, the gen- eral idea is to mathematically produce variates that explain the greatest total variance among the set of variables being analyzed. In this example, the factor analysis indicates there are two dimensions, or factors, as shown in Exhibit 24.6. Thus, EFA provides two important pieces of information:

1. How many factors exist among a set of variables? 2. What variables are related to or “load on” which factors?

factor analysis

A prototypical multivariate, interdependence technique that statistically identifies a reduced number of factors from a larger number of measured variables.

CHAPTER 24 Multivariate Statistical Analysis • 595

How Many Factors One of the first questions the researcher asks is, “How many factors will exist among a large num- ber of variables?” While a detailed discussion is beyond the scope of this text, the question is usually addressed based on the eigenvalues for a factor solution. Eigenvalues are a measure of how much variance is explained by each factor. The most common rule—and the default for most statistical programs—is to base the number of factors on the number of eigenvalues greater than 1.0. The basic thought is that a factor with an eigenvalue of 1.0 has the same total variance as one variable. It usually does not make sense to have factors, which are a combination of variables, which have less information than a single variable. So, unless some other rule is specified, the number of factors shown in a factor solution is based on this rule.

Factor Loadings Each arrow connecting a factor (represented by an oval in Exhibit 24.6) to a variable (represented by a box in Exhibit 24.6) is associated with a factor loading. A factor loading indicates how strongly correlated a measured variable is with that factor. In other words, to what extent does a variable “load” on a factor? EFA depends on the loadings for proper interpretation. A latent con- struct can be interpreted based on the pattern of loadings and the content of the variables. In this way, the latent construct is measured indirectly by the variables.

Loading estimates are provided by factor analysis programs. In Exhibit 24.6, the factor loading estimates are shown beneath the factor diagram. The thick arrows indicate high loading estimates and the thin dotted lines correspond to weak loading estimates. Factors are interpreted by examin- ing any patterns that emerge from the factor results. Here, a clear pattern emerges. The first four variables produce high loadings on factor 1 and the last four variables produce high loadings on factor 2.

When a clear pattern of factor loadings emerges, interpretation is easy. Because the first four variables all have content consistent with nostalgia and the second four variables all have content consistent with customer loyalty, the two factors can easily be labeled. Factor one represents the latent construct nostalgia and factor 2 represents the latent construct customer loyalty.

EXHIBIT 24.6

A Simple Illustration of Factor Analysis Nostalgia

Loyalty

X2 X3 X4 X5 X6 X7 X8

Factor Loading Estimates: Variable: Factor 1 Factor 2

X1 .90 �.02 X2 .88 .10 X3 .85 .12 X4 .70 .31 X5 �.10 .90 X6 �.05 .90 X7 .20 .75 X8 .21 .72

factor loading

Indicates how strongly a measured variable is correlated

with a factor.

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596 • PART SIX Data Analysis and Presentation

597

Factor Rotation Factor rotation is a mathematical way of simplifying factor results. The most common type of factor rotation is a process called varimax. A discussion of the technical aspects of the concept of factor rotation is far beyond the scope of this book. However, factor rotation involves creating new reference axes for a given set of variables. An initial factor solution is often difficult to interpret. Rotation “clears things up” by producing more obvious patterns of loadings. Users can observe this by looking at the unrotated and rotated solutions in the factor analysis output. An example of how to run factor analysis is provided in the Research Snapshot “Getting Factor Results with SAS or SPSS”.

Data Reduction Technique Factor analysis is considered a data reduction technique. Data reduction techniques allow a researcher to summarize information from many variables into a reduced set of variates or com- posite variables. Data reduction is advantageous for many reasons. In general, the rule of parsimony suggests that an explanation involving fewer components is better than one involving more. Fac- tor analysis accomplishes data reduction by capturing variance from many variables with a single variate. Data reduction is also a way of identifying which variables among a large set might be important in some analysis. Thus, data reduction simplifies decision making.

factor rotation

A mathematical way of simplifying factor analysis results so as to better identify which variables “load on” which factors; the most common procedure is varimax.

data reduction technique

Multivariate statistical approaches that summarize the information from many variables into a reduced set of variates formed as linear combinations of measured variables.

rule of parsimony

The rule of parsimony suggests that an explanation involving fewer components is better than one involving more.

RESEARCH SNAPSHOT

Although researchers may choose to use a spread- sheet to produce simple or even multiple regression results, they will almost always turn to a specialized program for procedures like factor analysis. As a way of familiarizing readers with the mechanics involved, here are some instructions for getting factor results in each program.

SAS is most typically interfaced by writing short computer programs. SAS can read Excel spreadsheets quite easily. The data simply need to be “imported” into SAS by using the File dialog box (click on File to begin this process—see SAS documentation contained in the help files for more on how to do this). Once the data are set up, a factor program can be easily produced. Suppose we wished to run a factor program including a varimax rotation on eight variables labeled X1–X8. The program would be:

proc factor rotate 5 v; var X1 2 X8;

After we click “run,” the results appear in the output window. In SPSS, the click-through sequence is as follows:

■ ANALYZE ■ dATA REduCTION ■ FACTOR ANALYSIS

This produces a dialog box. Now follow the steps below to get results that would match those above:

■ Highlight variables X1 to X8 (either individually or in multiples).

■ Click ▶ the to move them into the “Variables” window. ■ Click ROTATION.

● Select VARIMAX. ■ Optional: Click OPTIONS.

● Select SORTEd BY SIZE. ● Select SuPPRESS ABSOLuTE VALuES LESS THAN.

– These two options make the output easier to read by organizing the output by the size of the load- ings on each factor and by not showing loadings below some specified absolute value (0.1 by default). For factor analyses involving many variables, this is particularly helpful.

■ Click CONTINuE. ■ Click OK.

The results will appear in the output window.

Getting Factor Results with SAS or SPSS

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In our example, the researcher can now form two composite factors representing the latent constructs nostalgia and customer loyalty. These can be formed using factor equations of this form:

F k

5 L 1 X

1 1 L

2 X

2 1 L

3 X

3 1 L

4 X

4 1 L

5 X

5 1 L

6 X

6 1 L

7 X

7 1 L

8 X

where

F k is the factor score for the kth factor (in this case there are two factors)

L represents factor loadings (ith) 1 through 8 for the corresponding factor

X represents the value of the corresponding measured variable

Using this type of equation, the scores for variables X 1 –X

8 can be summarized by two scores,

one for factor 1 and one for factor 2. This provides an example of the rule of parsimony. If the researcher wanted to analyze the correlation among these variables, now all that needs to be done is to analyze the bivariate correlation between factor 1 (nostalgia) and factor 2 (loyalty). This should prove much easier than analyzing an 8 3 8 correlation matrix. Statistical programs like SPSS and SAS will produce factor scores automatically if requested.

We can see that because F 1 is associated with high values for L

1 through L

4 (and low values for

L 5 , L

6 , L

7 , and L

8 ) and F

2 is associated with high values for L

5 through L

8 (and low for L

1 , L

2 , L

3 , and

L 4 ), F

1 is determined almost entirely by the nostalgia items and F

2 is determined almost entirely

by the customer loyalty items. The factor pattern of high and low loadings can be used to match measured variables to factors in this way.

Creating Composite Scales with Factor Results When a clear pattern of loadings exists as in this case, the researcher may take a simpler approach. F

1 could be created simply by summing the four variables with high loadings and creating a sum-

mated scale representing nostalgia. F 2 could be created by summing the second four variables

(those loading highly on F 2 ) and creating a second summated variable. While not necessary, it is

often wise to divide these summated scales by the number of items so the scale of the factor is the same as the original items. For example, F

1 would be

((X 1

1 X 2

1 X 3

1 X 4 )/4)

The result provides a composite score on the 1–7 scale. The composite score approach would introduce very little error given the pattern of loadings. In other words, very low loadings suggest a variable does not contribute much to the factor. The reliability of each summated scale can be tested by computing a coefficient alpha estimate. Then, the researcher could conduct a bivariate regression analysis that would test how much nostalgia contributed to loyalty.

Communality While factor loadings show the relationship between a variable and each factor, a researcher may also wish to know how much a single variable has in common with all factors. Communality is a measure of the percentage of a variable’s variation that is explained by the factors. A relatively high communality indicates that a variable has much in common with the other variables taken as a group. A low communality means that the variable does not have a strong relationship with the other variables. The item might not be part of one of the common factors or might represent a separate dimension. Communality for any variable is equal to the sum of the squared loadings for that variable. The communality for X

1 is

0.902 1 0.022 5 0.8104

Communality values are shown on factor analysis printouts.

598 • PART SIX Data Analysis and Presentation

Total Variance Explained Along with the factor loadings, the percentage of total variance of original variables explained by the factors can be useful. Recall that common variance is correlation squared. Thus, if each loading is squared and totaled, that total divided by the number of factors provides an estimate of the variance in a set of variables explained by a factor. This explanation of variance is much the same as R2 in multiple regression. Again, these values are computed by the statistics program so there is seldom a need to compute them manually. In this case, though, the variance accounted for among the eight variables by the nostalgia factor is 0.36 and the variance among the eight variables explained by the loyalty factor is 0.35. Thus, the two factors explain 71 percent of the variance in the eight variables:

0.36 1 0.35 5 0.71

In other words, the researcher has 71 percent of the information in two factors that are in the original eight items, another example of the rule of parsimony.

Cluster Analysis Cluster analysis is a multivariate approach for identifying objects or individuals that are similar to one another in some respect. Cluster analysis classifies individuals or objects into a small number of mutually exclusive and exhaustive groups. Objects or individuals are assigned to groups so that there is great similarity within groups and much less similarity between groups. The clus- ter should have high internal (within-cluster) homogeneity and high external (between-cluster) heterogeneity.

Cluster analysis is an important tool for the business researcher. For example, an organization may want to group its employees based on their insurance or retirement needs, or on job per- formance dimensions. Similarly, a business may wish to identify market segments by identifying subjects or individuals who have similar needs, lifestyles, or responses to marketing promotions. Clusters, or subgroups, of recreational vehicle owners may be identified on the basis of their similarity with respect to recreational vehicle usage and the benefits they want from recreational vehicles. Alternatively, the researcher might use demographic or lifestyle variables to group indi- viduals into clusters identified as market segments.

We will illustrate cluster analysis with a hypothetical example relating to the types of vaca- tions taken by 12 individuals. Vacation behavior is represented on two dimensions: number of vacation days and dollar expenditures on vacations during a given year. Exhibit 24.7 is a scatter diagram that represents the geometric distance between each individual in two-dimensional space. The diagram portrays three clear-cut clusters. The first subgroup—consisting of indi- viduals L, H, and B— suggests a group of individuals who have many vacation days but do not spend much money on their vacations. The second cluster—represented by individuals A, I, K, G, and F—represents intermediate values on both variables: average amounts of vaca- tion days and average dollar expenditures on vacations. The third group—individuals C, J, E, and D—consists of individuals who have relatively few vacation days but spend large amounts on vacations.

In this example, individuals are grouped on the basis of their similarity or proximity to one another. The logic of cluster analysis is to group individuals or objects by their similarity to or distance from each other. The mathematical procedures for deriving clusters will not be dealt with here, as our purpose is only to introduce the technique.

A classic study provides a very pragmatic example of the use of cluster analysis.17 Managers frequently are interested in finding test-market cities that are very similar so that no extraneous variation will cause differences between the types of customers they may potentially have. For this study, characteristics associated with different types of restaurants (from fast food to fine dining), and the availability of those restaurants within a particular city were entered into a cluster analysis

cluster analysis

A multivariate approach for grouping observations based on similarity among measured variables.

CHAPTER 24 Multivariate Statistical Analysis • 599

to identify groups. Based upon the results, four different types of groups were identified. Urbane oases were cities with a numerous restaurants, across the full spectrum of fine dining to fast food. McCulture oases tended to have numerous restaurants, but they tended to be focused on standard- ized eating places such as Subway or Applebees. Finally, the Urbane and McCulture deserts fol- lowed the same characteristics of their oases counterparts, but tended to have fewer restaurants in general. (See Exhibit 24.8 for additional details.)

This example should help to clarify the difference between factor analysis and cluster analysis. In factor analysis the researcher might search for constructs that underlie the variables

EXHIBIT 24.8

Cluster Analysis of Cities According to the Availability and Prevalence of Certain Types of Restaurants

EXHIBIT 24.7

Clusters of Individuals on Two Dimensions L

H B

A I

F G

C J

E D

Nu m

be r o

f V ac

at io

n Da

Dollars Spent on Vacations

Cluster Category Example Cities Cluster Category Example Cities

Urbane Oases Los Angeles, CA Atlanta, GA Berkeley, CA Denver, CO Boston, MA Chicago, IL Baltimore, MD Seattle, WA Philadelphia, PA Honolulu, HI

Urbane Deserts Buffalo, NY Syracuse, NY Fresno, CA Spokane, WA Anaheim, CA Newark, NJ Oakland, CA New Haven, CT St. Paul, MN Manchester, NH

McCulture Oases Houston, TX Miami, FL Orlando, FL Phoenix, AZ St. Louis, MO Indianapolis, IN Cincinnati, OH Birmingham, AL Dallas, TX Savannah, GA

McCulture Deserts Detroit, MI Des Moines, IA Fort Worth, TX Omaha, NE Nashville, TN Wichita, KS Chattanooga, TN Boise, ID Mobile, AL Kansas City, MO

Source: Zachary Paul Neal, “Culinary Deserts, Gastronomic Oases: A Classification of U.S. Cities,” (2006), Urban Studies, 43(1): 1–21.

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600 • PART SIX Data Analysis and Presentation

(type of restaurant, number of restaurants); in cluster analysis the researcher would seek constructs that underlie the objects (cities). Cluster analysis differs from multiple discriminant analysis in that the groups are not predefined. The purpose of cluster analysis is to determine how many groups really exist and to define their composition.

Multidimensional Scaling Multidimensional scaling provides a means for placing objects in multidimensional space on the basis of respondents’ judgments of the similarity of objects. The perceptual difference among objects is reflected in the relative distance among objects in the multidimensional space.

In the most common form of multidimensional scaling, subjects are asked to evaluate an object’s similarity to other objects. For example, a sports car study may ask respondents to rate the similarity of a Nissan 370Z to a Chevrolet Corvette, then a BMW Z4 to the Corvette, followed by a Mercedes Benz SL to the Corvette, an Acura TSX to the Corvette, and so forth. Then, the comparisons are rotated (i.e., Nissan 370Z to the TSX, BMW Z4 to the Mercedes Benz SL, and so on until all pairs are exhausted). Multidimensional scaling would then generate a plot of the cars, and the analyst then attempts to explain the difference in objects on the basis of the plot. The interpretation of the plot is left to the researcher.

In one study MBA students were asked to provide their perceptions of relative simi- larities among six graduate schools. Next, the overall similarity scores for all possible pairs of objects were aggregated for all individual respondents and arranged in a matrix. With the aid of a computer program, the judgments about similarity were statistically transformed into dis- tances by placing the graduate schools into a specified multidimensional space. The distance between similar objects on the perceptual map was small for similar objects; dissimilar objects were farther apart.

Exhibit 24.9 shows a perceptual map in two- dimensional space. Inspection of the map illus- trates that Harvard and Stanford were perceived as quite similar to each other. MIT and Carn- egie also were perceived as very similar. MIT and Harvard, on the other hand, were perceived as dissimilar. The researchers identified the two axes as “quantitative versus qualitative curriculum” and “less versus more prestige.” The labeling of the dimension axes is a task of interpretation for the researcher and is not statistically deter- mined. As with other multivariate techniques in the analysis of interdependence, there are several alternative mathematical techniques for multidi- mensional scaling. Likewise, there are multiple ways of using multivariate procedures to gener- ate a perceptual map. For example, factor scores resulting from factor analysis can be plotted along the factor dimensions. Such an approach may show the competitive positioning of several different firms along dimensions related to value and quality.

Exhibit 24.10 summarizes the multivariate techniques for analysis of interdependence.

How similar are these cars? This is the input to multidimensional scaling.

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multidimensional scaling

A statistical technique that measures objects in multidimensional space on the basis of respondents’ judgments of the similarity of objects.

CHAPTER 24 Multivariate Statistical Analysis • 601

SUMMARY 1. Understand what multivariate statistical analysis involves and know the two types of mul- tivariate analysis. Multivariate statistical methods analyze multiple variables or even multiple sets of variables simultaneously. They are particularly useful for identifying latent constructs using multiple individual measures. Multivariate techniques represent data through the use of variates. Variates are mathematical combinations of variables. The two major types of multivariate pro- cedures are interdependence and dependence techniques. Interdependence techniques do not distinguish dependent and interdependent variables, whereas dependence techniques do make this distinction.

2. Interpret results from multiple regression analysis. Multiple regression analysis predicts a continuous dependent variable with multiple independent variables. The independent variables can be either continuous or categorical. Categorical variables must be coded as dummy variables.

EXHIBIT 24.9

Perceptual Map of Six Graduate Business Schools: Simple Space

Qualitative curriculum

Quantitative curriculum

More prestige

Less prestige

Chicago

Carnegie

Massachusetts Institute of Technology

Harvard

Stanford Wharton

Source: Green, P. E., Carmone F. J., and Robertson, P. J., “Nonmetric Scaling Methods: An Exposition and Overview,” The Wharton Quarterly, Vol. 2, 1968, pp. 159–173.

Technique Purpose Type of Measurement

Factor analysis To summarize into a reduced number of factors the information contained in a large number of variables

Interval

Cluster analysis To classify individuals or objects into a small number of mutually exclusive and exhaustive groups, ensuring that there will be as much likeness within groups and as much difference among groups as possible

Interval

Multidimensional scaling

To measure objects in multidimensional space on the basis of respondents’ judgments of their similarity

Varies depending on technique

EXHIBIT 24.10

Summary of Multivariate Techniques for Analysis of Interdependence

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602 • PART SIX Data Analysis and Presentation

Multiple regression results are analyzed by examining the significance of the overall model using the F-test results, the individual parameter estimates, the overall model R2, and the model col- linearity diagnostics. Standardized regression coefficients have the advantage of a common scale, making them comparable from model to model and variable to variable.

3. Interpret results from multivariate analysis of variance (MANOVA). MANOVA is an extension of ANOVA involving multiple related dependent variables. Thus, MANOVA repre- sents a form of the GLM predicting that multiple categorical independent variables affect multiple, related dependent variables. Interpretation of a MANOVA model is similar to interpretation of a regression model. However, the multivariate F-test results associated with Wilke’s lambda (Λ) are interpreted first, followed by interpretation of the individual ANOVA results.

4. Interpret basic exploratory factor analysis results. EFA is a data reduction technique in which the variance in multiple variables is represented by a smaller number of factors. The factors generally represent latent factors or indexes. The pattern of loadings suggests both the number of latent factors that may exist and indicates which variables are associated with each factor. Rotated factor solutions are useful in properly interpreting factor analysis results.

5. Know what multiple discriminant analysis can be used to do. Another dependence tech- nique is discriminant analysis. Discriminant analysis uses multiple independent variables to classify observations into one of a set of mutually exclusive categories. In other words, discriminant analy- sis predicts a categorical dependent variable with multiple independent variables.

6. Understand how cluster analysis can identify market segments. Cluster analysis classifies multiple observations into a smaller number of mutually exclusive and exhaustive groups. These should have as much similarity within groups and as much difference between groups as possible. In cluster analysis the groups are not predefined. However, clusters can be used to represent mar- ket segments because market segments also represent consumers who are similar to each other within a segment, but who are different from consumers in other segments.

KEY TERMS AND CONCEPTS cluster analysis, 599 data reduction technique, 597 dependence techniques, 584 discriminant analysis, 592 dummy variable, 587 factor analysis, 595

factor loading, 596 factor rotation, 597 general linear model (GLM), 586 interdependence techniques, 584 multicollinearity, 590 multidimensional scaling, 601

multiple regression analysis, 586 multivariate analysis of variance

(MANOVA), 591 partial correlation, 588 rule of parsimony, 597 variate, 583

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Define multivariate statistical analysis. 2. What is the variate in multivariate? What is an example of a

variate in multiple regression and in factor analysis? 3. What is the distinction between dependence techniques and

interdependence techniques? 4. What is GLM? How can multiple regression and n-way

ANOVA be described as GLM approaches? 5. What are the steps in interpreting a multiple regression analy-

sis result? Can the same steps be used to interpret a univariate ANOVA model?

6. A researcher dismisses a regression result because the model R2 was under 0.70. Do you think this was necessarily wise? Explain.

7. Return to the simple example of regression results for the toy company presented in the chapter. Since the data come equally from Europe and Canada, does this represent a potential source of variation that is not accounted for in the researcher’s model?

How could the researcher examine whether or not sales may be dependent upon country?

8. What is a factor loading? 9. How does factor analysis allow for data reduction?

10. How is the number of factors decided in most EFA programs? 11. What is multidimensional scaling? When might a researcher use

this technique? 12. What is cluster analysis? When might a researcher use this

technique? 13. Name at least two multivariate techniques that can be useful in

constructing perceptual maps. 14. A researcher uses multiple regression to predict a client’s sales

volume based on gross domestic product, personal income, disposable personal income, unemployment, and the consumer price index. What problems might be anticipated with this mul- tiple regression model?

Chapter 24: Multivariate Statistical Analysis 603

RESEARCH ACTIVITIES 1. Use the multistep process to interpret the regression results below. This model has been run by a researcher trying to explain customer

loyalty to a restaurant. The independent variables are customer perceptions of value, atmosphere, quality, and a location variable labeled center. This is a dummy variable that takes the value of 1 if the restaurant is in a shopping center and 0 if it is a standalone location. What substantive conclusions would you recommend to the restaurant company?

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 0.176 0.031 0.027 0.996

DV 5 Loyalty

ANOVA(b)

Model Sum of Squares df Mean Square F Sig.

1 Regression 27.9731 4 6.9933 7.049 0.0000138

Residual 876.0469 883 0.9921

Total 904.0200 887

Coefficients(a)

Model Unstandardized Coefficients B

Std. Error

Standardized Coefficients Beta t Sig. VIF

1 (Constant) 20.306 0.229 21.338 0.181

Value 0.104 0.036 0.099 2.877 0.004 1.087

Atmosphere 0.048 0.026 0.067 1.883 0.060 1.144

Quality 0.044 0.028 0.054 1.590 0.112 1.038

Center 20.250 0.071 20.124 23.508 0.000 1.132

2. Interpret the following GLM results. Following from an example in the chapter, performance is the performance rating for a business unit manager. Sales is a measure of the average sales for that unit. Experience is the number of years the manager has been in the indus- try. The variable dummy has been added. This variable is 0 if the manager has no advanced college degree and a 1 if the manager has an MBA. Do you have any recommendations?

604 Part 6: Data Analysis and Presentation

The SAS System 21:06 Wednesday, April 22, 2009 The GLM Procedure

Dependent Variable: performance

Sum of Source DF Squares Mean Square F Value Pr > F

Model 3 173.6381430 57.8793810 13.87 <.0001 Error 36 150.2341040 4.1731696 Corrected Total 39 323.8722470

R-Square Coeff Var Root MSE performance Mean 0.536132 2.514731 2.042834 81.23468

Source DF Type III SS Mean Square F Value Pr > F

dummy 1 136.9511200 136.9511200 32.82 <.0001 sales 1 22.4950649 22.4950649 5.39 0.0260 Experience 1 2.2356995 2.2356995 0.54 0.4689

Level of -------performance------- -----------sales----------- ------Experience--------

dummy N Mean Std Dev Mean Std Dev Mean Std Dev

0 22 79.4848842 1.78987031 15979.7723 2008.32604 23.8984087 8.27327485 1 18 83.3733171 2.50773844 16432.0080 2015.18863 20.6788050 8.96324112

3. Interpret the following regression results. All of the variables are the same as in number 2. These results are produced with a regression program instead of the GLM-univariate ANOVA program. a. What do you notice when the results are compared to those in number 2? Comment. b. List the independent variables in order from greatest to least in terms of how strong the relationship is with performance. c. When might one prefer to use an ANOVA program instead of a multiple regression program?

The SAS System 21:07 Wednesday, April 22, 2009 The REG Procedure Model: MODEL1

Dependent Variable: performance

Number of observations Read 40 Number of observations Used 40

Analysis of Variance

Sum of Source DF Squares Mean Square F Value Pr > F

Model 3 173.63814 57.87938 13.87 <.0001 Error 36 150.23410 4.17317 Corrected Total 39 323.87225

Root MSE 2.04283 R-Square 0.5361 Dependent Mean 81.23468 Adj R-Sq 0.4975 Coeff Var 2.51473

Parameter Estimates

Parameter Standard Standardized Variable Label DF Estimate Error t value Pr > |t| Estimate

Intercept Intercept 1 72.68459 2.88092 25.23 <.0001 0 dummy dummy 1 3.80621 0.66442 5.73 <.0001 0.66546 Sales Sales 1 0.00038324 0.00016507 2.32 0.0260 0.26578 Experience Experience 1 0.02829 0.03866 0.73 0.4689 0.08475

Chapter 24: Multivariate Statistical Analysis 605

4. Interpret the following factor analysis results. The variables represent sample results of self-reported emotions while viewing a film. Why are only two factors reported below? What would you name the two summated scales which could be produced based on these results?

Total Variance Explained

Component

Initial Eigenvalues

Total % of

Variance Cumulative %

Extraction Sums of Squared Loadings

Total % of

Variance Cumulative %

1 2.94 36.74 36.74 2.94 36.74 36.74

2 2.51 31.34 68.08 2.51 31.34 68.08

3 0.71 8.84 76.92

4 0.60 7.53 84.45

5 0.42 5.20 89.65

6 0.29 3.67 93.32

8 0.24 3.04 96.96

100.00

Extraction Method: Principal Component Analysis.

Component Matrix(a)

Factor 1 Factor 2

Interesting 0.664 20.327

Anxious 0.444 0.511

Enthusiastic 0.842 20.332

Worried 0.295 0.828

Exciting 0.812 20.206

Tired 0.269 0.835

Happy 0.784 20.383

Guilty 0.398 0.675

Extraction Method: Principal Component Analysis. a 2 components extracted.

Rotated Component Matrix(a)

Component Factor 1 Factor 2

Interesting 0.739 –0.024

Anxious 0.194 0.648

Enthusiastic 0.904 0.044

Worried 20.073 0.876

Exciting 0.825 0.147

Tired 20.100 0.872

Happy 0.872 –0.025

Guilty 0.084 0.779

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 3 iterations.

5. ’NET Go to http://www.census.gov and examine some of the tables for your area. Cut and paste the table into a spreadsheet or statisti- cal program. Run one dependence and one interdependence technique on the data. Interpret the results.

6. ’NET Use http://www.ask.com to find an F-ratio calculator that will return a p-value given a calculated F-ratio and the degrees of free- dom associated with the test.

7. ’NET The Federal Reserve Bank of St. Louis maintains a database called FRED (Federal Reserve Economic Data). Navigate to the FRED database at http://www.stls.frb.org/fred/index.html. Use the consumer price index, exchange rates, interest rates, and one other variable to predict the consumer price index for the same time period. The data can either be downloaded or cut and pasted into another file.

606 Part 6: Data Analysis and Presentation

The utah Jazz The Utah Jazz are interested in understanding the market for the National Basketball Association. A study is conducted as described here.

Data Collection Data came from a survey of adult residents of a large western metro- politan area. Respondents were selected in accordance with a quota sample of the area that was based on the age and sex characteristics reported in the most recent census. Six age categories for both males and females were used to gain representation of these characteristics in the market. In addition, interviewers were assigned to various geographic regions to ensure representation of the market with respect to socioeconomic characteristics. A total of 225 respondents age 18 and over provided data for the study.

Interviews were conducted by trained interviewers using a self- completion questionnaire. The presence of the interviewers served to answer any questions that might arise as well as to ensure compliance with the instructions.

Measures for the variables in the three categories of AIO (atti- tudes, interest, and opinions) were obtained using six-point rating scales. For example, the item for price proneness asked, “When you are buying a product such as food, clothing, and personal care items, how important is it to get the lowest price?” This item was anchored with “Not at all important” and “Extremely important.”

The broadly defined category of demographics included stan- dard socioeconomic characteristics as well as media preferences and attendance at professional hockey matches and university basketball games. Demographics were obtained using a variety of forced-choice and free-response measures, the natures of which are indicated in the variable information presented in Case Exhibit 24.1-1. The categorical measures of type of dwelling and preferred type of radio programming were coded as dummy variables for analysis. The criterion measure of patronage came from an open-ended question asking how many NBA games the respondent had attended during the past season.

CASE 24.1

CASE EXHIBIT 24.1-1

Characteristics of the Market for Professional Basketball

Means

None Low High Loading

Variables (n = 129) (n = 47) (n = 49) F-Ratio p I II

Market Orientationa

Price proneness 3.99 4.04 3.63 1.31 .271 Quality proneness 4.95 4.74 4.82 .74 .480 Product awareness 4.45 4.02 4.00 3.71 .026 Product involvement 4.34 4.43 4.14 .66 .517 Prepurchase planning 4.21 3.85 3.82 2.03 .134 Brand loyalty 3.95 4.39 3.92 .96 .384 Information search 3.83 3.55 3.96 1.06 .347

Interests in Leisure Pursuitsb

Need for change from work routine 4.11 4.34 4.55 1.92 .150 .34 Need for independence in leisure choice 4.88 4.94 4.96 .09 .911 .08 Need for companionship during leisure 4.85 5.13 4.88 1.16 .317 .10 Preference for passive versus active pursuits 3.64 4.15 4.57 7.28 .001 .70 Self-image as athletic 3.67 4.38 4.47 5.89 .003 .60 Childhood attendance at sporting events 3.38 3.89 4.18 5.41 .005 .60 Pleasure from sporting events 3.14 3.66 4.27 10.62 .000 .84

Opinions about Professional Sportsc

Athletes as a reference group 3.51 3.64 4.18 3.90 .022 .30 –.19 Excitement from enthusiastic crowd 4.27 4.72 4.73 2.70 .069 .24 .20 Excitement from animosity between teams 3.29 3.28 4.27 6.94 .001 .36 –.41 Acceptance of alcoholic beverages at games 2.60 3.64 3.39 6.88 .001 .34 .46 Enjoyment from large crowds 3.91 3.85 4.49 3.22 .042 .23 –.32 Enjoyment when standing at games 3.37 3.44 3.90 2.25 .108 .22 –.17 Excitement of professional basketball 4.09 3.91 4.67 5.34 .005 .27 –.49 Satisfaction from professional basketball 3.17 3.70 4.80 24.98 .000 .78 –.26 Importance of a winning team 4.26 4.69 5.07 6.12 .003 .39 .02

(Continued)

Chapter 24: Multivariate Statistical Analysis 607

Means

None Low High Loading

Variables (n = 129) (n = 47) (n = 49) F-Ratio p I II

Demographicsd

Years in local area (number of years) 24.47 23.51 19.04 2.02 .135 –.24

Sex (0 = female, 1 = male) .40 .55 .65 5.45 .006 .39

Marital status (0 = single, 1 = married) .60 .62 .45 2.00 .138 –.21 Household size (number of persons) 3.13 3.27 3.14 .11 .896 .01

Rents apartment (0 = no, 1 = yes) .18 .32 .35 3.70 .026 .30 Rents a house (0/1) .09 .09 .08 .03 .967 –.03 Owns a house (0/1) .60 .49 .41 3.08 .048 –.29 Owns a condominium (0/1) .05 .02 .06 .50 .607 .01 Head of household (0/1) .52 .64 .67 2.19 .115 .24 Occupational prestige of self (NORC scale) 68.05 69.36 70.63 1.27 .284 .19 Job leaves evenings free for entertainment (0/1) .87 .85 .92 .57 .567 .10 Prefers easy-listening music radio programming (0/1) .39 .34 .29 .83 .438 –.15 Prefers contemporary popular music radio radio (0/1) .16 .28 .27 1.96 .143 .20 Prefers rock music radio (0/1) .14 .11 .27 2.76 .066 .23 Prefers country-western music radio (0/1) .15 .19 .08 1.22 .299 –.12 Prefers talk and news radio programming (0/1) .09 .04 .06 .52 .597 –.08 Education (years of schooling) 13.08 13.66 13.56 5.11 .007 .38 Age (years) 41.51 39.79 33.59 4.21 .016 –.34 Annual household income (7-point scale) 4.88 5.11 5.16 .65 .523 .13 Monthly personal expenditures on entertainment for household (dollars)

85.10 112.45 101.29 1.38 .254 .13

Attendance at university basketball (games last year) .92 1.89 4.14 15.29 .000 .66 Attendance at professional hockey (matches last year) .69 2.28 2.78 5.33 .006 .37

a Canonical discriminant analysis not significant at p = .189; therefore, no loadings are given. b Canonical discriminant analysis significant at p = .004, first function significant. Centroids for the market segment groups are as follows: none, –.29; low, .19; high, .59.

c Canonical discriminant analysis significant at p = .000, both functions significant. Centroids for the market segment groups on the first function are as follows: none, –.47; low, .26; high, 1.00. Centroids on the second function are as follows: none, –.10; low, .57; high, –.27.

d Canonical discriminant analysis significant at p = .004, first function significant. Centroids for the market segment groups are as follows: none, –.41; low, .14; high, .97.

Source: Courtesy of the American Marketing Association. Adapted from paper presented at AMA conference, 1984.

CASE EXHIBIT 24.1-1 (Continued)

Characteristics of the Market for Professional Basketball

Data Analysis The distribution of responses to the attendance item was skewed, as might be expected. Thus, 57.3 percent of the respondents reported having attended none of the 41 possible games. Those who attended at least one game were recorded in accordance with specification of the light half and the heavy half of the market. This category of patrons was split as nearly as possible at the median, giving 20.9 percent who attended one or two games and 21.8 percent who attended three or more. The three patronage categories thus used for analysis were sub- sequently termed the none, low, and high attendance segments.

Given the categorical nature of the criterion measure and the continuous nature of the predictor variables, both univariate analysis of variance and discriminant analysis were employed for the survey. Each of the four categories of predictor variables was subjected to a separate discriminant analysis to test the multivariate hypothesis of relationship between patronage and the predictor set in question. The univariate ANOVAs were used to provide complementary informa- tion about the nature of the segments.

Results Case Exhibit 24.1-1 gives the results of the analyses conducted on the four sets of predictor variables. Each set produced at least one variable that was significant in univariate analysis. Three of the four discriminant analyses were significant.

The first predictor set involving AIOs, “marketing orientation,” provided only a single variable that ANOVA showed to differentiate among the members of the three patronage segments. The discrimi- nant analysis was nonsignificant.

“Interests in leisure pursuits” emerged as more predictive. By univariate ANOVA, four variables were found significant at the 0.05 level. The discriminant analysis was significant at p 5 0.004.

“Opinions about professional sports” provided significant prediction of patronage. Seven of the nine variables reached significance at the 0.05 level in univariate analysis. The discriminant analysis was significant beyond p 5 0.001, and it produced two significant functions. The first significant function provided 79.8 percent of the explained variance, and the second function provided 20.2 percent.

Finally, the set “demographics” was also found to be related to patronage. Counting the four dummy-coded measures of dwelling type and the five similar preferences for radio programming as sepa- rate variables, 7 of the 22 demographics reached significance in uni- variate analysis. The discriminant analysis was significant at p 5 0.004.

Question Interpret the managerial significance of the ANOVA and multiple discriminant analysis results.

608 Part 6: Data Analysis and Presentation

How do We Keep Them? Download the data sets for this case from the website provided by your instructor, or request them from your instructor.

Use the data labeled profit for this case. The data go along with the Research Snapshot “Too Much of a Good Thing!”. In addition, management has collected several semantic differential scales from the managers asking them to use emotions to describe the way they feel about their jobs. The emotions include:

involved exciting fun satisfied happy pleased

The managers want to understand turnover. So, another vari- able is included that gives the likelihood a manger will quit within 12 months (labeled turnover in data). After running some initial

regression models with eight independent variables predicting turn- over, management was confused. They complained that there were too many variables to make sense of.

Thus, the researcher turned to a data reduction technique. After- wards, a regression model with fewer independent variables gave some clear direction regarding emotions and turnover.

Questions 1. Perform the appropriate multivariate technique to identify

underlying dimensions that may exist among the emotion ratings. 2. Create scales for any underlying dimensions. 3. Use these scales as independent variables in a regression model. 4. Interpret the results.

CASE 24.2

Chapter 24: Multivariate Statistical Analysis 609

610

After studying this chapter, you should be able to

1. Discuss the research report from the perspective of the communications process

2. Define the parts of a research report following a standard format

3. Explain how to use tables for presenting numerical information

4. Summarize how to select and use the types of research charts

5. Describe how to give an effective oral presentation

6. Discuss the importance of Internet reporting and research follow-up

C H A P T E R

A significant part of this text has been devoted to the development and execution of business research, but perhaps the most important compo- nent of the business research process has yet to

be discussed. Regardless of the research topic or audience, you as a business researcher are faced with communicating your efforts to appropriate stakeholders. This is not a straightforward task for many researchers, who often find themselves staring at a blank computer screen, with a title on the page, and nothing else. To help you get started, you should reflect on a few criti- cal questions regarding your efforts. Fortunately, Tim North of Better Writing Skills (http://www.betterwritingskills.com) pro- vides some tips to help you frame this important aspect of your research.1

First, you must understand who the readers are, and focus your writing on those stakeholders who are most likely to use your results. It is difficult to satisfy everyone’s needs, so identify- ing the key readers of your business research is a must. Second, you must ask yourself why the readers want your results. Is it for evaluation purposes, to make decisions, or simply for

information? Third, as you write your report you should under- stand what your stakeholders expect, and how much they already understand about the project or program you are writ- ing about. Rehashing what is already known or commenting on results that are not particularly relevant will only reduce your report’s impact.

LEARNING OUTCOMES

Communicating Research Results Report Generation, Oral Presentation, and Follow-Up

Chapter Vignette:

A Business Report Title (and Nothing Else)— Tips to Get Started

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Introduction Why should a careful researcher have to be a good writer, too? After the researcher has spent days, weeks, or even months working on a project, preparation of the report may feel like an anticlimac- tic formality. All the “real” work has been done; it just has to be put on paper. This attitude can be disastrous, however. Even if the project was well designed, the data carefully obtained and analyzed by sophisticated statistical methods, and important conclusions reached, unless the report can effec- tively communicate this information all of the earlier efforts will have been wasted. Frequently, the research report is the only part of the project that others ever see. If people who need to use the research results have to wade through a disorganized presentation, are confused by technical jargon, or find sloppiness of language or thought, they will probably discount the report and make deci- sions without it, just as if the project had never been done. The research report is a crucial means for communicating the whole project. This chapter explains the communication of research results with written reports, oral presentations, and follow-up conversations.2

Insights from the Communications Model Some insights from the theory of communications help to clarify the importance of the research report. Exhibit 25.1 illustrates one view of the communication process. Several elements influence successful communication.

■ The communicator—the source or sender of the message (the writer of the report) ■ The message—the set of meanings being sent to or received by the audience (the findings of

the research project) ■ The medium—the way in which the message is delivered to the audience (the oral or written

report itself )

EXHIBIT 25.1

The Communication Process

1. Communicator 3. Medium 2. Message

Medium Original audience

Message 5. Feedback

With What Effect

Who Says What In What Way To Whom

Original communicator

4. Audience

‘‘It is a luxury to be understood.’’—RALPH WALDO EmERSON

communication process

The process by which one person or source sends a message to an audience or receiver and then receives feedback about the message.

Taking what for some stakeholders is a complicated and statistics-heavy set of business research results and translat- ing them into a useful and clearly written research report is not always easy. It takes practice, and at times guidance and mentor ship from more experienced colleagues. Nonetheless,

it is a crucial step in the business research process. Simply put, your elegant research results are meaningless if you cannot communicate them effectively. Taking advantage of Tim North’s guides to better writing may be the first step toward complet- ing this journey.

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CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 611

612

■ The audience—the receiver or destination of the message (the manager who will make a deci- sion based—we hope—on the report findings)

■ Feedback—a communication, also involving a message and channel, that flows in the reverse direction (from the audience to the original communicator) and that may be used to modify subsequent communications (the manager’s response to the report)

This model may make communication seem simple. In fact, communication is simple when the message flows smoothly from writer to reader, and then in return, from reader to writer to provide feedback. In most situations, however, communication is more complex. Exhibit 25.2 illustrates one key difficulty. The communicator and the audience each have individual fields of experience. These overlap to some extent; otherwise no communication would be possible. Still, a great deal of experience is not common to both parties. As communicators send a message, they encode it in terms that make sense to them based on their fields of experience. As the individuals in the audi- ence receive the message, they decode it based on their own fields of experience. The message is successfully communicated only if the parties share enough common experience for it to be encoded, transmitted, and decoded with roughly the same meaning.

In the research setting, the communicator (the researcher) has spent a great deal of time study- ing a problem. He or she has looked at secondary sources, gathered primary data, used statistical techniques to analyze the data, and reached conclusions. When the report on the project is written, all this “baggage” will affect its contents. On the assumption that the reader has a lot of background information on the project, the researcher may produce pages and pages of unexplained tables, expecting the reader to unearth from them the same patterns that the researcher has observed. The report may contain technical terms such as parameter estimate, F-distribution, statistical significance, correlations, and eigenvalue on the assumption that the reader will understand them. Another researcher may assume that the reader does not have a lot of background information

SURVEY

THIS! The development of a research report starts with building a good research question. Imagine that your supervisor assigns you the task of developing a report that com- ments on the work environment and experiences of stu- dents who may be potential employees of a firm.

1. Design an appropriate research question to answer this request.

2. Using Survey This! items, develop and conduct an analysis that addresses your research question.

3. Build appropriate tables and visual aids to support your findings and conclusions.

4. Draft a business research report that includes at least exhibits.

5. Develop and present a PowerPoint presentation of your results.

EXHIBIT 25.2

Communication Occurs in a Common Field of Experience

MessageCommunicator Audience Encoding Decoding

Communication

Field of Experience Field of Experience

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and may go overboard explaining everything in the report in sixth-grade terms. Although the researcher’s intent is to ensure that the reader will not get lost, this effort may insult the reader.

Usually when readers receive a report, they have not thought much about the project. They may have limited knowledge of statistics and many other responsibilities. If they cannot understand the report quickly, they may put it on a stack of things to deal with “someday.”

Simply delivering a report to its audience is not sufficient to ensure that it gets attention. The report needs to be written so as to draw on the common experience of the researcher and the reader. The person responsible for making sure that it does so is the writer—not the reader. Unless a report is really crucial, a busy reader will not spend time and effort struggling through an inad- equate or difficult-to-read document.

The Report in Context A research report is an oral presentation and/or written statement whose purpose is to communi- cate research results, strategic recommendations, and/or other conclusions to management or other specific audiences. Although this chapter deals primarily with the final written report required by an extensive research project, remember that the final report is not likely to be the only kind prepared. For a small project, a short oral or written report on the results may be all that is needed. Extensive projects may involve many written documents, interim reports, a long final written report, and sev- eral oral presentations. In addition, technical materials may be posted on an organization’s intranet.

The chapter’s emphasis on the final report should not be taken to mean that other communi- cations, such as progress reports during the course of the project, are any less important to the proj- ect’s eventual success. The chapter’s suggestions can be easily adapted to apply to these additional communications and shorter, less formal reports.

Report Format Although every research report is custom-made for the project it represents, some conventions of report format are universal. They represent a consensus about the parts necessary for a good research report and how they should be ordered. This consensus is not a law, however. Every book on report writing suggests the use of its own unique format, and every report writer has to pick and choose the sections and order that will work best for the project at hand. Many companies and universities also have in-house report formats or writing guides for writers to follow. The format described in this section serves as a starting point from which writers can shape their own appro- priate format. It includes seven major elements:

1. Title page (sometimes preceded by a title fly page) 2. Letter of transmittal 3. Letter of authorization 4. Table of contents (and lists of figures and tables) 5. Executive summary

a. Objectives b. Results c. Conclusions d. Recommendations

6. Body a. Introduction

1. Background 2. Objectives

b. Methodology c. Results d. Limitations e. Conclusions and recommendations

research report

An oral presentation or written statement of research results, strategic recommendations, and/or other conclusions to a specific audience.

report format

The makeup or arrangement of parts necessary to a good research report.

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 613

7. Appendix a. Data collection forms b. Detailed calculations c. General tables d. Bibliography e. Other support material

This format is illustrated graphically in Exhibit 25.3.

Tailoring the Format to the Project The format of a research report may need to be adjusted for two reasons: (1) to obtain the proper level of formality and (2) to decrease the complexity of the report. The format given here is for the most formal type of report, such as one for a large project done within an organization or one done by a research agency for a client company. This type of report is usually bound in a permanent cover and may be hundreds of pages long.

In less formal reports, each section is shorter, and some parts are omitted. Exhibit 25.4 illus- trates how the format is adapted to shorter, less formal reports. The situation may be compared to the way people’s clothing varies according to the formality of the occasion. The most formal report is dressed, so to speak, in a tuxedo or long evening gown. It includes the full assortment of prefatory parts—title fly page, title page, letters of transmittal and authorization, and table of contents. Like changing into a business suit, dropping down to the next level of formality involves eliminating parts of the prefatory material that are not needed in this situation and reducing the complexity of the report body. In general, as the report moves down through the sport coat and slacks and then blue jeans stages, more prefatory parts are dropped, and the complexity and length of the report body are reduced.

How does the researcher decide on the appropriate level of formality? The general rule is to include all the parts needed for effective communication in the particular circumstances—and no more. This depends on how far up in management the report is expected to go and how routine the matter is. A researcher’s immediate supervisor does not need a 100-page, “black-tie” report on

EXHIBIT 25.3

Report Format

Objectives

Results

Conclusions

Recommendations

Area 1

Area 2

Final area

Appended parts

Letter of transmittal

Letter of authorization

Table of contents

Summary

Prefatory parts

Introduction

Methodology

Results Limitations

Conclusions and recommendations

Data collection forms

Detailed calculations

General tables

Bibliography

Report parts

Title page

Main body ©

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614 • PART SIX Data Analysis and Presentation

a routine project. However, the board of directors does not want a one-page “blue jeans” report on a big project that backs a major expansion program. The formal report to top management may later be stripped of some of the prefatory parts (and thus reduced in formality) for wider circula- tion within the company.

The Parts of the Report The guidelines that call for each element of the research report also dictate the content of each part.

Title Page The title page should state the title of the report, for whom the report was prepared, by whom it was prepared, and the date of release or presentation. The title should give a brief but complete indication of the purpose of the research project. Addresses and titles of the preparer and recipi- ent may also be included. On confidential reports, the title page may list the people to whom the report should be circulated. For the most formal reports, the title page is preceded by a title fly page, which contains only the report’s title.

Letter of Transmittal Relatively formal and very formal reports include a letter of transmittal. Its purpose is to release or deliver the report to the recipient. It also serves to establish some rapport between the reader and the writer. This is the one part of the formal report in which a personal or even slightly informal tone should be used. The transmittal should not dive into the report findings except in the broad- est terms.

Exhibit 25.5 presents a sample letter of transmittal. Note that the opening paragraph releases the report and briefly identifies the factors of authorization. The letter comments generally on findings and matters of interest regarding the research. The closing section expresses the writer’s personal interest in the project just completed and in doing additional, related work.

EXHIBIT 25.4

Adapting Report Format to Required Formality

Title fly page Title page Letter of transmittal Letter of authorization Table of contents Summary Report body Appendix

Title page Letter of transmittal Table of contents Summary Report body Appendix

Title page Table of contents Summary Report body Appendix

Title page Table of contents Report body Appendix

Title page Report body

Report body Summary

Diminishing Need for Formality

‘‘The covers of this book are too far apart.’’—AmBROSE BIERCE

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CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 615

Letter of Authorization The letter of authorization is a letter to the researcher that approves the project, details who has responsibility for it, and describes the resources available to support it. Because the researcher would not write this letter personally, writing guidelines will not be discussed here. In many situa- tions, simply referring to the authorization in the letter of transmittal is sufficient. If so, the letter of

EXHIBIT 25.5

Example Transmittal Letter EMR ResearchGroup Moving you forward!____________________________________________ __________________________________________________________________

August 30, 2012

Mr. Mario Lagasto President, Leading Edge Food Group Columbia, IA 50057

Re: Presentation of Research Identifying Customer Loyalty

Dear Mr. Lagasto:

The report outlined in the research proposal of March 15, 2012, is complete. I have personally supervised the project, conducted the statistical analyses, and prepared this report along with my two senior research associates, Natalia James and David Parker.

The report addresses the key decision statement: In what ways can your restaurants build customer loyalty so that revenues increase through more frequent patronage? The key research questions involve identifying controllable characteristics that end up relating to greater share of wallet. As agreed upon in the proposal, the report offers no specific recommendations for managerial action, but rather, it presents conclusions which should enable you to make informed decisions. Thus, the conclusions conform to the deliverables described in the proposal letter.

We successfully accomplished the research project as described in the outline. We were able to meet our goals for interviewing groups of customers and non-customers in a timely fashion. We are grateful for your business and look forward to working with you as you develop strategic plans of action based on this report. Once you have taken a look at the report, please contact me and we will schedule a formal presentation and question and answer period for your management team.

Sincerely,

Barry J. Babin President

__________________________________________________________________ EMR Research Group 114 Railroad Ave Choudrant, LA 71272

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616 • PART SIX Data Analysis and Presentation

617

authorization need not be included in the report. In some cases, though, the reader may be unfa- miliar with the authorization or may need detailed information about it. In such cases, the report should include this letter, preferably an exact copy of the original

Table of Contents A table of contents is essential to any report more than a few pages long. It should list the divisions and subdivisions of the report with page references. The table of contents is based on the final outline of the report, but it should include only the first-level subdivisions. For short reports it is sufficient to include only the main divisions. If the report includes many figures or tables, a list of these should immediately follow the table of contents.

Summary The summary, also known as the executive summary, briefly explains why the research project was conducted, what aspects of the problem were considered, what the outcome was, and what should be done. It is a vital part of the report. Studies have indicated that nearly all managers read a report’s summary, while only a minority read the rest of the report. Thus, the writer’s only chance to produce an impact may be in the summary.

The summary should be written only after the rest of the report has been completed. It is like the “greatest hits” of the report. It should be just one or two pages long, so the writer must carefully sort out what is important enough to be included in it. Several pages of the full report may have to be condensed into one summarizing sentence. Some parts of the report may be condensed more than others; the number of words in the summary need not be in proportion to the length of the section being discussed. The summary should be written to be self-sufficient. In fact, the summary is often detached from the report and circulated by itself.

The summary contains four elements. First, it states the objectives of the report, including the most important background information and the specific purposes of the project. Second, it pres- ents the methodology and the major results. Finally, the conclusions of the report are presented. These are opinions based on the results and constitute an interpretation of the results. Finally come recommendations, or suggestions for action, based on the conclusions. In many cases, managers prefer not to have recommendations included in the report or summary. Whether or not recom- mendations are to be included should be clear from the particular context of the report.

RESEARCH SNAPSHOT

The research summary, like the original research design, should be based on the business problem to be solved and the result- ing insight into how to address that problem. In today’s com- petitive business environment, companies cannot afford to do research just because it is interesting. They need research that helps them compete as an organization.

Logically, this should mean that companies are measuring whether research helps their bottom line. In practice, however, measuring the return on investment (ROI) for research is still a relatively new idea. Research consultants at A. Dawn Lesh International surveyed companies to find out how they measure the effectiveness of their research projects. The firm discovered that only 10 to 15 percent of the companies measured research effectiveness at all. Some of those that did measure it simply looked at whether the project’s client was satisfied or whether the quality of the work was high. In other cases, researchers determined the expected value of each idea uncovered by the research project.

Lesh International proposes measures that put a dollar value on the research. One of these, which the firm calls ROI Lite, is the dollar value of the decision multiplied by the client’s estimate of the increased confidence that the right alternative will be selected, divided by the cost of the research. The second measure, ROI Complete, incorporates the likelihood that the research client will act on the information.

Sources: Based Maddox, Kate, “The ROI of Research,” B to B, 89 (April 5, 2005), 25–26; Maddox, Kate, “Market Research Charges Online,” B to B, 90 (April 4, 2005), 28–31; Hieggelke, Brent, “Marketing & ROI,” iMedia Connection, (October 19, 2004), http://www.imediaconnection.com/ content/4430.asp, accessed August 21, 2011.

Research ROI

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An additional element that can be included in the summary is a short justification for the research study and report itself. As seen in the Research Snapshot “How Do We Stack Up? The Value of sba.gov,” the use of ROI to measure research effectiveness is a new way that business researchers are providing this justification.

Body The body constitutes the bulk of the report. It begins with an introduction section setting out the background factors that made the project necessary as well as the objectives of the report. It con- tinues with discussions of the methodology, results, and limitations of the study and finishes with conclusions and recommendations based on the results.

The introduction explains why the project was done and what it aimed to discover. It should include the basic authorization and submittal data. The relevant background comes next. Back- ground information is important, and may require that you gather additional external data as discussed in the “How Do We Stack Up?” Research Snapshot. Enough background should be included to explain why the project was worth doing, but unessential historical factors should be omitted. The question of how much is enough should be answered by referring to the needs of the audience. A government report that will be widely circulated requires more background than a company’s internal report on customer satisfaction. The last part of the introduction explains exactly what the project tried to discover. It discusses the statement of the problem and research questions as they were stated in the research proposal. Each purpose presented here should have a corresponding entry in the results section later in the report.

The second part of the body is the research methodology section. This part is a challenge to write because it must explain technical procedures in a manner appropriate for the audience. The material in this section may be supplemented with more detailed explanations in the appendix or a glossary of technical terms. This part of the report should address four topics:

1. Research design. Was the study exploratory, descriptive, or causal? Did the data come from primary or secondary sources? Were results collected by survey, observation, or experiment? A copy of the survey questionnaire or observation form should be included in the appendix. Why was this particular design suited to the study?

2. Sample design. What was the target population? What sampling frame was used? What sample units were used? How were they selected? How large was the sample? What was the response rate? Detailed computations to support these explanations should be saved for the appendix.

many business research projects are designed to capture infor- mation on attitudes and behaviors that are reflective of the company. The bigger question to your research stakeholders may be, “How do we stack up with other similar firms?” When writing a research report, it often helps to provide some kind of context, whether it is comparative data from other firms, or general information that the report reader can use to get the big picture. Obviously, you can’t always get the exact same data from other similar firms. But you may be able to get at least some level of context, if you look in the right place. One place to look is the small business administration site (http://www .sba.gov), the clearinghouse website that captures governmental and economic statistics from agencies across the United States.

Sba.gov includes statistics on employment, consumer spending, production, trade, and other firm-level or industry- level data that may be useful to you as you frame your research

results. For example, if your research report is focused on turn- over trends in your firm, you may be able to capture industry-wide employment trends to compare your results to the larger picture within an industry or geographic location.

The value of sba.gov is the potential to compare how your firm is doing to the “big picture.” While it may not have results for every research project or report, it is worth searching through as you develop the body of your report.

Source: http://www.sba.gov, accessed August 11, 2011.

How Do We Stack Up? The Value of Sba.gov

RESEARCH SNAPSHOT

introduction section

The part of the body of a research report that discusses background information and the specific objectives of the

research.

research methodology section

The part of the body of a report that presents the

findings of the project. It includes tables, charts, and an

organized narrative.

Co ur

te sy

, S BA

.g ov

3. Data collection and fieldwork. How many and what types of fieldworkers were used? What training and supervision did they receive? Was the work verified? This section is important for establishing the degree of accuracy of the results.

4. Analysis. This section should outline the general statistical methods used in the study, but the information presented here should not overlap with what is presented in the results section.

The results section should make up the bulk of the report and should present, in some logi- cal order, those findings of the project that bear on the objectives. The results should be organized around the research objectives, providing an answer to the research questions. The results should be convincing, but not oversell the project. Summary tables and charts should be used to aid the discussion. These may serve as points of reference to the data being discussed and free the prose from excessive facts and figures. Comprehensive or detailed charts, however, should be saved for the appendix.

Because no research is perfect, its limitations should be indicated. If problems arose with non- response error or sampling procedures, these should be discussed. However, the discussion of limi- tations should avoid overemphasizing the weaknesses; its aim should be to provide a realistic basis for assessing the results.

The last part of the body is the conclusions and recommendations section. As mentioned ear- lier, conclusions are opinions based on the results, and recommendations are suggestions for action. The conclusions and recommendations should be presented in this section in more detail than in the summary, and the text should include justification as needed.

Appendix The appendix presents the “too . . .” material. Any material that is too technical or too detailed to go in the body should appear in the appendix. This includes materials of interest only to some readers or subsidiary materials not directly related to the objectives. Some examples of appen- dix materials are comprehensive discussions of the research methodology, data collection forms, detailed calculations, discussions of highly technical questions, detailed or comprehensive tables of results, and a bibliography (if appropriate). Since the advent of company intranets, much appendix material is posted on internal web pages.

Basic Business Research Report The outline described applies especially to applied business research projects. When pure or basic research reports are written, such as might be submitted and potentially published in an academic business journal, the outline changes slightly since some components become irrelevant. A com- mon outline used in basic business research proceeds as follows:

1. Abstract 2. Introduction 3. Background

a. Literature review b. Hypotheses

4. Research methods 5. Results 6. Discussion

a. Implications b. Limitations c. Future research

7. Conclusions 8. References 9. Appendices

The material in the sections does not change very much between different business research prob- lems, so here we only note exceptions. The basic research report will place a greater emphasis on

results section

The part of the body of a report that presents the findings of the project. It includes tables, charts, and an organized narrative.

conclusions and recommendations section

The part of the body of a report that provides opinions based on the results and suggestions for action.

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 619

how the current research is integrated into the previous literature dealing with the research topic. This section finishes with a specific set of theoretical hypotheses. The research methodology and results section may contain more statistical detail and jargon since the reader is expected to be knowledgeable in basic research methodology. A quick look at an academic business journal like the Journal of Business Research, the Journal of Marketing, the Journal of Finance, or the Journal of Management will give a reader a feel for this type of writing. Overall, though, both basic and applied business research reports involve technical writing and the principles of good technical writing apply.

Effective Use of Graphic Aids Used properly, graphic aids can clarify complex points or emphasize a message. Used improperly or sloppily, they can distract or even mislead a reader. Graphic aids work best when they are an integral part of the text. The graphics should always be interpreted in the text. This does not mean that the writer should exhaustively explain an obvious chart or table, but it does mean that the text should point out the key elements of any graphic aid and relate them to the discussion in progress.

Several types of graphic aids may be useful in research reports including tables, charts, maps, and diagrams. The following discussion briefly covers the most common ones, tables and charts. The reader interested in other types of graphic material should consult more specialized sources.

Tables Tables are most useful for presenting numerical information, especially when several pieces of information have been gathered about each item discussed. For example, consider how hard it might be to follow the information in Exhibit 25.6 with only narrative text and no graphical aids. Using tables allows a writer to point out significant features without getting bogged down in detail. The body of the report should include only relatively short summary tables, with comprehensive tables reserved for an appendix.

EXHIBIT 25.6

Parts of a Table

Table 1024. Retail Sales—New Passenger Cars: 2000 to 2008

Item 2000 2001 2002 2003 2004 2005 2006 2007 2008

Total new passenger car sales(1,000)

8,847 8,423 8,103 7,610 7,545 7,720 7,821 7,618 6,813

Domestic1 6,831 6,325 5,878 5,527 5,396 5,533 5,476 5,253 4,535

Imports 2,016 2,098 2,226 2,083 2,149 2,187 2,345 2,365 2,278

Japan 863 837 930 830 810 923 1,154 1,183 1,142

Germany 517 523 547 544 542 534 561 567 507

Other 637 738 749 709 797 729 630 615 630

1 Includes cars produced in Canada and Mexico. Source: U.S. Census Bureau, Statistical Abstract of the Unistated States, “Retail Sales and Leases of New and Used Vehicles,” accessed August 11, 2011 Southfield. MI: Ward’s Motor Vehicle Facts & Figures, 2002. Southfield, MI: 2002. See also <http://www.bts.gov>.

— Represents zero. 1Change from prior year. Source: U.S. Census Bureau, Statistical Abstract of the United States, 2010, table 1024, p. 678.

St u

b h

e a d

s Fo

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o te

B a n

n e rh

e a d

Table number Title

graphic aids

Pictures or diagrams used to clarify complex points or

emphasize a message.

Source

620 • PART SIX Data Analysis and Presentation

Each table should include the following elements:

■ Table number. This allows for simple reference from the text to the table. If the text includes many tables, a list of tables should be included just after the table of contents.

■ Title. The title should indicate the contents of the table and be complete enough to be intel- ligible without referring to the text.

■ Stubheads and bannerheads. The stubheads contain the captions for the rows of the table, and the bannerheads (or boxheads) contain those for the columns.

■ Footnotes. Any explanations or qualifications for particular table entries or sections should be given in footnotes.

■ Source notes. If a table is based on material from one or more secondary sources rather than on new data generated by the project, the sources should be acknowledged, usually below the table.

Tables in a survey research report typically follow the format shown in Exhibit 25.7. This example cross-tabulates demographics with survey responses. Data from a statistical test also might be reported in table form, as shown in Exhibit 25.8.

EXHIBIT 25.7

Reporting Format for a Typical Cross-Tabulation

Age Group

Online Activity 12–17 18–28 29–40 41–50 51–59 60–69 70+

E-mail 89% 88% 92% 90% 94% 90% 89%

Online games 81% 54% 37% 29% 25% 25% 32%

Instant messaging 75% 66% 52% 38% 42% 33% 14%

Downloading music 51% 45% 28% 16% 14% 8% 5%

Job hunting 30% 62% 51% 40% 36% 17% 2%

Job research — 44% 59% 59% 54% 31% 13%

Source: Excerpted from Fox, Susannah and Mary Madden, “Generations Online,” Pew Internet and American Life Project, December 2005, p. 3, http://www.pewinternet.org.

EXHIBIT 25.8

Reporting Format for a Typical Statistical Test

Does Customer Satisfaction Change across Mode of Data Collection?

Customer Satisfaction

Web Survey Mail Survey Telephone Survey Total

N 429 160 231 820

Mean 7.76 7.76 7.77 7.76

Std. Deviation 1.89 1.87 1.96 1.91

ANOVA

Sum of Squares df Mean Square F Sig.

Customer Satisfaction Between Groups .045 2 .023 .006 .994

Within Groups 2982.057 817 3.650

Source: Courtesy of authors, proprietary study conducted 2011.

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 621

Suppose an airline asks a question about customers’ satisfaction with its baggage-handling service. In addition to showing the simple frequency for each category, most research analysts would cross- tabulate answers to the baggage-handling questions with several demographic variables such as gender, income, education, and age. To present multiple cross-tabulations individually in separate tables requires considerable space. Thus, many research reports use a space-saving format, with either stubheads for rows or bannerheads for columns, to allow the reader to view several cross-tabulations at the same time. Exhibit 25.9 on the next page presents several cross-tabulations in a single table with stubheads.

EXHIBIT 25.9

Using a Stubhead Format to Include Several Cross-Tabulations in One Table

Level of Highest Degree

Characteristic Total

Persons

Not a High

School Graduate

High School

Graduate Only

Some College,

No Degree Associate’s Bachelor’s Master’s Professional Doctorate

All persons* $37,046 $18,734 $27,915 $29,533 $35, 958 $51,206 $62,514 $115,212 $88,471

Age:

25 to 34 years old 33,212 18,920 26,073 28,954 32,276 43,794 51,040 71,120 62,109

35 to 44 years old 42,475 22,123 31,479 36,038 38,442 57,438 66,264 126,165 101,382

45 to 54 years old 45,908 23,185 32,978 40,291 41,511 59,208 68,344 132,180 92,229

55 to 64 years old 45,154 23,602 31,742 38,131 39,147 57,423 66,760 138,845 98,433

65 years old and older 28,918 17,123 20,618 28,017 23,080 41,323 42,194 77,312 56,724

Sex:

Male 44,726 21,447 33,286 36,419 43,462 63,084 76,896 136,128 95,894

Female 28,367 14,214 21,659 22,615 29,537 38,447 48,205 72,445 73,516

* For persons 18 years old and over with earnings. Source: Excerpted from U.S. Census Bureau, Statistical Abstract of the United States, 2006, Table 217, p. 148.

Charts Charts translate numerical information into visual form so that relationships may be easily grasped. The accuracy of the numbers is reduced to gain this advantage. Each chart should include the following elements:

■ Figure number. Charts (and other illustrative material) should be numbered in a separate series from tables. The numbers allow for easy reference from the text. If there are many charts, a list of them should be included after the table of contents.

■ Title. The title should describe the contents of the chart and be independent of the text expla- nation. The number and title may be placed at the top or bottom of the chart.

■ Explanatory legends. Enough explanation should be put on the chart to spare the reader a need to look at the accompanying text. Such explanations should include labels for axes, scale numbers, and a key to the various quantities being graphed.

■ Source and footnotes. Any secondary sources for the data should be acknowledged. Footnotes may be used to explain items, although they are less common for charts than for tables.

Charts are subject to distortion, whether unintentional or deliberate. Exhibit 25.10 shows how the same data can appear quite differently by altering the scale.

622 • PART SIX Data Analysis and Presentation

EXHIBIT 25.11

Distortion from Changing Vertical Scale

EXHIBIT 25.10

Distortion by Alternating Scales

Changing the Visual Image Contracting or expanding vertical (amount) scale or horizontal (time) scale tends to change the visual picture

Original Scale Arrangement Expanding Vertical

Contracting Horizontal Expanding Horizontal

Contracting Vertical Contracting Vertical and Expanding Horizontal

Expanding Vertical and Contracting

Horizontal

Source: Adapted with permission from Spear, Mary Eleanor, Practical Charting Techniques (© New York: McGraw-Hill, 1969), p. 56.

Another common way of introducing distortion is to have the vertical scale start at some value larger than zero or not reflect the entire range of the scale. Exhibit 25.11 shows how this exaggerates difference between measures. The means presented in Exhibit 25.8 were gathered using a scale from 1 to 10. The exhibit shows how virtually no difference can be detected when the vertical axis includes the entire scale range. However, if the vertical scale is restricted, even these very trivial differences can appear large. The vertical axis of a graph should reflect the scale used for the data collection.

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5

10.0

Customer Satisfaction

Web Survey

Mail Survey

Telephone Survey

7.74

7.75

7.76

7.77

7.78

7.79

Customer Satisfaction

Web Survey

Mail Survey

Telephone Survey

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CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 623

EXHIBIT 25.12

A Simple Pie Chart Units Sold

Full Price

20% Off

50% Off

49%

19%

32%

Pie Charts One of the most useful kinds of charts is the pie chart, which shows the composition of some total quantity at a particular time. As shown in the examples in Exhibits 25.12 and 25.13, each angle, or “slice,” is proportional to its percentage of the whole. Exhibit 25.12 shows a simple pie chart representing the percentage of sales at three different price levels. Exhibit 25.13 illustrates how pie charts can be paired to illustrate differences. Companies often use pie charts to show how revenues were used or the composition of their sales. Each of the segments should be labeled with its description and percentage. The writer should not try to include too many small slices; about six slices is a typical maximum.

Line Graphs Line graphs are useful for showing the relationship of one variable to another. The dependent vari- able generally is shown on the vertical axis, and the independent variable on the horizontal axis. The most common independent variable for such charts is time, but it is by no means the only one. Exhibit 25.14 depicts a simple line graph.

Variations of the line graph also are useful. The multiple-line graph, such as the example in Exhibit 25.15, shows the relationship of more than one dependent variable to the independent variable. The line for each dependent variable should be in a different color or pattern and should be clearly labeled. The writer should not try to squeeze in too many variables; this can quickly lead to confusion rather than clarification.

A second variation is the stratum chart, which shows how the composition of a total quantity changes as the independent variable changes. Exhibit 25.16 provides an example. The same cau- tions mentioned in connection with multiple-line graphs apply to stratum charts.

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624 • PART SIX Data Analysis and Presentation

EXHIBIT 25.13

Pie ChartsU.S. Energy Consumption, 2004 Total 5 100 quadrillion Btu

U.S. Electricity Generation, 2004 Total 5 3,953 billion kWh

40%

23%

8% 6% 3%

18%

50%

20%

Petroleum

Natural Gas

Coal & Coal Coke

Nuclear Power

Renewable Energy

Source: Energy Information Administration, U.S. Department of Energy, “Renewable Energy Sources: A Consumer’s Guide,” EIA Brochures, http://www.eia.gov, accessed March 28, 2006.

EXHIBIT 25.14

Simple Line Graph

0 1960 20202010*

*Projected data for 2004–2014.

20001990 Year

19801970

100

200

300

400

500

600 Spending on Prescription Drugs

Bi lli

on s

of D

ol la

Source: Data from U.S. Census Bureau, Statistical Abstract of the United States, 2006, Table 118, p. 98.

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 625

Bar Charts A bar chart shows changes in the value of a dependent variable (plotted on the vertical axis) at discrete intervals of the independent variable (on the horizontal axis). A simple bar chart is shown in Exhibit 25.17.

Like the line graph, the bar chart format has variations. A common variant is the subdivided-bar chart (see Exhibit 25.18). It is much like a stratum chart, showing the composition of the whole quan- tity. The multiple-bar chart (see Exhibit 25.19) shows how multiple variables are related to the pri- mary variable. In each of these cases, each bar or segment of the bar needs to be clearly identified with a different color or pattern. The writer should not use too many divisions or dependent variables. Too much detail obscures the essential advantage of charts, which is to make relationships easy to grasp.

EXHIBIT 25.16

Stratum Chart

‘77

$800

700

600

500

400

300

200

100

Costs of Regulation

Environmental Regulation Other Social Regulation Economic Regulation Efficiency Costs

Economic Regulation Transfer Costs Paperwork

‘80 ‘82 ‘84 ‘86 ‘88 ‘90 ‘92 ‘94 ‘96 ‘98 2000

Costs of Regulation

EXHIBIT 25.15

Multiple-Line Graph

0 1996 1997 1998 1999 2000

Median Age of Motor Vehicles

Cars

Light Trucks

Median Age (Years)*

*Age calculated as of July 1.

2001 2002 2003 2004 2005

Source: Data from Dolliver, Mark “Aging of America, Vehicular Division,” Adweek, March 6, 2006, downloaded from InfoTrac at http://web2.infotrac.galegroup.com.

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626 • PART SIX Data Analysis and Presentation

EXHIBIT 25.17

Simple Bar Chart

Percent

Ty pe

o f T

re at

m en

Adults Who Have Undergone Cosmetic Treatments

Teeth whitening, bonding, or other cosmetic dental work

Lasik surgery to correct vision

Cosmetic surgery*

Bariatric surgery for weight loss

*Includes face lift, chin implant, tummy tuck, etc. **Includes chemical peels, laser abrasion, etc.

Facial skin resurfacing treatment**

Laser treatment for veins, hair removal, etc.

0 1 2 3 4 5 6 7 8 9 10

Source: Data from Harris Interactive, “Despite Risks, Adults Not Shying Away from Cosmetic Surgery and Other Treatments,” news release, February 13, 2006.

EXHIBIT 25.18

Subdivided Bar Chart

2000 Average Retail Price: $1.48/gallon

2011 Average Retail Price: $3.68/gallon

12%

14%

28%

46%

Distribution & Marketing Costs & Profits

Refining Costs & Profits

Federal & State Taxes

Crude Oil

66%

12%

11%

Source: Energy Information Administration, U.S. Department of Energy, “What We Pay For in a Gallon of Regular Gasoline (June 2011),” http://www.eia.gov/oog/info/gdu/gasdiesel.asp, accessed August 11, 2011.

CHAPTER 25 Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up • 627

The Oral Presentation The conclusions and recommendations of most research reports are presented orally as well as in writing. The purpose of an oral presentation is to highlight the most important findings of a research project and provide clients or line managers with an opportunity to ask questions. The oral presentation may be as simple as a short video conference with a manager at the client organiza- tion’s location or as formal as a report to the company board of directors.

In either situation, the key to effective presentation is preparation. The Research Snapshot “The 10/20/30 Rule of PowerPoint” provides some recommendations that can help you as a pre- senter. Communication specialists often suggest that a person preparing an oral presentation begin at the end.3 In other words, while preparing a presentation, a researcher should think about what he or she wants the client to know when it has been completed. The researcher should select the three or four most important findings for emphasis and rely on the written report for a full sum- mary. The researcher also needs to be ready to defend the results of the research. This is not the same as being defensive; instead, the researcher should be prepared to deal in a confident, compe- tent manner with the questions that arise. Remember that even the most reliable and valid research project is worthless if the managers who must act on its results are not convinced of its importance.

As with written reports, a key to effective oral presentation is adapting to the audience. Delivering an hour-long formal speech when a ten-minute discussion is called for (or vice versa) will reflect poorly on both the presenter and the report.

Lecturing or reading to the audience is sure to impede communication at any level of for- mality. Presenters should refrain from reading prepared text word for word. By relying on brief notes, familiarity with the subject, and as much rehearsal as the occasion calls for, presenters will foster better communication. Presenters should avoid research jargon and use short, familiar words. Presenters should maintain eye contact with the audience and repeat the main points. Because the audience cannot go back and replay what the speaker has said, an oral presentation often is organized around a standard format: “Tell them what you are going to tell them, tell them, and tell them what you just told them.”

Graphic and other visual aids can be as useful in an oral presentation as in a written one. While presenters can choose from a variety of media, most professional presentations are based on

oral presentation

A spoken summary of the major findings, conclusions,

and recommendations, given to clients or line managers to provide them with the opportunity to clarify any

ambiguous issues by asking questions.

EXHIBIT 25.19

Multiple-Bar Chart

100,000

Un its

S ol

Recent Best Selling Cars in the U.S.A.

200,000

300,000

400,000

500,000

600,000

700,000

2007 2008 0

Honda AccordChevy Impala

Chevy Silvarado

Honda Civic

Ford F-150

Toyota Camry

Dodge Ram

Source: “Forbes.com—10 Best Selling Vehicles of 2007,” http://www.autospies.com/news/Forbes-com-10-Best- Selling- Vehicles-of-2007-23985/, accessed August 22, 2011; Mitchell, Jacqueline, “The Year’s Best- And Worst-Selling Cars,” http://www.forbes.com/2008/12/03/2008-car-sales-forbeslife-cx_jm_1203cars.html, accessed August 22, 2011.

628 • PART SIX Data Analysis and Presentation

629

PowerPoint or comparable presentation software. For smaller audiences, the researcher may put the visual aids on posters or flip charts. Another possibility is to make copies of the charts for each participant, possibly as a supplement to one of the other forms of presentation.

Whatever medium is chosen, each visual aid should be designed to convey a simple, attention- getting message that supports a point on which the audience should focus its thinking. As they do in written presentations, presenters should interpret graphics for the audience. The best slides are easy to read and interpret. Large typeface, multiple colors, bullets that highlight, and other artistic devices can enhance the readability of charts.

Using gestures during presentations also can help convey the message and make presentations more interesting. Here are some tips on how to gesture:4

■ Open up your arms to embrace your audience. Keep your arms between your waist and shoulders.

■ Drop your arms to your sides when not using them. ■ Avoid quick and jerky gestures, which make you appear nervous. Hold gestures longer than

you would in normal conversation. ■ Vary gestures. Switch from hand to hand and at other times use both hands or no hands. ■ Don’t overuse gestures.

Some gestures are used to draw attention to points illustrated by visual aids. For these, gesturing with an open hand can seem more friendly and can even release tension related to nervousness. In contrast, a nervous speaker who uses a laser pointer may distract the audience as the pointer jumps around in the speaker’s shaky hand.5

Reports on the Internet or Intranet Many clients want numerous employees to have access to research findings. One easy way to share data is to make executive summaries and reports available on a company intranet. In addition, a com- pany can use information technology on the Internet to design questionnaires, administer surveys, analyze data, and share the results in a presentation-ready format. Real-time data capture allows for beginning-to-end reporting. A number of companies offer fully web-based research management systems—for example, WebSurveyor’s online solution for capturing and reporting research findings.

many business researchers find themselves being asked to provide a presentation of research findings to different stake- holders. Given today’s reliance on visual technology, it is natural to develop a presentation using microsoft’s PowerPoint presen- tation software. But a poorly developed PowerPoint presenta- tion can limit your impact. One way to avoid this is to use Guy Kawasaki’s 10/20/30 Rule of PowerPoint.

The 10 refers to the number of optimal slides to have for your presentation. more than 10 slides can cause your audi- ence to lose interest, causing them to disregard your research findings. The 20 part of the rule is related to the time to actu- ally present the results. In a typical hour-long meeting, any presentation over 20 minutes will start to lose your audience as well, and in many instances the opportunity to discuss or ask questions regarding your research is more valuable than the presentation itself. Finally, Kawasaki recommends that

PowerPoint presentations use, as a minimum, 30-point font size. Readability is more important than an overkill of informa- tion that is unreadable to the audience. Kawasaki even provides a more flexible corollary to the 30 rule—take the age of the oldest person in the audience, and divide by two! Regardless of the size of the font or the length of the presentation, com- municating research should be done carefully, to maximize the impact of the results you obtain. Perhaps the 10/20/30 rule can work for you!

Source: Kawasaki, Guy, “The 10/20/30 Rule of PowerPoint,” How to Change the World (December 30, 2005), http://blog.guykawasaki.com/2005/12/ the_102030_rule.html, accessed August 22, 2011.

The 10/20/30 Rule of PowerPoint

RESEARCH SNAPSHOT

pe nc

er G

ra nt

/P ho

to Ed

The Research Follow-Up Research reports and oral presentations should communicate research findings so that managers can make business decisions. In many cases, the manager who receives the research report is unable to interpret the information and draw conclusions relevant to managerial decisions. For this rea- son, effective researchers do not treat the report as the end of the research process. They conduct a research follow-up, in which they recontact decision makers and/or clients after the latter have had a chance to read over the report. The purpose is to determine whether the researchers need to provide additional information or clarify issues of concern to management.

research follow-up

Recontacting decision makers and/or clients after they have had a chance to read over a

research report in order to determine whether additional information or clarification is

necessary.

SUMMARY

1. Discuss the research report from the perspective of the communications process. A research report is an oral or written presentation of research findings directed to a specific audience to accom- plish a particular purpose. Report preparation is the final stage of the research project. It is important because the project can guide management decisions only if it is effectively communicated. The theory of communications emphasizes that the writer (communicator) must tailor the report (mes- sage) so that it will be understood by the manager (audience), who has a different field of experience.

2. Define the parts of a research report following a standard format. The consensus is that the format for a research report should include certain prefatory parts, the body of the report, and appended parts. The report format should be varied to suit the level of formality of the particu- lar situation. The prefatory parts of a formal report include a title page, letters of transmittal and authorization, a table of contents, and a summary. The summary is the part of a report most often read and should include a brief statement of the objectives, results, conclusions, and (depending on the research situation) recommendations. The report body includes an introduction that gives the background and objectives, a statement of methodology, and a discussion of the results, their limitations, and appropriate conclusions and recommendations. The appendix includes various materials too specialized to appear in the body of the report.

3. Explain how to use tables for presenting numerical information. Tables present large amounts of numerical information in a concise manner. They are especially useful for presenting several pieces of information about each item discussed. Short tables are helpful in the body of the report; long tables are better suited for an appendix. Each table should include a number, title, stubheads and bannerheads, footnotes for any explanations or qualifications of entries, and source notes for data from secondary sources.

4. Summarize how to select and use the types of research charts. Charts present numerical data in a way that highlights their relationships. Each chart should include a figure number, title, explanatory legends, and a source for secondary sources. Pie charts show the composition of a total (the parts that make up a whole). Line graphs show the relationship of a dependent variable (on the vertical axis) to an independent variable (horizontal axis). Most commonly, the independent variable is time. Bar charts show changes in a dependent variable at discrete intervals of the inde- pendent variable—for example, comparing one year with another or one subset of the population with another. Variants of these charts are useful for more complex situations.

5. Describe how to give an effective oral presentation. Most research projects are reported orally as well as in writing, so the researcher needs to prepare an oral presentation. The presenta- tion should defend the results without being defensive. The presentation must be tailored to the situation and the audience. The presenter should practice delivering the presentation in a natural way, without reading to the audience. Graphic aids are useful supplements when they are simple and easy to read. Gestures also add interest and emphasis.

6. Discuss the importance of Internet reporting and research follow-up. Posting a summary of results online gives clients ready access to that information. Some online survey software pro- cesses the data and displays results in a presentation-ready format. In the follow-up stage of a research project, the researchers recontact decision makers after submitting the report. This helps the researchers determine whether they need to provide further information or clarify any issues of concern to management.

630 • PART SIX Data Analysis and Presentation

KEY TERMS AND CONCEPTS communication process, 611 conclusions and recommendations

section, 619 graphic aids, 620

introduction section, 618 oral presentation, 628 report format, 613 research follow-up, 630

research methodology section, 618 research report, 613 results section, 619

QUESTIONS FOR REVIEW AND CRITICAL THINKING 1. Why is it important to think of the research report from a

communications perspective? 2. As a manager, what degree of formality would you want from

your research department? 3. What types of tables might be used to describe some of the

statistical tests discussed in previous chapters? 4. What is the difference between a basic business research paper

and an applied research report? 5. What is a pie chart? What is a bar chart? When might one be

preferable over the other? 6. What are some basic business research journals? Find some

published research reports in these journals. How do they meet the standards set forth in this chapter?

7. What rules should be followed when preparing slides for computer-generated presentations?

8. ETHICS What ethical concerns arise when you prepare (or read) a report?

9. ETHICS A researcher working for Hi Time prepares a bar chart comparing the number of customers visiting two competing booths at a fashion trade show. One booth is the Hi Time booth, and the other is for a competing company, So Cool. First, the chart is prepared as shown here:

In preparing for a presentation to the Hi Time Board, the client tells the researcher that the chart doesn’t seem to reflect the improvements made since 2006. Therefore, the researcher prepares the chart as shown here:

a. What has reformatting the bar chart accomplished? b. Was it ethical for the client to ask for the bar chart to be

redrawn? c. Would it be ethical for the researcher to use the new chart

in the presentation?

2006 2011

100

150

200

250

So Cool Hi Time

225

230

2006 2011

235

240

245

250

So Cool Hi Time

RESEARCH ACTIVITIES 1. ‘NET Input “Starbucks” or “McDonald’s” in an Internet search

engine like Google News. Look at the news and articles for that company. Limit the search by using the word “report.” Find one

of the articles that actually presents some research reports, such as consumer reactions to a new product. Prepare PowerPoint slides that contain appropriate charts to present the results.

Chapter 25: Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 631

A study by the Annenberg Public Policy Center investigated one major area of business decisions: pricing practices.6 Specifically, the study addressed

consumer knowledge and attitudes about the practice of online retailers adjusting their prices according to customer characteristics, such as how frequently they buy from the retailer. For example, a website selling cameras charged different prices for the same model depending on whether the visitor to the site had previously visited sites that supply price comparisons. In general, charging different prices is called price discrimination and is legal unless it discriminates by race or sex or involves antitrust or price-fixing laws (such as two competitors agreeing to charge certain prices).

The Annenberg study consisted of telephone interviews con- ducted with a sample of 1,500 adults, screened to find persons who had used the Internet in the preceding 30 days. The question- naire gathered demographic data and data about Internet usage. In addition, the interviewer read 17 statements about basic laws

and practices related to price discrimination and the targeting of consumers according to their shopping behaviors. Respondents were asked whether each of these statements was true or false. Case Exhibit 25.1-1 Exhibit 25.1-4 summarize some of the results from this study.

Questions 1. The information provided here is not detailed enough for a for-

mal report, but assume that you are making an informal report in a preliminary stage of the reporting process. Which of these findings do you want to emphasize as your main points? Why?

2. Prepare a written summary of the findings, using at least two tables or charts.

3. Prepare two tables or charts that would be suitable to accompany an oral presentation of these results. Are they different from the visual aids you prepared for question 2? Why or why not?

Annenberg Public Policy Center CASE 25.1

CASE EXHIBIT 25.1-1

Selected Information about the Sample

Sex

Male 48%

Female 52%

Online Connection at Home

Dial-up connection only 31%

Cable modem (with/without dial-up) 18%

DSL (with/without dial-up) 25%

Cable or DSL with another method 13%

Don’t know 4%

No connection at home 9%

Self-Ranked Expertise Navigating the Internet

Beginner 14%

Intermediate 40%

Advanced 34%

Expert 12%

Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, p. 15, downloaded at http://www.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_ appc_report_web_final.pdf, accessed August 22, 2011.

CASE EXHIBIT 25.1-2

Responses to Selected Knowledge Questions

Response*

Statement True False Don’t Know

Companies today have the ability to follow my activity across many sites on the web.

80% 8% 12%

It is legal for an online store to charge different people different prices at the same time of day.

38% 29% 33%

By law, a site such as Expedia or Orbitz that compares prices on different airlines must include the lowest airline prices.

37% 32% 31%

It is legal for an offline store to charge different people different prices at the same time of day.

29% 42% 29%

When a website has a privacy policy, it means the site will not share my information with other websites or companies.

59% 25% 16%

* When the numbers do not add up to 100%, it is because of a rounding error. Boldface type indicates the correct answer.

Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, p. 20, downloaded at http://www.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_ appc_report_web_final.pdf, accessed August 22, 2011.

(Continued )

632 Part 6: Data Analysis and Presentation

Case 25.1 (Continued )

CASE EXHIBIT 25.1-3

Responses to Selected Attitude Questions

Response*

Statement Agree Disagree Neutral Don’t Know

It’s okay if a store charges me a price based on what it knows about me.

8% 91% — 1%

It’s okay if an online store I use charges different people different prices for the same products during the same hour.

11% 87% 1% 1%

It would bother me to learn that other people pay less than I do for the same products.

76% 22% 1% 1%

It would bother me if websites I shop at keep detailed records of my buying behavior.

57% 41% 2% 1%

It’s okay if a store I shop at frequently uses information it has about me to create a picture of me that improves the services it provides for me.

50% 47% 2% 1%

* When the numbers do not add up to 100%, it is because of a rounding error.

Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, p. 22, downloaded at http://www .annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_appc_report_ web_final.pdf, accessed August 22, 2011.

CASE EXHIBIT 25.1-4

Predicting Knowledge Score from Selected Demographics

Unstandardized Regression Coefficient (B)

Standardized Regression Coefficient ( )

Education 0.630* 0.200

Income 0.383* 0.150

Self-perceived ability to navigate Internet

0.616* 0.149

Constant 2.687

R2 0.148

* Significance < 0.001 level.

Source: Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, p. 29, downloaded at http://www.annenbergpublicpolicycenter.org/Downloads/information_and_society/turow_ appc_report_web_final.pdf, accessed August 22, 2011.

Chapter 25: Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up 633

P A R T S E V E N

Comprehensive Cases with Computerized Databases

CASE 1 Running the Numbers: Does It Pay?

CASE 2 Attiring Situation

CASE 3 Values and the Automobile Market

CASE 4 TABH, INC., Automotive Consulting

CASE 5 The Atlanta Braves

CASE 6

Knowing the Way

Running the Numbers: Does It Pay? (Download the data sets for this case from www.cengagebrain.com or request them from your instructor.) Dr. William Ray, a research con-

sultant, has received a government grant of $75,000 to fund research examining how aspects of a student’s college experiences relate to his or her job performance. Senator B. I. G. Shot is being lobbied by his constituents that employers are discriminating against people who do not like math by giving them lower salaries. Senator Shot has obtained $50,000 of the $75,000 grant from these constituents. The senator was also instrumental in the selection of Dr. Ray as the recipient and hopes the research supported by the grant will help provide a basis to support the proposed legislation making discrimi- nation against people who do not like math illegal.

The research questions listed in this particular grant proposal include:

RQ1: Does a student’s liking of quantitative coursework in col- lege affect his or her future earnings?

RQ2: Do people with an affinity for quantitative courses get promoted more quickly than those who do not?

Dr. Ray has gained the cooperation of a Fortune 500 service firm that employs over 20,000 employees across eight locations. The company allows Dr. Ray to survey employees who have been out of college for three years. Three hundred responses were obtained by sending an e-mail invitation to approximately 1,000 employees who

fit this profile. The invitation explained that the research was about various employee attitudes and indicated that employees would not be required to identify themselves during the survey. Respondents were informed that all responses would be strictly confidential. The e-mail provided a click-through questionnaire which directed respondents to a website where the survey was conducted using an online survey provider. Each invitation was coded so that the actual respondents could be identified by both e-mail address and name. Dr. Ray, however, kept this information confidential so the company could not identify any particular employee’s response.

The following table describes the variables that were collected.

Variables Available from Company Records

Variable Name Variable Type Coding

PROM Nominal indicating whether the employee has been promoted

1 5 Promoted 0 5 Not Promoted

GPA Self-reported GPA in last year of college

0 (lowest) to 4 (highest)

Sex Nominal 1 5 Female 0 5 Male

School Nominal School initials

Salary Ratio Actual annual salary from last year

COMPREHENSIVE CASES

CASE 1

Questions from Survey

Coding Strongly Disagree

(1) Disagree

(2) Neutral

(3) Agree

(4) Strongly Agree

(5)

X 1

The quantitative courses I took in school were the most useful courses.

□ □ □ □ □

X 2

Very few topics can be understood if you do not understand the arithmetic.

□ □ □ □ □

X 3

I hated going to math classes in college. □ □ □ □ □

X 4

I learned a great deal from the quantitative projects assigned to me in college.

□ □ □ □ □

X 5

Students do not need to study quantitative topics in college to succeed in their careers.

□ □ □ □ □

636

(Continued )

Please use the following items to describe your undergraduate college experience. For each pair of items, choose the check box closest to the adjective that best describes your experience.

Coding (−3) (−2) (−1) (0) (1) (2) (3)

S 1

Dull □ □ □ □ □ □ □ Exciting

S 2

Laborious □ □ □ □ □ □ □ Playful

S 3

Stressful □ □ □ □ □ □ □ Relaxing

S 4

Boring □ □ □ □ □ □ □ Fun

S 5

Carefree □ □ □ □ □ □ □ Responsible

Attiring Situation RESERV is a national level placement firm special- izing in putting retailers and service providers together with potential employees who fill positions at all levels

of the organization. This includes entry-level positions and senior management positions. One international specialty clothing store chain has approached them with issues involving key characteristics of retail employees. The two key characteristics of primary interest involve the appearance of potential employees and problems with customer integrity.

Over the last five years, store management has adopted a very relaxed dress code that has allowed employees some flexibility in the way they dress for work. Casual attire was permitted with the idea that younger customers could better identify with store employees, most of whom are younger than average. However, senior management had just become aware of how some very suc- cessful companies tightly control the appearance of their sales force. The Walt Disney Company, for example, has strict grooming poli- cies for all employees, provides uniforms (or costumes) for most cast members, and does not permit any employees to work if they have visible tattoos. Disney executives discuss many positive ben- efits from this policy and one is that customers are more respon- sive to the employees. Thus, it just may be that the appearance of employees can influence the behavior of customers. This influence can be from the greater identity that employees display—meaning they stand out better and may encourage acquiescence through friendliness.

Senior research associate Michael Neil decides to conduct an experiment to examine relevant research questions including:

RQ1: How does employee appearance affect customer pur- chasing behavior?

RQ2: How does employee appearance affect customer ethics?

Mr. Neil decides the problem can best be attacked by conduct- ing a laboratory experiment. In the experiment, two variables are manipulated in a between-subjects design. The experiment includes two experimental variables that are controlled by the researcher and by the subjects’ biological sex, which was recorded and included as a blocking variable. The experimental variables (and blocking variable) are:

Name Description Values

X 1

A manipulation of the attire of the service-providing employee

0 5 Professional Attire (Neatly groomed w/ business attire) 1 5 Unprofessional Attire (Unkempt hair w/jeans and t-shirt)

X 2

The manner with which the service-providing employee tries to gain extra sales—or simply, the close approach

0 5 Soft Close 1 5 Hard Close

Gender Subject’s biological sex 0 5 Male 1 5 Female

Three dependent variables are included:

Name Description Range

TIME How much time the subject spent with the employee beyond what was necessary to choose the slacks and the shirt

0–10 minutes

SPEND How much of the $25 the subject spent on extra products offered for sale by the retail service provider

$0–$25

KEEP How much of the $25 the subject kept rather than returning to the researcher

$0–$25

Questions 1. Does this grant present Dr. Ray with an ethical dilemma(s) in

any way? 2. Derive at least one hypothesis for each research question listed

above. Provide a sound rationale or theoretical explanation that leads to the hypothesis.

3. Use the data that corresponds to this case to perform an ade- quate test of each hypothesis. Interpret the results.

4. Is there evidence supporting the discrimination claim? Explain.

5. List another hypothesis (unrelated to the research questions in the grant) that could be tested with this data.

6. Test that hypothesis. 7. Considering employees’ attitudes about their college experi-

ence, does the amount of fun that students had in college or the degree to which they thought quantitative classes were a posi- tive experience relate more strongly to salary?

8. Would the “problem” that led to the grant be a better candidate for ethnographic research? Explain.

CASE 2

Comprehensive Cases 637

Case 1 (Continued )

(Continued )

Additionally, several variables were collected following the experi- ment that tried to capture how the subject felt during the exercise. All of these items were gathered using a 7-item semantic differential scale.

Name Description

SD 1

Low Quality–High Quality

SD 2

Dislike–Like

SD 3

Unfavorable–Favorable

SD 4

Negative–Positive

SD 5

Easy–Difficult

SD 6

Restful–Tiring

SD 7

Comfortable–Uncomfortable

SD 8

Calm–Tense

The experiment was conducted in a university union. Subjects were recruited from the food court area. RESERV employees approached potential subjects and requested their participation in a study that examined how customers really bought things. Subjects would each receive vouchers that could be exchanged for merchandise in return for their participation. Each potential subject was informed that the participation could take between 20 and 40 minutes to complete. Upon agreeing to participate, subjects were escorted to a waiting area where they were provided with further instructions and min- gled with other participants before entering a small room that was set up to resemble an actual retail clothing counter.

Each subject was told to play the role of a customer who had just purchased some dress slacks and a shirt. The employee was to complete the transaction. Once the subject entered the mock retail environment, a research assistant who was playing the role of the retail employee entered the room. As a retail sales associate, one important role was to suggest add-on sales. Several dozen accessory items ranging from socks and handkerchiefs to small jewelry items were displayed at the counter.

As a result of this experimental procedure, each subject was ran- domly assigned to one of four conditions, each corresponding to a unique combination of the experimental variables described above. In other words, the employee fit into one of the following categories:

1. Dressed professionally and used a soft close (i.e., “Perhaps you would like to see some additional accessories”) in trying to sell merchandise beyond the slacks and shirt.

2. Dressed unprofessionally and used a soft close. 3. Dressed professionally and used a hard close (i.e., “You really

need to match this up with some coordinated accessories which happen to be on sale today only”) in trying to sell merchandise beyond the slacks and shirt.

4. Dressed unprofessionally and used a hard close.

RESERV wishes to use this information to explain how employee appearance encourages shoppers to continue shopping (TIME) and spend money (SPEND). Rather than simply ask pur- chase intentions, researchers gave each subject $25 (in one-dollar bills), which they were allowed to spend on accessories. This allowed each subject to participate in an actual transaction. In addition, the experiment did not provide explicit instructions on what was to be done with the money that was left over. Once the simulated shop- ping trip was complete, subjects were taken to another small room where they answered a questionnaire containing the semantic dif- ferential scales and demographic information, which they completed while alone and at their own pace. Because the instructions did not specifically tell subjects what to do with the money they possessed following the experiment, this allowed the researchers to operation- alize a behavioral dependent variable (KEEP) that simulated ques- tionable behavior based on the implied assumption that the money was to be either handed to the research assistant when the shopping trip was complete or turned in along with the questionnaire. In other words, subjects who kept money were considered as behaving less ethically than those who left the money behind or turned it in to a member of the research team.

Questions 1. Develop at least three hypotheses that correspond to the research

questions. 2. Test the hypotheses using an appropriate statistical approach. 3. Suppose the researcher is curious about how the feel-

ings captured with the semantic differentials influence the dependent variables SPEND and KEEP. Conduct an analy- sis to explore this possibility. Are any problems present in testing this?

4. Is there a role for factor analysis in any of this analysis? 5. Critique the experiment from the viewpoint of internal and

external validity. 6. What conclusions would be justified by management regarding

their employee appearance policy?

Values and the Automobile Market (Download the data sets for this case from www.cengagebrain.com or request them from your instructor.) In the last decade, the luxury car

segment became one of the most competitive in the automobile market. Many American consumers who purchase luxury cars prefer imports from Germany and Japan.

A marketing vice president with General Motors once com- mented, “Import-committed buyers have been frustrating to us.” This type of thinking has led industry analysts to argue that to suc- cessfully compete in the luxury car segment, U.S. carmakers need to develop a better understanding of consumers so that they can

better target market segments and better position their products via more effective advertising. Insight into the foreign-domestic luxury car choice may result from examining owners’ personal values in addition to their evaluations of car attributes, because luxury cars, like many other conspicuously consumed luxury products, may be purchased mainly for value-expressive reasons.

Industry analysts believe it would be important to assess whether personal values of consumers could be used to explain ownership of American, German, and Japanese luxury cars. Further, they believe they should also assess whether knowledge of owners’ personal values provides any additional information useful in explaining ownership

CASE 3

638 Part 7: Comprehensive Cases with Computerized Databases

Case 2 (Continued )

(Continued )

of American, German, and Japanese luxury cars beyond that obtained from their evaluations of the cars’ attributes.

Personal values are likely to provide insights into reasons for ownership of luxury cars for at least two reasons. First, Americans have always had very personal relationships with their cars and have used them as symbols of their self-concepts. For instance, people who value a sense of accomplishment are quite likely to desire a luxury car that they feel is an appropriate symbol of their achievement, whereas people who value fun, enjoyment, and excitement are likely to desire a luxury car that they perceive as fun and exciting to drive. An advertiser trying to persuade the former segment to purchase a luxury car should position the car as a status symbol that will help its owners demonstrate their accomplishments to others. Similarly, an advertiser trying to persuade the latter segment to purchase a luxury car should position the car as a fun and exciting car to drive. In other words, effective advertis- ing shows consumers how purchasing a given product will help them achieve their valued state, because brands tied to values will be per- ceived more favorably than brands that deliver more mundane benefits.

Second, when a market is overcrowded with competing brands offering very similar options—as is the case with the luxury car market—consumers are quite likely to choose between brands on the basis of value-expressive considerations.

METHOD Data were collected via a mail survey sent to 498 consumers chosen at random from a list obtained from a syndicated research company located in an affluent county in a southern state. The list contained names of people who had purchased either an American luxury car (Cadillac or Lincoln), a German luxury car (Mercedes or BMW), or

a Japanese luxury car (Infiniti or Lexus) within the last year. A cover letter explained that the survey was part of an academic research project. People were asked to return the questionnaires anonymously to a university address. (A postage-paid envelope was provided with each survey.) A notice was included that stated that the project was approved by the University Internal Review Board and emphasized the fact that participation was voluntary. Beyond an appeal to help the researchers, respondents were not offered any other incentive to com- plete the surveys. Of the 498 questionnaires originally sent, 17 were returned by the post office as undeliverable. One hundred fifty-five completed surveys were received, for a response rate of 32.2 percent.

THE SURVEY INSTRUMENT The survey included questions on (1) various issues that people con- sider when purchasing new cars, (2) importance of car attributes, (3) importance of different values, and (4) demographics (sex, age, educa- tion, and family income). Questions relating to the issues that people consider when purchasing new cars were developed through initial interviews with consumers and were measured with a 7-point Likert scale with end anchors of “strongly agree” and “strongly disagree.” (See Case Exhibit 3.1.) A list of 12 car attributes was developed from the ini- tial interviews with consumers and by consulting Consumer Reports. (See Case Exhibit 3.2.) The importance of each attribute was measured with a 7-point numerical scale with end points labeled “very important” and “very unimportant.” The List of Values (LOV) scale in Case Exhibit 3.3 was used to measure the importance of values. Respondents were asked to rate each of the eight values—we combined fun, enjoyment, and excitement into one value—on a 7-point numerical scale with end points labeled “very important” and “very unimportant.”

CASE EXHIBIT 3.1

Issues That Consumers Consider When Buying Luxury Automobiles

Having a luxury car is a major part of my fun and excitement.a (Issue 1)

Owning a luxury car is a part of “being good to myself.” (Issue 2)

When I was able to buy my first luxury car, I felt a sense of accomplishment. (Issue 3)

I enjoy giving my friends advice about luxury cars. (Issue 4)

Getting a good deal when I buy a luxury car makes me feel better about myself. (Issue 5)

I seek novelty and I am willing to try innovations in cars. (Issue 6)

I tend to buy the same brand of the car several times in a row. (Issue 7)

I tend to buy from the same dealer several times in a row. (Issue 8)

I usually use sources of information such as Consumer Reports in deciding on a car. (Issue 9)

I usually visit three or more dealerships before I buy a car. (Issue 10)

I would read a brochure or watch a video about defensive driving. (Issue 11)

When I buy a new luxury car, my family’s opinion is very important to me. (Issue 12)

My family usually accompanies me when I am shopping for a new luxury car. (Issue 13)

I usually rely upon ads and salespersons for information on cars. (Issue 14)

I usually rely upon friends and acquaintances for information on cars. (Issue 15)

When I shop for a car, it is important that the car dealer make me feel at ease. (Issue 16)

Most of my friends drive luxury import cars. (Issue 17)

Most of my friends drive luxury domestic cars. (Issue 18)

I think celebrity endorsers in ads influence people’s choices of luxury cars. (Issue 19)

I would not buy a luxury car if I felt that my debt level were higher than usual. (Issue 20)

a Note: Subjects’ responses were measured with 1 as “strongly agree” and 7 as “strongly disagree.”

Comprehensive Cases 639

Case 3 (Continued )

(Continued )

CASE EXHIBIT 3.2

Car Attributes

Attribute Code Attribute Code

Comfort Comfort Low maintenance cost Lomc

Safety Safety Reliability Rely

Power Power Warranty Warrant

Speed Speed Nonpolluting Nonpol

Styling Styling High gas mileage Gasmle

Durability Durabil Speed of repairs Repairs

CASE EXHIBIT 3.3

List of Values

Value Code Value Code

Fun-enjoyment-excitement Fun Sense of accomplishment Accomp

Sense of belonging Belong Warm relationship Warm

Being well respected Respect Security Security

Self-fulfillment Selfful Self-respect Selfres

THE SAMPLE Of the 155 respondents in the sample, 58 (37.4 percent) owned an American luxury car, 38 (24.5 percent) owned a European luxury car, and 59 (38.1 percent) owned a Japanese luxury car. The majority of the sample consisted of older consumers (85 percent were 35 years of age or above), more educated

consumers (64 percent were college graduates), and economically well-off consumers (87.2 percent earned $65,000 or more).

CODING Case Exhibit 3.4 lists the SPSS variable names and identifies codes for these variables. (Note that this data set is also available in Microsoft Excel.)

CASE EXHIBIT 3.4

List of Variables and Computer Codes

ID—Identification number

Age (categories are 2 5 35 years and under, 3 5 36–45 yrs, 4 5 46–55 yrs, 5 5 56–65 yrs, 6 5 651 yrs)

Sex (1 5 male, 0 5 female)

Educ—Education (1 5 less than high school, 2 5 high school grad, 3 5 some college, 4 5 college grad, 5 5 graduate degree)

Income (1 5 less than $35,000, 2 5 $35–50,000, 3 5 $50,001–65,000, 4 5 $65,0011)

Car—Type of luxury car (American car, European car, Japanese car)

Issues—The sequence of issues listed in Case Exhibit 3.1. (Strongly agree 5 1; strongly disagree 5 7)

Attributes—The sequence of car attributes listed in Case Exhibit 3.2. (Very important to you 5 1; very unimportant to you 5 7)

Values—The sequence of values listed in Case Exhibit 3.3. (Very important 5 1; very unimportant 5 7)

ADDITIONAL INFORMATION Several of the questions will require the use of a computerized data- base. Your instructor will provide information about obtaining the VALUES data set if the material is part of the case assignment.

Questions 1. Is the sampling method adequate? Is the attitude-measuring scale

sound? Explain. 2. Using the computerized database with a statistical software pack-

age, calculate the means of the three automotive groups for the

values variables. Do any of the values variables show significant differences between American, Japanese, and European car owners?

3. Are there any significant differences on importance of attributes? 4. Write a short statement interpreting the results of this research.

Advanced Questions 5. Are any of the value scale items highly correlated? 6. Should multivariate analysis be used to understand the data?

Case materials based on research by Ajay Sukhdial and Goutam Chakraborty, Oklahoma State University.

640 Part 7: Comprehensive Cases with Computerized Databases

Case 3 (Continued )

Comprehensive Cases 641

TABH, INC., Automotive Consulting (Download the data sets for this case from www.cengagebrain.com or request them from your instructor.) TABH Consulting specializes

in research for automobile dealers in the United States, Canada, Mexico, and Europe. Although much of their work is done on a custom basis with customers such as dealerships and dealership net- works selling all major makes of automobiles, they also produce a monthly “white paper” that is sold via their website. This off-the- shelf research is purchased by other research firms and by compa- nies within the auto industry itself. This month, they would like to produce a white paper analyzing the viability of college students attending schools located in small college towns as a potentially underserved market segment.

TABH management assigns a junior analyst named Michelle Gonzalez to the project. Lacking time for a more comprehensive

study, Michelle decides to contact the traffic department at Cal Poly University in Pomona, California, and at University of Central Mis- souri in Warrensburg, Missouri. Michelle wishes to obtain data from the students’ automobile parking registration records. Although both schools are willing to provide anonymous data records for a limited number of students, Cal Poly offers Michelle a chance to visit during the registration period, which just happens to be the following week. As a result, not only can Michelle get data from students’ registration forms, but she can obtain a small amount of primary data by inter- cepting students near the registration window. In return, Michelle is asked to purchase a booth at the Cal Poly career fair.

As a result, Michelle obtains some basic information from stu- dents. The information results in a small data set consisting of the following observations for 100 undergraduate college students in Pomona, California:

CASE 4

Variable Description

Sex Student’s sex dummy coded with 1 5 female and 0 5 male

Color Color of a student’s car as listed on the registration form

Major Student’s major field of study (Business, Liberal Arts (LA), or Engineering (ENG))

Grade Student’s grade record reported as the mode (A, B, or C)

Finance Whether the student financed the registered car or paid for it with cash, coded 0 5 cash payment and 1 5 financed

Residence Whether the student lives on campus or commutes to school, coded 0 5 commute and 1 5 on campus

Animal Michelle asks each student to quickly draw a cartoon about the type of car they would like to purchase. Students are told to depict the car as an animal in the cartoon. Although Michelle expects to interpret these cartoons more deeply when time allows, the initial coding specifies what type of animal was drawn by each respondent. When Michelle was unsure of what animal was drawn, a second researcher was conferred with to determine what animal was depicted. Some students depicted the car as a dog, some as a cat, and some as a mule.

The purpose of the white paper is to offer car dealers considering new locations a comparison of the profile of a small town university with the primary market segments for their particular automobile. For instance, a company specializing in small pickup trucks appeals to a different market segment than does a company specializing in two-door economy sedans. Many small towns currently do not have dealerships, particularly beyond the “Big 3.” Although TABH cannot predict with certainty who may purchase the white paper, it particularly wants to appeal to companies with high sales growth in the United States, such as Kia (http://www.kia.com), Hyundai (http://www.hyundaiusa.com), and potentially European auto dealerships currently without significant U.S. distribution, such as Smart (http://www.smart.com), among others. TABH also hopes the white paper may eventually lead to a customized project for one of these companies. Thus, the general research question is:

What are the automobile market segment characteristics of students attending U.S. universities in small towns?

This question can be broken down into a series of more specific questions:

• What segments can be identified based on identifiable character- istics of students?

• How do different segments view a car? • What types of automobiles would be most in demand?

Questions 1. What types of tests can be performed using the data that may at

least indirectly address the primary research question? 2. What do you think the primary conclusions of the white paper

will be based on the data provided? 3. Assuming a small college town lacked an auto dealership

(beyond Ford, GM, and Chrysler), what two companies should be most interested in this type of location? Use the Internet if necessary to perform some cursory research on different car companies.

4. What are the weaknesses in basing decisions on this type of research?

5. Are there key issues that may diminish the usefulness of this research?

6. What kinds of themes might emerge from the cartoon drawings? 7. Are there any ethical dilemmas presented in this case?

642 Part 7: Comprehensive Cases with Computerized Databases

Knowing the Way (Download the data sets for this case from www.cengagebrain.com or request them from your instructor.) The Swamp Palace Museum is

an interactive museum that teaches visitors the ways of life on the swamps of the southern United States. Visitors can visit over one hundred exhibits demonstrating the ecology of the swamp and the habits of the animals and insects that call the swamp home. Additionally, the museum includes several fast-food and full ser- vice restaurants, the opportunity for swimming, and several thrill rides. The park covers over 40 acres and includes miles and miles of pathways.

The park was originally supported with one-time government funding but now it has to become self-supporting. After five years of operation, the park has not lacked for visitors but has struggled

just to break even. The Swamp Palace has sought help from the Marketivity Group to help them address the long-term viability of the park. Initially, the Swamp Palace conducts exploratory research employing a participant-observer technique in which trained inter- viewers pretend to be park guests and engage in dialog with museum patrons. After employing interpretive techniques to the data gathered in these interviews, the Marketivity Group reports the exploratory results to management. A report emphasizes these key findings: 1. Patrons who complain tend to base their complaints over defi-

ciencies in quality. Happy patrons voice nothing indicating a low-quality theme.

2. Patrons also express a theme around value. Unhappy patrons believe the value offered by the park is low based in part on what is perceived as a high admission price.

CASE 6

The Atlanta Braves A visit to Turner Field, the Atlanta Braves’ state-of- the-art ballpark, feels like a trip back to the future. The stadium blends 1940s tradition with twenty-first

century convenience. The Braves’ marketing campaign reflects the charm and nostalgia of baseball’s past, but it has a futuristic slogan: “Turner Field: Not just baseball. A baseball theme park.”

Fans love the fact that they’re closer to the action at Turner Field. It’s only 45 feet from either first or third base to the dugouts, with the stands just behind. Besides that, there’s a Braves Museum and Hall of Fame with more than 200 artifacts. Cybernauts will find Turner Field awesome because it’s a ballpark that makes them a part of the action. At the stadium, built originally for the 1996 Olympics and converted for baseball after the Games, there are interactive games to test fans’ hitting and pitching skills, and their knowledge of baseball trivia; electronic kiosks with touch screens and data banks filled with scout- ing reports on 300 past and present Braves, along with the Braves’ Internet home page; a dozen 27-inch television monitors mounted above the Braves’ Clubhouse Store, broadcasting all the other major league games in progress, with a video ticker-tape screen underneath spitting out up-to-the-minute scores and stats; a sophisticated com- munications system, with four miles of fiber-optic cable underneath the playing field that will allow World Series games to be simulcast around the globe, as well as special black boxes placed throughout the stadium to allow as many as 5,500 cell-phone calls an hour.

The marketing of Turner Field is aimed at many types of fans. It is not enough just to provide nine innings of baseball.

Turner Field’s theme-park concept was the brainchild of Braves President Stan Kasten. In the early 1990s, as the Braves grew into one of the best teams in baseball, Kasten increasingly became frus- trated while watching fans flock to Atlanta–Fulton County Stadium a few hours before games, with little to do but eat overcooked hot dogs and watch batting practice.

As Kasten saw it, they spent too much time milling on the club- level concourse and too little time spending money. What if he could find a way for families to make an outing of it, bring the amenities of the city to Hank Aaron Drive, and create a neighborhood feel in a main plaza at the ballpark? “I wanted to broaden fans’ experience at the ballpark and broaden our fan base,” Kasten says. “People have no

problem spending money when they’re getting value. We have one of the highest payrolls in baseball, and I needed to find new ways to sustain our revenues.”

Turner Field’s main entry plaza opens three hours before games—compared to two hours for the rest of the ballpark—and stays open for about two hours after games. On weekends, there is live music.

Everyone’s invited—186 $1 “skyline seats” are available for each game—and that buck gets you anywhere, from the open-air porch at the Chop House restaurant (which specializes in barbecue, bison dogs, Moon Pies, and Tomahawk lager) to the grassy roof at Coke’s Sky Field, where fans can keep cool under a mist machine.

Interactive games in Scouts Alley range from $1 to $4, and the chroma-key studios in the East and West Pavilions cost $10–20, where fans can have their picture inserted into a baseball card or into a photo of a great moment in Braves history. Admission to the museum is $2. And it should come as no surprise that there are seven ATMs located throughout the ballpark.

One of the Braves’ key marketing objectives is to help build a new generation of baseball fans. The stadium was planned so that fans will find something to love and learn at every turn. The minute a fan’s ticket is torn, that person becomes part of what’s happening at Turner Field.

Questions 1. What are the key elements of the Turner Field marketing effort? 2. What aspect of the planning of Turner Field, home of the

Atlanta Braves, may have been influenced by research using secondary data?

3. What role should business research play in a sporting organiza- tion such as the Atlanta Braves, both in making capital decisions and in supporting everyday operational maters?

4. Suppose an executive for the Braves wishes to know whether the stadium has caused employees (including ticket takers, park- ing attendants, ushers, security personnel, team employees, etc.) to be more committed to the Braves organization than when they were playing in an old-fashioned stadium. What would a potential research design involve and what data collection and statistical tests, if any, could be useful?

CASE 5

(Continued )

3. Patrons express the difficulty in getting around in the park as a key theme. Even happy patrons joke about how difficult it is to find their way around.

4. As groups get larger, at least one member of the group was unhappy about having to accompany the others to the park.

After further subsequent discussions, Marketivity is hired to undertake a further study aimed at helping in addressing these deci- sion statements:

• In what ways can Swamp Palace use technology to improve a customer’s ability to effectively navigate around the museum?

• In what ways can Swamp Palace increase return visits by customers? • Is participation in online coupon programs an effective way of

increasing patronage and value?

Several technologies are considered as ways of enhancing value. One is a mobile phone app that will provide oral and visual navigation aids around the park. For instance, if someone says “take me to the Blind Bayou Bar,” the phone will give directions using prominent museum landmarks. Second, Swamp Palace is considering subscrib- ing to an Internet coupon program that would provide patrons with discounts. Marketivity translates these statements into several research questions including the following:

1. Do patrons who use a mobile phone navigation app report higher service quality and have an improved experience relative to those who do not?

2. Do patrons who use the mobile phone app have a greater likeli- hood of upgrading to a season pass?

3. Do patrons who use a coupon report more positive price perceptions?

4. Do patrons who use the mobile phone app have a greater likeli- hood of upgrading to a season pass?

5. What factors contribute to improved value perceptions?

Marketivity implements a quasiexperimental design over a one week period in August. A sample of 200 visitors are randomly intercepted before entering the park. Approximately half are given the opportunity to download a free navigation app for their cell phone. Similarly, about half are invited to go to a kiosk and down- load a coupon from the Internet. The park provides Marketivity with employees to intercept the patrons and explain the research procedures. Upon exiting the park, the patrons are taken to a desk where they fill out a short questionnaire. The employee then keys the data into the computer. The variables in the data set are described in the table below:

Name Description* Values

Wayf A variable indicating whether the patron was provided the mobile phone app on entering the park Yes or No

Groupon A variable indicating whether the patron used a Groupon discount to enter the park 1 5 Yes 2 5 No

SQ1 Employees at SPM offer high quality service. 5-point Likert (SD to SA)

SQ2 The attractions at SPM are high in quality. 5-point Likert (SD to SA)

SQ3 The food quality at SPM is very good. 5-point Likert (SD to SA)

SQ4 The service at SPM is excellent overall. 5-point Likert (SD to SA)

SQ5 The quality of SPM is very good. 5-point Likert (SD to SA)

VAL1 The time I spent at SPM was truly a joy. 5-point Likert (SD to SA)

VAL2 I enjoyed being engaged in exciting activities during my visit to SPM. 5-point Likert (SD to SA)

VAL3 While at SPM, I was able to forget my problems. 5-point Likert (SD to SA)

VAL4 I think SPM offers guests a lot of value. 5-point Likert (SD to SA)

PriceP The admission price is very fair. 5-point Likert (SD to SA)

Use the terms below to describe your feelings about your overall experience at the museum:

FEEL1 Favorable ------- Unfavorable 7-point semantic differential

FEEL2 Exciting ------- Boring 7-point semantic differential

FEEL3 Happy ------- Sad 7-point semantic differential

FEEL4 Delighted ------- Terrible 7-point semantic differential

UPGRADE Whether respondent agreed to upgrade their ticket to a season pass 1 5 No 2 5 Yes 3 5 Undecided

Gender Sex of respondent 1 5 Female 2 5 Male

Age Age group 1 5 less than 18 2 5 19–24 3 5 25–35 4 5 36–45 5 5 46 or more

Others How many others were with the patron 0 5 None 1 5 1 2 5 2 3 5 3 4 5 more than 3

*Notes: Missing values in the data set are indicated by either an empty cell (sometimes with a . in the cell) or by the numeral 9. SPM stands for Swamp Place Museum, SD 5 Strongly Disagree (1), and SA 5 Strongly Agree (5).

Comprehensive Cases 643

Case 6 (Continued )

(Continued )

Questions 1. Run frequencies on Gender, Others, and Age. Are any problems

evident with coding? Take any necessary corrective actions. 2. Compute a composite scale for the 5 SQ items and the 4 VAL

items. Compute a coefficient alpha for each of the resulting ser- vice quality and value scales.

3. Perform an appropriate test of each research question RQ1, RQ2, RQ3, RQ4, and RQ5.

4. List an additional research question that can be addressed with a one-way ANOVA. Conduct the test.

5. List an additional research question that can be addressed with a GLM model. Conduct the test.

6. Summarize the implications for the decision statements that arise from the tests above. Make sure you cover whether the park should invest in the navigation system and coupon technologies.

644 Part 7: Comprehensive Cases with Computerized Databases

Case 6 (Continued )

645

GLOSSARY OF FREQUENTLY USED SYMBOLS

Greek Letters

 (alpha) level of significance or probability of a Type I error

b (beta) probability of a Type II error or slope of the regression line

m (mu) population mean

 (rho) population Pearson correlation coefficient

 (sigma) take the sum of

p (pi) population proportion

 (sigma) population standard deviation

x2 chi-square statistic

English Letters

df number of degrees of freedom

F F-statistic

n sample size

p sample proportion

Pr( ) probability of the outcome in the parentheses

r sample Pearson correlation coefficient

r 2 coefficient of determination (squared correlation coefficient)

R2 coefficient of determination (multiple regression)

S sample standard deviation (inferential statistics)

S x estimated standard error of the mean

S p estimated standard error of the proportion

S2 sample variance (inferential statistics)

t t-statistic

X variable or any unspecified observation

X sample mean

Y any unspecified observation on a second variable, usually the dependent variable

Ŷ predicted dependent variable score

Z standardized score (descriptive statistics) or Z-statistic

646

GLOSSARY

A Absolute causality Means the cause is necessary and sufficient to

bring about the effect. Abstract level In theory development, the level of knowledge

expressing a concept that exists only as an idea or a quality apart from an object.

Acquiescence bias A tendency for respondents to agree with all or most questions asked of them in a survey.

Administrative error An error caused by the improper administration or execution of the research task.

Advocacy research Research undertaken to support a specific claim in a legal action or represent some advocacy group.

Analysis of variance (ANOVA) Analysis involving the investigation of the effects of one treatment variable on an interval-scaled dependent variable—a hypothesis-testing technique to determine whether statistically significant differences in means occur between two or more groups.

Applied business research Research conducted to address a specific business decision for a specific firm or organization.

Attitude An enduring disposition to consistently respond in a given manner to various aspects of the world, composed of affective, cognitive, and behavioral components.

Attribute A single characteristic or fundamental feature of an object, person, situation, or issue.

B Back translation Taking a questionnaire that has previously

been translated into another language and having a second, independent translator translate it back to the original language.

Backward linkage Implies that later steps influence earlier stages of the research process.

Balanced rating scale A fixed-alternative rating scale with an equal number of positive and negative categories; a neutral point or point of indifference is at the center of the scale.

Basic business research Research conducted without a specific decision in mind that usually does not address the needs of

a specific organization. It attempts to expand the limits of knowledge in general and is not aimed at solving a particular pragmatic problem.

Basic experimental design An experimental design in which only one variable is manipulated.

Behavioral differential A rating scale instrument similar to a semantic differential, developed to measure the behavioral intentions of subjects toward future actions.

Between-groups variance The sum of differences between the group mean and the grand mean summed over all groups for a given set of observations.

Between-subjects design Each subject receives only one treatment combination.

Bivariate statistical analysis Tests of hypotheses involving two variables.

Blocking variables A categorical (less than interval) variable that is not manipulated like an experimental variable but is included in the statistical analysis of experiments.

Box and whisker plots Graphic representations of central tendencies, percentiles, variabilities, and the shapes of frequency distributions.

Briefing session A training session to ensure that each interviewer is provided with common information.

Business ethics The application of morals to behavior related to the exchange environment.

Business intelligence The subset of data and information that actually has some explanatory power enabling effective decisions to be made.

Business opportunity A situation that makes some potential competitive advantage possible.

Business problem A situation that makes some significant negative consequence more likely.

Business research The application of the scientific method in searching for the truth about business phenomena. These activities include defining business opportunities and problems, generating and evaluating ideas, monitoring performance, and understanding the business process.

C Callbacks Attempts to recontact individuals selected for a sample

who were not available initially. Case studies The documented history of a particular person,

group, organization, or event. Categorical variable A variable that indicates membership in

some group. Category scale A rating scale that consists of several response

categories, often providing respondents with alternatives to indicate positions on a continuum.

Causal inference A conclusion that when one thing happens, another specific thing will follow.

Causal research Allows causal inferences to be made; seeks to identify cause-and-effect relationships.

Cell Refers to a specific treatment combination associated with an experimental group.

Census An investigation of all the individual elements that make up a population.

Central location interviewing Telephone interviews conducted from a central location allowing firms to hire a staff of professional interviewers and to supervise and control the quality of interviewing more effectively.

Central-limit theorem The theory that, as sample size increases, the distribution of sample means of size n, randomly selected, approaches a normal distribution.

Checkboxes In an Internet questionnaire, small graphic boxes, next to answers, that a respondent clicks on to choose an answer; typically, a check mark or an X appears in the box when the respondent clicks on it.

Checklist question A fixed-alternative question that allows the respondent to provide multiple answers to a single question by checking off items.

Chi-square (2) test One of the basic tests for statistical significance that is particularly appropriate for testing hypotheses about frequencies arranged in a frequency or contingency table.

Choice A measurement task that identifies preferences by requiring respondents to choose between two or more alternatives.

Classificatory variable Another term for a categorical variable because it classifies units into categories.

Click-through rate Proportion of people who are exposed to an Internet ad who actually click on its hyperlink to enter the website; click-through rates are generally very low.

Cluster analysis A multivariate approach for grouping observations based on similarity among measured variables.

Cluster sampling An economically efficient sampling technique in which the primary sampling unit is not the individual element in the population but a large cluster of elements; clusters are selected randomly.

Code book A book that identifies each variable in a study and gives the variable’s description, code name, and position in the data matrix.

Codes Rules for interpreting, classifying, and recording data in the coding process; also, the actual numerical or other character symbols assigned to raw data.

Coding The process of assigning a numerical score or other character symbol to previously edited data.

Coefficient alpha () The most commonly applied estimate of a multiple-item scale’s reliability. It represents the average of all possible split-half reliabilities for a construct.

Coefficient of determination (R2) A measure obtained by squaring the correlation coefficient; the proportion of the total variance of a variable accounted for by another value of another variable.

Cohort effect Refers to a change in the dependent variable that occurs because members of one experimental group experienced different historical situations than members of other experimental groups.

Communication process The process by which one person or source sends a message to an audience or receiver and then receives feedback about the message.

Comparative rating scale Any measure of attitudes that asks respondents to rate a concept in comparison with a benchmark explicitly used as a frame of reference.

Completely randomized design An experimental design that uses a random process to assign subjects to treatment levels of an experimental variable.

Composite measures Assign a value to an observation based on a mathematical derivation of multiple variables.

Composite scale A way of representing a latent construct by summing or averaging respondents’ reactions to multiple items each assumed to indicate the latent construct.

Computer-assisted telephone interviewing (CATI) Technology that allows answers to telephone interviews to be entered directly into a computer for processing.

Concept (or construct) A generalized idea about a class of objects that has been given a name; an abstraction of reality that is the basic unit for theory development.

Concept A generalized idea that represents something of meaning. Conclusions and recommendations section The part of the

body of a report that provides opinions based on the results and suggestions for action.

Concomitant variation One of three criteria for causality; occurs when two events “covary,” meaning they vary systematically.

Conditional causality Means that a cause is necessary but not sufficient to bring about an effect.

Confidence interval estimate A specified range of numbers within which a population mean is expected to lie; an estimate of the population mean based on the knowledge that it will be equal to the sample mean plus or minus a small sampling error.

Confidence level A percentage or decimal value that tells how confident a researcher can be about being correct; it states the long-run percentage of confidence intervals that will include the true population mean.

Confidentiality The information involved in a research study will not be shared with others.

Conflict of interest Occurs when one researcher works for two competing companies.

Confound A confound means that there is an alternative explanation beyond the experimental variables for any observed differences in the dependent variable.

Constancy of conditions Means that subjects in all experimental groups are exposed to identical conditions except for the differing experimental treatments.

Constant Something that does not change; is not useful in addressing research questions.

Constant-sum scale A measure of attitudes in which respondents are asked to divide a constant sum to indicate the relative importance of attributes; respondents often sort cards, but the task may also be a rating task.

Glossary 647

Construct A term used to refer to concepts measured with multiple variables.

Construct validity Exists when a measure reliably measures and truthfully represents a unique concept; consists of several components including face validity, content validity, criterion validity, convergent validity, and discriminant validity.

Consumer panel A longitudinal survey of the same sample of individuals or households to record their attitudes, behavior, or purchasing habits over time.

Content analysis The systematic observation and quantitative description of the manifest content of communication.

Content providers Parties that furnish information on the World Wide Web.

Content validity The degree that a measure covers the breadth of the domain of interest.

Contingency table A data matrix that displays the frequency of some combination of possible responses to multiple variables; cross-tabulation results.

Continuous measures Measures that reflect the intensity of a concept by assigning values that can take on any value along some scale range.

Continuous variable A variable that can take on a range of values that correspond to some quantitative amount.

Contributory causality Means that a cause need be neither necessary nor sufficient to bring about an effect.

Contrived observation Observation in which the investigator creates an artificial environment in order to test a hypothesis.

Control group A group of subjects to whom no experimental treatment is administered.

Convenience sampling The sampling procedure of obtaining those people or units that are most conveniently available.

Convergent validity Concepts that should be related to one another are in fact related; highly reliable scales contain convergent validity.

Conversations An informal qualitative data-gathering approach in which the researcher engages a respondent in a discussion of the relevant subject matter.

Cookies Small computer files that a content provider can save onto the computer of someone who visits its website.

Correlation coefficient A statistical measure of the covariation, or association, between two at-least interval variables.

Correlation matrix The standard form for reporting correlation coefficients for more than two variables.

Correspondence rules Indicate the way that a certain value on a scale corresponds to some true value of a concept.

Counterbalancing Attempts to eliminate the confounding effects of order of presentation by requiring that one-fourth of the subjects be exposed to treatment A first, one-fourth to treatment B first, one-fourth to treatment C first, and finally one-fourth to treatment D first.

Counterbiasing statement An introductory statement or preamble to a potentially embarrassing question that reduces a respondent’s reluctance to answer by suggesting that certain behavior is not unusual.

Covariance The extent to which two variables are associated systematically with each other.

Cover letter Letter that accompanies a questionnaire to induce the reader to complete and return the questionnaire.

Criterion validity The ability of a measure to correlate with other standard measures of similar constructs or established criteria.

Critical values The values that lie exactly on the boundary of the region of rejection.

Cross-checks The comparison of data from one source with data from another source to determine the similarity of independent projects.

Cross-functional teams Employee teams composed of individuals from various functional areas such as engineering, production, finance, and marketing who share a common purpose.

Cross-sectional study A study in which various segments of a population are sampled and data are collected at a single moment in time.

Cross-tabulation The appropriate technique for addressing research questions involving relationships among multiple less- than interval variables; results in a combined frequency table displaying one variable in rows and another in columns.

Cross-validate To verify that the empirical findings from one culture also exist and behave similarly in another culture.

Curbstoning A form of interviewer cheating in which an interviewer makes up the responses instead of conducting an actual interview.

Custom research Research projects that are tailored specifically to a client’s unique needs.

Customer discovery Involves mining data to look for patterns identifying who is likely to be a valuable customer.

Customer relationship management (CRM) The part of the DSS that addresses exchanges between the firm and its customers.

D Data analysis The application of reasoning to understand the data

that have been gathered. Data conversion The process of changing the original form of the

data to a format suitable to achieve the research objective; also called data transformation.

Data entry The activity of transferring data from a research project to computers.

Data Facts or recorded measures of certain phenomena (things). Data file The way a data set is stored electronically in spreadsheet-

like form in which the rows represent sampling units and the columns represent variables.

Data integrity The notion that the data file actually contains the information that the researcher promised the decision maker he or she would obtain, meaning in part that the data have been edited and properly coded so that they are useful to the decision maker.

Data mining The use of powerful computers to dig through volumes of data to discover patterns about an organization’s customers and products; applies to many different forms of analysis.

Data quality The degree to which data represent the true situation. Data reduction technique Multivariate statistical approaches that

summarize the information from many variables into a reduced set of variates formed as linear combinations of measured variables.

Data transformation Process of changing the data from their original form to a format suitable for performing a data analysis addressing research objectives.

Data warehouse The multitiered computer storehouse of current and historical data.

Data warehousing The process allowing important day-to-day operational data to be stored and organized for simplified access.

648 Glossary

Data wholesalers Companies that put together consortia of data sources into packages that are offered to municipal, corporate, and university libraries for a fee.

Data-processing error A category of administrative error that occurs because of incorrect data entry, incorrect computer programming, or other procedural errors during data analysis.

Database A collection of raw data arranged logically and organized in a form that can be stored and processed by a computer.

Database marketing The use of customer databases to promote one-to-one relationships with customers and create precisely targeted promotions.

Debriefing Research subjects are fully informed and provided with a chance to ask any questions they may have about the experiment.

Decision making The process of developing and deciding among alternative ways of resolving a problem or choosing from among alternative opportunities.

Decision statement A written expression of the key question(s) that the research user wishes to answer.

Decision support system (DSS) A computer-based system that helps decision makers confront problems through direct interaction with databases and analytical software programs.

Deductive reasoning The logical process of deriving a conclusion about a specific instance based on a known general premise or something known to be true.

Degrees of freedom (df ) The number of observations minus the number of constraints or assumptions needed to calculate a statistical term.

Deliverables The term used often in consulting to describe research objectives to a research client.

Demand characteristic Experimental design element or procedure that unintentionally provides subjects with hints about the research hypothesis.

Demand effect Occurs when demand characteristics actually affect the dependent variable.

Dependence techniques Multivariate statistical techniques that explain or predict one or more dependent variables.

Dependent variable A process outcome or a variable that is predicted and/or explained by other variables.

Depth interview A one-on-one interview between a professional researcher and a research respondent conducted about some relevant business or social topic.

Descriptive analysis The elementary transformation of raw data in a way that describes the basic characteristics such as central tendency, distribution, and variability.

Descriptive research Describes characteristics of objects, people, groups, organizations, or environments; tries to “paint a picture” of a given situation.

Descriptive statistics Statistics that summarize and describe the data in a simple and understandable manner.

Determinant-choice question A fixed-alternative question that requires the respondent to choose one response from among multiple alternatives.

Diagnostic analysis Seeks to diagnose reasons for business outcomes and focuses specifically on the beliefs and feelings consumers have about and toward competing products.

Dialog boxes Windows that open on a computer screen to prompt the user to enter information.

Direct observation A straightforward attempt to observe and record what naturally occurs; the investigator does not create an artificial situation.

Discrete measures Measures that take on only one of a finite number of values.

Discriminant analysis A statistical technique for predicting the probability that an object will belong in one of two or more mutually exclusive categories of the dependent variable, based on several independent variables.

Discriminant validity Represents the uniqueness or distinctiveness of a measure; a scale should not correlate too highly with a measure of a different construct.

Discussion guide A focus group outline that includes written introductory comments informing the group about the focus group purpose and rules and then outlines topics or questions to be addressed in the group session.

Disguised questions Indirect questions that assume the purpose of the study must be hidden from the respondent.

Disproportional stratified sample A stratified sample in which the sample size for each stratum is allocated according to analytical considerations.

Do-not-call legislation Restricts any telemarketing effort from calling consumers who either register with a no-call list or who request not to be called.

Door-in-the-face compliance technique A two-step process for securing a high response rate. In step 1 an initial request, so large that nearly everyone refuses it, is made. Next, a second request is made for a smaller favor; respondents are expected to comply with this more reasonable request.

Door-to-door interviews Personal interviews conducted at respondents’ doorsteps in an effort to increase the participation rate in the survey.

Double-barreled question A question that may induce bias because it covers two issues at once.

Drop-down box In an Internet questionnaire, a space-saving device that reveals responses when they are needed but otherwise hides them from view.

Drop-off method A survey method that requires the interviewer to travel to the respondent’s location to drop off questionnaires that will be picked up later.

Dummy coding Numeric “0” or “1” coding where each number represents an alternate response such as “female” or “male.”

Dummy tables Tables placed in research proposals that are exact representations of the actual tables that will show results in the final report with the exception that the results are hypothetical (fictitious).

Dummy variable The way a dichotomous (two group) independent variable is represented in regression analysis by assigning a 0 to one group and a 1 to the other.

E E-mail surveys Surveys distributed through electronic mail. Editing The process of checking the completeness, consistency,

and legibility of data and making the data ready for coding and transfer to storage.

Elaboration analysis An analysis of the basic cross-tabulation for each level of a variable not previously considered, such as subgroups of the sample.

Electronic data interchange (EDI) Type of exchange that occurs when one company’s computer system is integrated with another company’s system.

Empirical level Level of knowledge that is verifiable by experience or observation.

Glossary 649

Empirical testing Examining a research hypothesis against reality using data.

Environmental scanning Entails all information gathering designed to detect changes in the external operating environment of the firm.

Error trapping Using software to control the flow of an Internet questionnaire—for example, to prevent respondents from backing up or failing to answer a question.

Ethical dilemma Refers to a situation in which one chooses from alternative courses of actions, each with different ethical implications.

Ethnography Represents ways of studying cultures through methods that involve becoming highly active within that culture.

Evaluation research The formal, objective measurement and appraisal of the extent a given activity, project, or program has achieved its objectives or whether continuing programs are presently performing as projected.

Experiment A carefully controlled study in which the researcher manipulates a proposed cause and observes any corresponding change in the proposed effect.

Experimental condition One of the possible levels of an experimental variable manipulation.

Experimental group A group of subjects to whom an experimental treatment is administered.

Experimental treatment The term referring to the way an experimental variable is manipulated.

Experimental variable Represents the proposed cause and is controlled by the researcher by manipulating it.

Exploratory research Conducted to clarify ambiguous situations or discover ideas that may be potential business opportunities.

External data Data created, recorded, or generated by an entity other than the researcher’s organization.

External validity The accuracy with which experimental results can be generalized beyond the experimental subjects.

Extraneous variables Variables that naturally exist in the environment that may have some systematic effect on the dependent variable.

Extremity bias A category of response bias that results because some individuals tend to use extremes when responding to questions.

Eye-tracking monitor A mechanical device used to observe eye movements; some eye monitors use infrared light beams to measure unconscious eye movements.

F F-test A procedure used to determine whether there is more

variability in the scores of one sample than in the scores of another sample.

F-test (regression) A procedure to determine whether more variability is explained by the regression or unexplained by the regression.

Face validity A scale’s content logically appears to reflect what was intended to be measured.

Factor analysis A prototypical multivariate, interdependence technique that statistically identifies a reduced number of factors from a larger number of measured variables.

Factor loading Indicates how strongly a measured variable is correlated with a factor.

Factor rotation A mathematical way of simplifying factor analysis results so as to better identify which variables “load on” which factors; the most common procedure is varimax.

Factorial design A design that allows for the testing of the effects of two or more treatments (experimental variables) at various levels.

Fax survey A survey that uses fax machines as a way for respondents to receive and return questionnaires.

Field A collection of characters that represents a single type of data—usually a variable.

Field editing Preliminary editing by a field supervisor on the same day as the interview to catch technical omissions, check legibility of handwriting, and clarify responses that are logically or conceptually inconsistent.

Field experiments Research projects involving experimental manipulations that are implemented in a natural environment.

Field interviewing service A research supplier that specializes in gathering data.

Field notes The researcher’s descriptions of what actually happens in the field; these notes then become the text from which meaning is extracted.

Fieldworker An individual who is responsible for gathering data in the field.

Filter question A question that screens out respondents who are not qualified to answer a second question.

Fixed-alternative questions Questions in which respondents are given specific, limited-alternative responses and asked to choose the one closest to their own viewpoint.

Focus blog A type of informal, “continuous” focus group established as an Internet blog for the purpose of collecting qualitative data from participant comments.

Focus group A small group discussion about some research topic led by a moderator who guides discussion among the participants.

Focus group interview An unstructured, free-flowing interview with a small group of around six to ten people. Focus groups are led by a trained moderator who follows a flexible format encouraging dialogue among respondents.

Foot-in-the-door compliance technique A technique for obtaining a high response rate, in which compliance with a large or difficult task is induced by first obtaining the respondent’s compliance with a smaller request.

Forced answering software Software that prevents respondents from continuing with an Internet questionnaire if they fail to answer a question.

Forced-choice rating scale A fixed-alternative rating scale that requires respondents to choose one of the fixed alternatives.

Forecast analyst Employee who provides technical assistance such as running computer programs and manipulating data to generate a sales forecast.

Forward linkage Implies that the earlier stages of the research process influence the later stages.

Free-association techniques Record respondents’ first (top-of- mind) cognitive reactions to some stimulus.

Frequency distribution A set of data organized by summarizing the number of times a particular value of a variable occurs.

Frequency table A table showing the different ways respondents answered a question.

Frequency-determination question A fixed-alternative question that asks for an answer about general frequency of occurrence.

Frugging Fund-raising under the guise of research.

650 Glossary

Funded business research Refers to basic research usually performed by academic researchers that is financially supported by some public or private institution, as in federal government grants.

Funnel technique Asking general questions before specific questions in order to obtain unbiased responses.

G General linear model (GLM) A way of explaining and predicting

a dependent variable based on fluctuations (variation) from its mean. The fluctuations are due to changes in independent variables.

Global information system An organized collection of computer hardware, software, data, and personnel designed to capture, store, update, manipulate, analyze, and immediately display information about worldwide business activity.

Goodness-of-fit (GOF) A general term representing how well some computed table or matrix of values matches some population or predetermined table or matrix of the same size.

Grand mean The mean of a variable over all observations. Graphic aids Pictures or diagrams used to clarify complex points

or emphasize a message. Graphic rating scale A measure of attitudes that allows

respondents to rate an object by choosing any point along a graphic continuum.

Grounded theory Represents an inductive investigation in which the researcher poses questions about information provided by respondents or taken from historical records; the researcher asks the questions to him or herself and repeatedly questions the responses to derive deeper explanations.

H Hawthorne effect People will perform differently from normal

when they know they are experimental subjects. Hermeneutic unit Refers to a text passage from a respondent’s

story that is linked with a key theme from within this story or provided by the researcher.

Hermeneutics An approach to understanding phenomenology that relies on analysis of texts through which a person tells a story about him or herself.

Hidden observation Observation in which the subject is unaware that observation is taking place.

Histogram A graphical way of showing a frequency distribution in which the height of a bar corresponds to the observed frequency of the category.

History effect Occurs when some change other than the experimental treatment occurs during the course of an experiment that affects the dependent variable.

Host Where the content for a particular website physically resides and is accessed.

Human subjects review committee Carefully reviews proposed research designs to try to make sure that no harm can come to any research participant.

Hypothesis Formal statement of an unproven proposition that is empirically testable.

Hypothesis test of a proportion A test that is conceptually similar to the one used when the mean is the characteristic of interest but that differs in the mathematical formulation of the standard error of the proportion.

Hypothetical constructs Variables that are not directly observable but are measurable through indirect indicators, such as verbal expression or overt behavior.

I Idealism A term that reflects the degree to which one bases one’s

morality on moral standards. Image profile A graphic representation of semantic differential

data for competing brands, products, or stores to highlight comparisons.

Importance-performance analysis Another name for quadrant analysis.

Impute To fill in a missing data point through the use of a statistical algorithm that provides a best guess for the missing response based on available information.

In-house editing A rigorous editing job performed by a centralized office staff.

In-house interviewer A fieldworker who is employed by the company conducting the research.

In-house research Research performed by employees of the company that will benefit from the research.

Independent samples t-test A test for hypotheses which compares the mean scores for two groups comprised of some interval- or ratio-scaled variable using a less-than interval classificatory variable.

Independent variable A variable that is expected to influence the dependent variable in some way.

Index measures An index assigns a value based on how much of the concept being measured is associated with an observation. Indexes often are formed by putting several variables together.

Index numbers Scores or observations recalibrated to indicate how they relate to a base number.

Index of retail saturation A calculation that describes the relationship between retail demand and supply.

Inductive reasoning The logical process of establishing a general proposition on the basis of observation of particular facts.

Inferential statistics Using statistics to project characteristics from a sample to an entire population.

Information completeness Having the right amount of information.

Information Data formatted (structured) to support decision making or define the relationship between two facts.

Informed consent When an individual understands what the researcher wants him or her to do and consents to the research study.

Instrumentation effect A nuisance that occurs when a change in the wording of questions, a change in interviewers, or a change in other procedures causes a change in the dependent variable.

Interaction effect Differences in dependent variable means due to a specific combination of independent variables.

Interactive help desk In an Internet questionnaire, a live, real- time support feature that solves problems or answers questions respondents may encounter in completing the questionnaire.

Interactive medium A medium, such as the Internet, that a person can use to communicate with and interact with other users.

Interdependence techniques Multivariate statistical techniques that give meaning to a set of variables or seek to group things together; no distinction is made between dependent and independent variables.

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Internal and proprietary data Secondary data that originate inside the organization.

Internal consistency Represents a measure’s homogeneity or the extent to which each indicator of a concept converges on some common meaning.

Internal validity Exists to the extent that an experimental variable is truly responsible for any variance in the dependent variable.

Internet A worldwide network of computers that allows users access to information from distant sources.

Internet survey A self-administered questionnaire posted on a website.

Interpretation The process of drawing inferences from the analysis results.

Interquartile range A measure of variability. Interrogative techniques Asking multiple what, where, who,

when, why, and how questions. Intersubjective certifiability Different individuals following the

same procedure will produce the same results or come to the same conclusion.

Interval scales Scales that have both nominal and ordinal properties, but that also capture information about differences in quantities of a concept from one observation to the next.

Interviewer bias A response bias that occurs because the presence of the interviewer influences respondents’ answers.

Interviewer cheating The practice by fieldworkers of filling in fake answers or falsifying interviews.

Interviewer cheating The practice of filling in fake answers or falsifying questionnaires while working as an interviewer.

Interviewer error Mistakes made by interviewers failing to record survey responses correctly.

Intranet A company’s private data network that uses Internet standards and technology.

Introduction section The part of the body of a research report that discusses background information and the specific objectives of the research.

Inverse (negative) relationship Covariation in which the association between variables is in opposing directions. As one goes up, the other goes down.

Item nonresponse Failure of a respondent to provide an answer to a survey question.

Item nonresponse The technical term for an unanswered question on an otherwise complete questionnaire resulting in missing data.

J Judgment (purposive) sampling A nonprobability sampling

technique in which an experienced individual selects the sample based on personal judgment about some appropriate characteristic of the sample member.

K Keyword search Takes place as the search engine searches

through millions of web pages for documents containing the keywords.

Knowledge A blend of previous experience, insight, and data that forms organizational memory.

Knowledge management The process of creating an inclusive, comprehensive, easily accessible organizational memory, which is often called the organization’s intellectual capital.

L Laboratory experiment The researcher has more complete

control over the research setting and extraneous variables. Ladder of abstraction Organization of concepts in sequence from

the most concrete and individual to the most general. Laddering A particular approach to probing, asking respondents

to compare differences between brands at different levels that produces distinctions at the attribute level, the benefit level, and the value or motivation level.

Latent construct A concept that is not directly observable or measurable, but can be estimated through proxy measures.

Leading question A question that suggests or implies certain answers. Likert scale A measure of attitudes designed to allow respondents

to rate how strongly they agree or disagree with carefully constructed statements, ranging from very positive to very negative attitudes toward some object.

Literature review A directed search of published works, including periodicals and books, that discusses theory and presents empirical results that are relevant to the topic at hand.

Loaded question A question that suggests a socially desirable answer or is emotionally charged.

Longitudinal study A survey of respondents at different times, thus allowing analysis of response continuity and changes over time.

M Mail survey A self-administered questionnaire sent to respondents

through the mail. Main effect The experimental difference in dependent variable

means between the different levels of any single experimental variable.

Mall intercept interviews Personal interviews conducted in a shopping mall.

Manager of decision support systems Employee who supervises the collection and analysis of sales, inventory, and other periodic customer relationship management (CRM) data.

Managerial action standard A specific performance criterion upon which a decision can be based.

Manipulation check A validity test of an experimental manipulation to make sure that the manipulation does produce differences in the independent variable.

Manipulation Means that the researcher alters the level of the variable in specific increments.

Marginals Row and column totals in a contingency table, which are shown in its margins.

Market tracking The observation and analysis of trends in industry volume and brand share over time.

Market-basket analysis A form of data mining that analyzes anonymous point-of-sale transaction databases to identify coinciding purchases or relationships between products purchased and other retail shopping information.

Marketing-oriented Describes a firm in which all decisions are made with a conscious awareness of their effect on the customer.

Maturation effects Effects that are a function of time and the naturally occurring events that coincide with growth and experience.

Mean A measure of central tendency; the arithmetic average. Measure of association A general term that refers to a number of

bivariate statistical techniques used to measure the strength of a relationship between two variables.

652 Glossary

Measurement The process of describing some property of a phenomenon of interest, usually by assigning numbers in a reliable and valid way.

Median A measure of central tendency that is the midpoint; the value below which half the values in a distribution fall.

Median split Dividing a data set into two categories by placing respondents below the median in one category and respondents above the median in another.

Mixed-mode survey Study that employs any combination of survey methods.

Mode A measure of central tendency; the value that occurs most often. Model building The use of secondary data to help specify

relationships between two or more variables; can involve the development of descriptive or predictive equations.

Moderator A person who leads a focus group interview and ensures that everyone gets a chance to speak and contribute to the discussion.

Moderator variable A third variable that changes the nature of a relationship between the original independent and dependent variables.

Monadic rating scale Any measure of attitudes that asks respondents about a single concept in isolation.

Moral standards Principles that reflect beliefs about what is ethical and what is unethical.

Mortality effect (sample attrition) Occurs when some subjects withdraw from the experiment before it is completed.

Multicollinearity The extent to which independent variables in a multiple regression analysis are correlated with each other; high multicollinearity can make interpreting parameter estimates difficult or impossible.

Multidimensional scaling A statistical technique that measures objects in multidimensional space on the basis of respondents’ judgments of the similarity of objects.

Multiple regression analysis An analysis of association in which the effects of two or more independent variables on a single, interval-scaled dependent variable are investigated simultaneously.

Multiple-grid question Several similar questions arranged in a grid format.

Multistage area sampling Sampling that involves using a combination of two or more probability sampling techniques.

Multivariate analysis of variance (MANOVA) A multivariate technique that predicts multiple continuous dependent variables with multiple categorical independent variables.

Multivariate statistical analysis Statistical analysis involving three or more variables or sets of variables.

Mutually exclusive No overlap exists among the fixed-alternative categories

N Neural networks A form of artificial intelligence in which a

computer is programmed to mimic the way that human brains process information.

No contacts People who are not at home or who are otherwise inaccessible on the first and second contact.

Nominal scales Represent the most elementary level of measurement in which values are assigned to an object for identification or classification purposes only.

Nonparametric statistics Appropriate when the variables being analyzed do not conform to any known or continuous distribution.

Nonprobability sampling A sampling technique in which units of the sample are selected on the basis of personal judgment or convenience; the probability of any particular member of the population being chosen is unknown.

Nonrespondent error Error that the respondent is not responsible for creating, such as when the interviewer marks a response incorrectly.

Nonrespondents People who are not contacted or who refuse to cooperate in the research.

Nonresponse error The statistical differences between a survey that includes only those who responded and a perfect survey that would also include those who failed to respond.

Nonspurious association One of three criteria for causality; means any covariation between a cause and an effect is true and not simply due to some other variable.

Normal distribution A symmetrical, bell-shaped distribution that describes the expected probability distribution of many chance occurrences.

Nuisance variables Items that may affect the dependent measure but are not of primary interest.

Numerical scale An attitude rating scale similar to a semantic differential except that it uses numbers, instead of verbal descriptions, as response options to identify response positions.

O Observation The systematic process of recording the behavioral

patterns of people, objects, and occurrences as they are witnessed.

Observer bias A distortion of measurement resulting from the cognitive behavior or actions of a witnessing observer.

Online focus group A qualitative research effort in which a group of individuals provides unstructured comments by entering their remarks into an electronic Internet display board of some type.

Open-ended boxes In an Internet questionnaire, boxes where respondents can type in their own answers to open-ended questions.

Open-ended response questions Questions that pose some problem and ask respondents to answer in their own words.

Operationalization The process of identifying scales that correspond to variance in a concept that will be involved in a research process.

Operationalizing The process of identifying the actual measurement scales to assess the variables of interest.

Opt in To give permission to receive selected e-mail, such as questionnaires, from a company with an Internet presence.

Optical scanning system A data processing input device that reads material directly from mark-sensed questionnaires.

Oral presentation A spoken summary of the major findings, conclusions, and recommendations, given to clients or line managers to provide them with the opportunity to clarify any ambiguous issues by asking questions.

Order bias Bias caused by the influence of earlier questions in a questionnaire or by an answer’s position in a set of answers.

Ordinal scales Ranking scales allowing things to be arranged based on how much of some concept they possess.

Outlier A value that lies outside the normal range of the data. Outside agency An independent research firm contracted by the

company that actually will benefit from the research.

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P P-value Probability value, or the observed or computed

significance level; p-values are compared to significance levels to test hypotheses.

Paired comparisons A measurement technique that involves presenting the respondent with two objects and asking the respondent to pick the preferred object; more than two objects may be presented, but comparisons are made in pairs.

Paired-samples t-test An appropriate test for comparing the scores of two interval variables drawn from related populations.

Parametric statistics Involve numbers with known, continuous distributions; when the data are interval or ratio scaled and the sample size is large, parametric statistical procedures are appropriate.

Partial correlation The correlation between two variables after taking into account the fact that they are correlated with other variables too.

Participant-observation Ethnographic research approach where the researcher becomes immersed within the culture that he or she is studying and draws data from his or her observations.

Percentage distribution A frequency distribution organized into a table (or graph) that summarizes percentage values associated with particular values of a variable.

Performance-monitoring research Refers to research that regularly, sometimes routinely, provides feedback for evaluation and control of business activity.

Personal interview Face-to-face communication in which an interviewer asks a respondent to answer questions.

Phenomenology A philosophical approach to studying human experiences based on the idea that human experience itself is inherently subjective and determined by the context in which people live.

Piggyback A procedure in which one respondent stimulates thought among the others; as this process continues, increasingly creative insights are possible.

Pilot study A small-scale research project that collects data from respondents similar to those to be used in the full study.

Pivot question A filter question used to determine which version of a second question will be asked.

Placebo A false experimental condition aimed at creating the impression of an effect.

Placebo A false experimental effect used to create the perception that some effect has been administered.

Placebo effect The effect in a dependent variable associated with the psychological impact that goes along with knowledge of some treatment being administered.

Plug value An answer that an editor “plugs in” to replace blanks or missing values so as to permit data analysis; choice of value is based on a predetermined decision rule.

Point estimate An estimate of the population mean in the form of a single value, usually the sample mean.

Pooled estimate of the standard error An estimate of the standard error for a t-test of independent means that assumes the variances of both groups are equal.

Pop-up boxes In an Internet questionnaire, boxes that appear at selected points and contain information or instructions for respondents.

Population (universe) Any complete group of entities that share some common set of characteristics.

Population distribution A frequency distribution of the elements of a population.

Population element An individual member of a population. Population parameters Variables in a population or measured

characteristics of the population. Preliminary tabulation A tabulation of the results of a pretest

to help determine whether the questionnaire will meet the objectives of the research.

Pretest A small-scale study in which the results are only preliminary and intended only to assist in design of a subsequent study.

Pretesting Screening procedure that involves a trial run with a group of respondents to iron out fundamental problems in the survey design.

Primary sampling unit (PSU) A term used to designate a unit selected in the first stage of sampling.

Probability sampling A sampling technique in which every member of the population has a known, nonzero probability of selection.

Probability The long-run relative frequency with which an event will occur.

Probing An interview technique that tries to draw deeper and more elaborate explanations from the discussion.

Problem definition The process of defining and developing a decision statement and the steps involved in translating it into more precise research terminology, including a set of research objectives.

Problem Occurs when there is a difference between the current conditions and a more preferable set of conditions.

Product-oriented Describes a firm that prioritizes decision making in a way that emphasizes technical superiority in the product.

Production-oriented Describes a firm that prioritizes efficiency and effectiveness of the production processes in making decisions.

Projective technique An indirect means of questioning enabling respondents to project beliefs and feelings onto a third party, an inanimate object, or a task situation.

Proportion The percentage of elements that meet some criterion. Proportional stratified sample A stratified sample in which

the number of sampling units drawn from each stratum is in proportion to the population size of that stratum.

Propositions Statements explaining the logical linkage among certain concepts by asserting a universal connection between concepts.

Proprietary business research The gathering of new data to investigate specific problems.

Pseudo-research Conducted not to gather information for marketing decisions but to bolster a point of view and satisfy other needs.

Psychogalvanometer A device that measures galvanic skin response, a measure of involuntary changes in the electrical resistance of the skin.

Pull technology Consumers request information from a web page and the browser then determines a response; the consumer is essentially asking for the data.

Pupilometer A mechanical device used to observe and record changes in the diameter of a subject’s pupils.

Push button In a dialog box on an Internet questionnaire, a small outlined area, such as a rectangle or an arrow, that the respondent clicks on to select an option or perform a function, such as submit.

Push poll Telemarketing under the guise of research. Push technology Sends data to a user’s computer without a

request being made; software is used to guess what information might be interesting to consumers based on the pattern of previous responses.

654 Glossary

Q Quadrant analysis An extension of cross-tabulation in which

responses to two rating-scale questions are plotted in four quadrants of a two-dimensional table.

Qualitative business research Research that addresses business objectives through techniques that allow the researcher to provide elaborate interpretations of phenomena without depending on numerical measurement; its focus is on discovering true inner meanings and new insights.

Qualitative data Data that are not characterized by numbers, and instead are textual, visual, or oral; focus is on stories, visual portrayals, meaningful characterizations, interpretations, and other expressive descriptions.

Quantitative business research Business research that addresses research objectives through empirical assessments that involve numerical measurement and analysis.

Quantitative data Represent phenomena by assigning numbers in an ordered and meaningful way.

Quasiexperimental designs Experimental designs that do not involve random allocation of subjects to treatment combinations.

Quota sampling A nonprobability sampling procedure that ensures that various subgroups of a population will be represented on pertinent characteristics to the exact extent that the investigator desires.

R Radio button In an Internet questionnaire, a circular icon,

resembling a button, which activates one response choice and deactivates others when a respondent clicks on it.

Random digit dialing Use of telephone exchanges and a table of random numbers to contact respondents with unlisted phone numbers.

Random sampling error A statistical fluctuation that occurs because of chance variation in the elements selected for a sample.

Random sampling error The difference between the sample result and the result of a census conducted using identical procedures.

Randomization The random assignment of subject and treatments to groups; it is one device for equally distributing the effects of extraneous variables to all conditions.

Randomized-block design A design that attempts to isolate the effects of a single extraneous variable by blocking out its effects on the dependent variable.

Ranking A measurement task that requires respondents to rank order a small number of stores, brands, or objects on the basis of overall preference or some characteristic of the stimulus.

Rating A measurement task that requires respondents to estimate the magnitude of a characteristic or quality that a brand, store, or object possesses.

Ratio scales Represent the highest form of measurement in that they have all the properties of interval scales with the additional attribute of representing absolute quantities; characterized by a meaningful absolute zero.

Raw data The unedited information gathered from a respondent in the exact form as provided by that respondent.

Record A collection of related fields that represents the responses from one sampling unit.

Refusals People who are unwilling to participate in a research project.

Relativism A term that reflects the degree to which one rejects moral standards in favor of the acceptability of some action. This way of thinking rejects absolute principles in favor of situation-based evaluations.

Relevance A characteristic of data reflecting how pertinent these particular facts are to the situation at hand.

Reliability An indicator of a measure’s internal consistency. Repeated measures Experiments in which an individual

subject is exposed to more than one level of an experimental treatment.

Replication The same interpretation will be drawn if the study is repeated by different researchers with different respondents following the same methods.

Report format The makeup or arrangement of parts necessary to a good research report.

Research analyst A person responsible for client contact, project design, preparation of proposals, selection of research suppliers, and supervision of data collection, analysis, and reporting activities.

Research assistants Research employees who provide technical assistance with questionnaire design, data analyses, and similar activities.

Research design A master plan that specifies the methods and procedures for collecting and analyzing the needed information.

Research follow-up Recontacting decision makers and/or clients after they have had a chance to read over a research report in order to determine whether additional information or clarification is necessary.

Research generalist An employee who serves as a link between management and research specialists. The research generalist acts as a problem definer, an educator, a liaison, a communicator, and a friendly ear.

Research methodology section The part of the body of a report that presents the findings of the project. It includes tables, charts, and an organized narrative.

Research objectives The goals to be achieved by conducting research.

Research program Numerous related studies that come together to address multiple, related research objectives.

Research project A single study that addresses one or a small number of research objectives.

Research proposal A written statement of the research design. Research questions Express the research objectives in terms of

questions that can be addressed by research. Research report An oral presentation or written statement of

research results, strategic recommendations, and/or other conclusions to a specific audience.

Research suppliers Commercial providers of research services. Researcher-dependent Research in which the researcher must

extract meaning from unstructured responses such as text from a recorded interview or a collage representing the meaning of some experience.

Respondent error A category of sample bias resulting from some respondent action or inaction such as nonresponse or response bias.

Respondents People who verbally answer an interviewer’s questions or provide answers to written questions.

Response bias A bias that occurs when respondents either consciously or unconsciously tend to answer questions with a certain slant that misrepresents the truth.

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Response latency The amount of time it takes to make a choice between two alternatives; used as a measure of the strength of preference.

Response rate The number of questionnaires returned or completed divided by the number of eligible people who were asked to participate in the survey.

Results section The part of the body of a report that presents the findings of the project. It includes tables, charts, and an organized narrative.

Reverse coding Changing the value of a response to a scale so it is opposite of the original value. For example, a scale from 1–5 is reversed so 1=5, 2=4, 3=3, 4=2, and 5=1. This if often done so negative items in a scale are scored in the same direction as positive items.

Reverse directory A directory similar to a telephone directory except that listings are by city and street address or by phone number rather than alphabetical by last name.

Rule of parsimony The rule of parsimony suggests that an explanation involving fewer components is better than one involving more.

S Sample A subset, or some part, of a larger population. Sample bias A persistent tendency for the results of a sample to

deviate in one direction from the true value of the population parameter.

Sample distribution A frequency distribution of a sample. Sample selection error An administrative error caused by

improper sample design or sampling procedure execution. Sample statistics Variables in a sample or measures computed

from sample data. Sample survey A more formal term for a survey. Sampling distribution A theoretical probability distribution of

sample means for all possible samples of a certain size drawn from a particular population.

Sampling frame A list of elements from which a sample may be drawn; also called working population.

Sampling frame error An error that occurs when certain sample elements are not listed or are not accurately represented in a sampling frame.

Sampling Involves any procedure that draws conclusions based on measurements of a portion of the population.

Sampling unit A single element or group of elements subject to selection in the sample.

Scales A device providing a range of values that correspond to different values in a concept being measured.

Scanner data The accumulated records resulting from point-of- sale data recordings.

Scanner-based consumer panel A type of consumer panel in which participants’ purchasing habits are recorded with a laser scanner rather than a purchase diary.

Scientific method A set of prescribed procedures for establishing and connecting theoretical statements about events, for analyzing empirical evidence, and for predicting events yet unknown; techniques or procedures used to analyze empirical evidence in an attempt to confirm or disprove prior conceptions.

Search engine A computerized directory that allows anyone to search the World Wide Web for information using a keyword search.

Secondary data Data that have been previously collected for some purpose other than the one at hand.

Secondary sampling unit A term used to designate a unit selected in the second stage of sampling.

Selection effect Sample bias from differential selection of respondents for experimental groups.

Self-administered questionnaires Surveys in which the respondent takes the responsibility for reading and answering the questions.

Self-selection bias A bias that occurs because people who feel strongly about a subject are more likely to respond to survey questions than people who feel indifferent about it.

Semantic differential A measure of attitudes that consists of a series of seven-point rating scales that use bipolar adjectives to anchor the beginning and end of each scale.

Sensitivity A measurement instrument’s ability to accurately measure variability in stimuli or responses.

Significance level A critical probability associated with a statistical hypothesis test that indicates how likely an inference supporting a difference between an observed value and some statistical expectation is true. The acceptable level of Type I error.

Simple (bivariate) linear regression A measure of linear association that investigates straight-line relationships between a continuous dependent variable and an independent variable that is usually continuous, but can be a categorical dummy variable.

Simple random sampling A sampling procedure that assures each element in the population of an equal chance of being included in the sample.

Simple-dichotomy (dichotomous) question A fixed-alternative question that requires the respondent to choose one of two alternatives.

Single-source data Diverse types of data offered by a single company; usually integrated on the basis of a common variable such as geographic area or store.

Site analysis techniques Techniques that use secondary data to select the best location for retail or wholesale operations.

Situation analysis The gathering of background information to familiarize researchers and managers with the decision-making environment.

Smart agent software Software capable of learning an Internet user’s preferences and automatically searching out information in selected websites and then distributing it.

Snowball sampling A sampling procedure in which initial respondents are selected by probability methods and additional respondents are obtained from information provided by the initial respondents.

Social desirability bias Bias in responses caused by respondents’ desire, either conscious or unconscious, to gain prestige or appear in a different social role.

Sorting A measurement task that presents a respondent with several objects or product concepts and requires the respondent to arrange the objects into piles or classify the product concepts.

Split-ballot technique Using two alternative phrasings of the same question for respective halves of a sample to elicit a more accurate total response than would a single phrasing.

Split-half method A method for assessing internal consistency by checking the results of one-half of a set of scaled items against the results from the other half.

656 Glossary

Spyware Software placed on a computer without consent or knowledge of the user.

Standard deviation A quantitative index of a distribution’s spread, or variability; the square root of the variance for a distribution.

Standard error of the mean The standard deviation of the sampling distribution.

Standardized normal distribution A purely theoretical probability distribution that reflects a specific normal curve for the standardized value, z.

Standardized regression coefficient () The estimated coefficient indicating the strength of relationship between an independent variable and dependent variable expressed on a standardized scale where higher absolute values indicate stronger relationships (range is from 21 to 1).

Standardized research service Companies that develop a unique methodology for investigating a business specialty area.

Stapel scale A measure of attitudes that consists of a single adjective in the center of an even number of numerical values.

Statistical base The number of respondents or observations (in a row or column) used as a basis for computing percentages.

Status bar In an Internet questionnaire, a visual indicator that tells the respondent what portion of the survey he or she has completed.

Stratified sampling A probability sampling procedure in which simple random subsamples that are more or less equal on some characteristic are drawn from within each stratum of the population.

String characters Computer terminology to represent formatting a variable using a series of alphabetic characters (nonnumeric characters) that may form a word.

Structured question A question that imposes a limit on the number of allowable responses.

Subjective Results are researcher-dependent, meaning different researchers may reach different conclusions based on the same interview.

Subjects The sampling units for an experiment, usually human respondents who provide measures based on the experimental manipulation.

Sugging Selling under the guise of research. Summated scale A scale created by simply summing (adding

together) the response to each item making up the composite measure.

Survey A research technique in which a sample is interviewed in some form or the behavior of respondents is observed and described in some way.

Symptoms Observable cues that serve as a signal of a problem because they are caused by that problem.

Syndicated service A research supplier that provides standardized information for many clients in return for a fee.

Systematic error Error resulting from some imperfect aspect of the research design that causes respondent error or from a mistake in the execution of the research.

Systematic or nonsampling error Occurs if the sampling units in an experimental cell are somehow different than the units in another cell, and this difference affects the dependent variable.

Systematic sampling A sampling procedure in which a starting point is selected by a random process and then every nth number on the list is selected.

T t-distribution A symmetrical, bell-shaped distribution that is

contingent on sample size; has a mean of 0 and a standard deviation equal to 1.

t-test A hypothesis test that uses the t-distribution. A univariate t-test is appropriate when the variable being analyzed is interval or ratio.

Tabulation The orderly arrangement of data in a table or other summary format showing the number of responses to each response category; tallying.

Tachistoscope Device that controls the amount of time a subject is exposed to a visual image.

Telephone interviews Personal interviews conducted by telephone, the mainstay of commercial survey research.

Television monitoring Computerized mechanical observation used to obtain television ratings.

Temporal sequence One of three criteria for causality; deals with the time order of events—the cause must occur before the effect.

Tertiary sampling unit A term used to designate a unit selected in the third stage of sampling.

Test tabulation Tallying of a small sample of the total number of replies to a particular question in order to construct coding categories.

Test units The subjects or entities whose responses to the experimental treatment are measured or observed.

Test-market An experiment that is conducted within actual market conditions.

Test-retest method Administering the same scale or measure to the same respondents at two separate points in time to test for stability.

Testing effects A nuisance effect occurring when the initial measurement or test alerts or primes subjects in a way that affects their response to the experimental treatments.

Tests of differences The investigation of hypotheses stating that two (or more) groups differ with respect to measures on a variable.

The scientific method The way researchers go about using knowledge and evidence to reach objective conclusions about the real world.

Thematic apperception test (TAT) A test that presents subjects with an ambiguous picture(s) in which consumers and products are the center of attention; the investigator asks the subject to tell what is happening in the picture(s) now and what might happen next.

Themes Identified by the frequency with which the same term (or a synonym) arises in the narrative description.

Theory A formal, logical explanation of some events that includes predictions of how things relate to one another.

Thurstone scale An attitude scale in which judges assign scale values to attitudinal statements and subjects are asked to respond to these statements.

Time series design Used for an experiment investigating long- term structural changes.

Timeliness Means that the data are current enough to still be relevant.

Total quality management A business philosophy that emphasizes market-driven quality as a top organizational priority.

Total variability The sum of within-group variance and between- groups variance.

Glossary 657

Totally exhaustive A category exists for every respondent among the fixed-alternative categories.

Tracking study A type of longitudinal study that uses successive samples to compare trends and identify changes in variables such as consumer satisfaction, brand image, or advertising awareness.

Type I error An error caused by rejecting the null hypothesis when it is true; has a probability of alpha. Practically, a Type I error occurs when the researcher concludes that a relationship or difference exists in the population when in reality it does not exist.

Type II error An error caused by failing to reject the null hypothesis when the alternative hypothesis is true; has a probability of beta. Practically, a Type II error occurs when a researcher concludes that no relationship or difference exists when in fact one does exist.

U Unbalanced rating scale A fixed-alternative rating scale that

has more response categories at one end than the other, resulting in an unequal number of positive and negative categories.

Undisguised questions Straightforward questions that assume the respondent is willing to answer.

Uniform resource locator (URL) A website address that web browsers recognize.

Unit of analysis A study indicates what or who should provide the data and at what level of aggregation.

Univariate statistical analysis Tests of hypotheses involving only one variable.

Unobtrusive methods Methods in which research respondents do not have to be disturbed for data to be gathered.

Unstructured question A question that does not restrict the respondents’ answers.

V Validity The accuracy of a measure or the extent to which a score

truthfully represents a concept. Value labels Unique labels assigned to each possible numeric code

for a response.

Variable Anything that varies or changes from one instance to another; can exhibit differences in value, usually in magnitude or strength, or in direction.

Variable piping software Software that allows variables to be inserted into an Internet questionnaire as a respondent is completing it.

Variables Anything that may assume different numerical values; the empirical assessment of a concept.

Variance A measure of variability or dispersion. Its square root is the standard deviation.

Variate A mathematical way in which a set of variables can be represented with one equation.

Verification Quality-control procedures in fieldwork intended to ensure that interviewers are following the sampling procedures and to determine whether interviewers are cheating.

Visible observation Observation in which the observer’s presence is known to the subject.

Voice-pitch analysis A physiological measurement technique that records abnormal frequencies in the voice that are supposed to reflect emotional reactions to various stimuli.

W Welcome screen The first web page in an Internet survey, which

introduces the survey and requests that the respondent enter a password or PIN.

Within-group error or variance The sum of the differences between observed values and the group mean for a given set of observations; also known as total error variance.

Within-subjects design Involves repeated measures because with each treatment the same subject is measured.

World Wide Web (WWW) A portion of the Internet that is a system of computer servers that organize information into documents called web pages.

Z Z-test for differences of proportions A technique used to test

the hypothesis that proportions are significantly different for two independent samples or groups.

658 Glossary

659

ENDNOTES

Chapter 1 1 Frankland, D., Vittal, S., Grant, M., &

Dickson, M. “Case Study: ESPN Drives Fan Value Through Customer Intelligence”. Published March 1, 2011. Forrester Research Inc., Cambridge, MA.

2 “U.S. Coffee Makers Perky as Consumption Increases,” Nations Restaurant Business 36 (April 22, 2002), 34; “U.S. Specialty Coffee Market in 30 Year Renaissance,” (December 15, 2000), http://www .cnn.com.

3 www.starbucks.com. 4 Adapted from “DuPont Employee

Survey Finds Eldercare Emerging as Key Work/Life Issues,” PR Newswire ( January 2, 2001), 49–93.

5 Garvin, Andrew P., “Evolve Approach to Serve Complex Market,” Marketing News (September 15, 2005), 22.

6 Gibson, Lawrence D., “Quo Vadis Marketing Research?” Marketing Research 12 (Spring 2000), 36–41.

7 Matthew, Arnold, “FDA Delays DTC Draft Guidance to Study How Consumers Use Brief Summaries,” Medical Marketing and Media 39 (November 2004), 10.

8 Reyes, Sonia, “Ian Friendly: Groove Tube,” BrandWeek (October 16, 2000), M111–M116.

9 Garretson, Judith and Scot Burton, “The Role of Spokescharacters as Advertisement and Package Cues in Integrated Marketing Communications,” Journal of Marketing 69 (October 2005), 118–132.

10 Clancy, Kevin J. and Randy L. Stone, “Don’t Blame the Metrics,” Harvard Business Review 83 ( June 2005), 26–28.

11 Honomichl, Jack, “Growth Stunt,” Marketing News ( June 4, 2001), 144.

12 “You Say Tomato, I Say Tomahto,” Express Magazine (Spring 2006), 19.

Chapter 2 1 1 - 2010 UPS Annual Report

(available at www.ups.com). 2 See Albers, Brad, “Home Depot’s

Special Projects Support Team Powers Information Management for Business Needs,” Journal of Organizational Excellence 21 (Winter 2001), 3–15; Songini, Marc L., “Home Depot’s Next IT Project: Data Warehouse,” Computerworld 36 (October 7, 2002), 1–2.

3 LaBahn, Douglass W. and Robert Krapfel, “Early Supplier Involvement in Customer New Product Development: A Contingency Model of Component Supplier Intentions,” Journal of Business Research 47 (March 2000), 173–190.

4 Tay, Nicholas S. P. and Robert F. Lusch, “A Preliminary Test of Hunt’s General Theory of Competition: Using Artificial Adaptive Agents to Study Complex and Ill-Defined Environments,” Journal of Business Research 58 (September 2005), 1155–1168.

5 Knapp, Ellen M., “Knowledge Management,” Business and Economic Review ( July–September 1998), 3–6.

6 Sherman, D. J., D. Berkowitz, and W. E. Soulder, “New Product Development Performance and the Interaction of Cross-Functional Integration and Knowledge Management,” Journal of Product Innovation Management 22 (September 2005), 399–411.

7 Chonko, L. B., A. J. Dubinsky, E. Jones, and J. A. Roberts, “Organizational and Individual Learning in the Sales Force: An Agenda for Sales Research,” Journal of Business Research 56 (December 2003), 935–946.

8 “Benefits of RFID Becoming More Visible,” DSN Retailing Today (August 8, 2005), 22.

9 Hall, Mark, “Seeding for Data Growth,” Computerworld 36 (April 15, 2002), 52.

10 Angwein, J. and Delaney, K. J., “Top Web Sites Build Up Ad Backlog, Raise Rates,” Wall Street Journal (November 16, 2005), A1.

11 Gale Annual Directory of Databases, Gale Research Inc.: Detroit.

12 Patrick A. Moore and Ronald Milliman, “Application of the Internet in Marketing Education,” (paper presented at the Southwest Marketing Association, Houston, Texas, 1995).

13 Geng, X., M. B. Stinchcombe, and A. B. Whinston, “Radically New Product Introduction Using On-Line Auctions,” Journal of Electronic Commerce 5 (Spring 2001), 169–189.

14 “A Better Web Through Higher Math” Business Week Online ( January 2, 2002), http://www.businessweek .com, accessed November 12, 2005.

15 Rangaswamy, Arvind and G. Lilien, “Software Tools for New Product Development,” Journal of Marketing Research 34 (February 1997), 177–184.

16 Desouza, Kevin and Yukika Awazu, “Maintaining Knowledge Management Systems: A Strategic Imperative,” Journal of the American Society for Information Science and Technology 56 (May 2005), 765–768.

17 Rangaswamy, Arvind and G. Lilien (1997).

18 Close, A. G., A. Dixit and N. Malhotra, “Chalkboards to Cybercourses: The Internet and Marketing Education,” Marketing Education Review 15 (Summer 2005), 81–94.

19 Adapted with permission from deJony, Jennifer, “View from the Top,” Technology 1 (1995), downloaded from the Internet July 3, 1998.

Chapter 3 1 Cox, Reavis, Wroe Alderson, and

Stanley J. Shapiro, Theory in Marketing (Chicago: American Marketing Association, 1964), 20.

2 Babin, B. J., D. M. Hardesty, and T. A. Suter, “Color and Shopping Intentions: The Effect of Price Fairness and Perceived Affect,” Journal of Business Research 56 ( July 2003) 541–551.

3 Robert Dubin, Theory Building (New York: Free Press, 1969), 9.

4 Pietrobon, Ricardo, Marcus Taylor, Ulrich Guller, Laurence D. Higgins, Danny O. Jacobs, and Timothy Carey, “Predicting Gender Differences as Latent Variables: Summed Scores, and Individual Item Responses: A Methods Case Study,” Health and Quality of Life Outcomes (2004), 2–59.

5 Robert Bartels, Marketing Theory and Metatheory (Chicago: American Marketing Association, 1970), 6.

6 Hull, Clark L., A Behavioral System (New York: John Wiley & Sons, 1952), 1.

7 Based on Ellen J. Jackofsky, “Turnover and Job Performance: An Integrated Process Model,” Academy of Management Review 9, No. 1 (1984), p. 78; and Paul Solomon, “Reducing

Unwanted Staff Turnovers in Public Accounting: An Action Plan,” Northern California Executive Review (Spring 1986), pp. 22–25.

8 Karl Popper, Conjectures and Refutations (London: Routledge and Keagan Paul, 1963).

9 Kerlinger, Fred N., Behavioral Research: A Conceptual Approach (New York: Holt, Rinehart and Winston, 1979), 3.

10 Zaltman, Gerald, Christian Pinson, and Reinhart Angelmar, Metatheory and Consumer Research (New York: Holt, Rinehart and Winston, 1972), 12–13.

Chapter 4 1 “Hewlett-Packard Makes Hoover’s

Part of Global CRM Strategy and End-to-End Sales Process,” http:// www.hoovers.com/about/case- studies/100000292-1.html, accessed August 3, 2011.

2 This section is based in part on Richard Draft, Management (Hillsdale, IL: Dryden Press, 1994).

3 Zahay, Debra, Abbie Griffin, and Elisa Fredericks, “Sources, Uses, and Forms of Data in the New Product Development Process,” Industrial Marketing Management 33 (October 2004), 658–666.

4 Hara, Yoshika, “New Industry Awaits Human-Friendly Bipeds— ‘Personal Robots’ get Ready to Walk on the Human Side,” Electronic Engineering Times (September 16, 2002), 157–159.

5 Janoff, Barry, “Brands of the Land,” BrandWeek (April 20, 2001), 28.

6 Bocchi, Eastman and Swift (2004); Carr, S., “As Distance Education Comes of Age, the Challenge Is Keeping the Students,” Chronicle of Higher Education (2000), 23, A1; Moskal, P. D. and C. G. Dziuban, “Present and Future Directions for Assessing Cybereducation: The Changing Research Paradigm,” in L. R. Vandervert, L. V. Chavinina, and R. A. Cornell, Eds., Cybereducation: The Future of Long-Distance Learning (New York: P. D. Moskal and C. G. Dziuban, Liebert, 2001), 157–184.

7 Read, Melissa, “Ice Cream Purchases and Murder Rates—Correlation Does Not Imply Causation” Spunlogic Blog (February 5th, 2007), http://blog.spunlogic .com/index.php/2007/02/05/ ice-cream-purchases-and-murder- rates-correlation-does-not-imply- causation/.

8 Thomas, Jerry W., “Skipping MR a Major Error,” Marketing News (March 4, 2005), 50.

9 Einstein, A. and L. Infeld, The Evolution of Physics (New York: Simon and Schuster, 1942), 95.

10 Perdue, B. C. and J. O. Summers, “Checking the Success of Manipulations in Marketing Experiments,” Journal of Marketing Research 23 (November 1986), 317–326.

11 See, for example, Kwok, S. and M. Uncles, “Sales Promotion Effectiveness: The Impact of Consumer Differences at an Ethnic- Group Level,” Journal of Product and Brand Management 14, no. 3 (2005), 170–186.

12 Approximate exchange rates as of January 25, 2009.

13 Crowley, Michael, “Conservatives (Finally) Rejoice,” New Republic (2004) 231, 13–14.

Chapter 5 1 Kinnear, Thomas C. and Ann Root,

Eds., Survey of Marketing Research (Chicago: American Marketing Association, 1994).

2 Blembach, J. and K. Clancy, “Boy, Oh Boy!” Adweek 21 (September 25, 2000), 16, Southeastern Edition.

3 See Armstrong, J. S., “Why Do We Know? Predicting the Interests and Opinions of the American Consumer,” Journal of Forecasting 5 (September 1989), 464.

4 See John, Joby and Mark Needel, “Entry-Level Marketing Research Recruits: What Do Recruiters Need?” Journal of Marketing Education (Spring 1989), 68–73.

5 See Izzo, G. Martin and Scott J. Vitell, “Exploring the Effects of Professional Education on Salespeople: The Case of Autonomous Agents,” Journal of Marketing Theory & Practice 11 (Fall 2003), 26–38; Loe, Terry and William A. Weeks, “An Empirical Investigation of Efforts to Improve Sales Students’ Moral Reasoning,” Journal of Personal Selling and Sales Management 20 (Fall 2000), 243–252.

6 Barnett, Tim and Sean Valentino, “Issue Contingencies and Marketers’ Recognition of Ethical Issues, Ethical Judgments and Behavioral Intentions,” Journal of Business Research 57 (April 2004), 338–346.

7 Robin, D. P., R. E. Reidenbach, and B. J. Babin, “The Nature, Measurement and Stability of Ethical Judgments in the Workplace,” Psychological Reports 80 (1997), 563–580.

8 Gillin, Donna L., “The Evolution of Privacy Legislation: How Privacy Issues Are Changing Research,” Marketing Research 13 (Winter 2001), 6–7.

9 Jarvis, Steve, “CMOR Finds Survey Refusal Rate Still Rising,” Marketing News 36 (February 4, 2002), 4.

10 Gillin, Donna L., “The Evolution of Privacy Legislation: How Privacy Issues Are Changing Research,” Marketing Research 13 (Winter 2001), 6–7.

11 Spangenberg, E., B. Grohmann, and D. E. Sprott, “It’s Beginning to Smell (and Sound) a Lot Like Christmas: The Interactive Effects of Ambient Scent and Music in a Retail Setting,” The Journal of Business Research 58 (November 2005), 582–589; Michon, Richard, Jean-Charles Chebat, and L. W. Turley, “Mall Atmospherics: The Interaction Effects of the Mall Environment on Shopping Behavior,” Journal of Business Research 58 (May 2005), 576–583.

12 Robinson, K, “Wal-Mart Push Polls Chicago, Claims 74% Support for New Store,” Chicago Sun Times, ( July 29, 2009), http://chicagoist .com/2009/07/29/is_wal-mart_ push_polling_chicago.php, accessed March 13, 2011.

13 “Marketers Value Honesty in Marketing Researchers,” Marketing News 29 ( June 5, 1995), 27.

14 Brennan, M., S. Benson, and Z. Kearns, “The Effect, of Introductions on Telephone Survey Participation Rates,” International Journal of Market Research 47, no. 1 (2005), 65–74.

15 Mack, Beth, “Online Privacy Critical to Research Success,” Marketing News 36 (November 25, 2002), 21.

Chapter 6 1 Gibson, Lawrence D., “Defining

Marketing Problems: Don’t Spin Your Wheels Solving the Wrong Puzzle,” Marketing Research 10 (Spring 1998), 4–12.

2 “What’s New in Your Industry,” Business China 30 (February 16, 2004), 8–9.

3 Chapman, Randall G., “Problem Definition in Marketing Research Studies,” Journal of Consumer Marketing 6 (Spring 1989), 51–59; Yang, Yoo S., Robert P. Leone, and Dala L. Aldaen, “A Market Expansion Ability Approach to Identify Potential Exporters,” Journal of Marketing 56 ( January 1992), 84–96.

4 Ackoff, Russell L., The Scientific Method (New York: Wiley, 1962), 71.

5 Majchrzak, A., L. P. Cooper, and O. E. Neece, “Knowedge Reuse for Innovation,” Management Science 50 (February 2004), 174–188.

6 Gibson, Lawrence D. (1988). 7 Gibson, Lawrence D. (1988). 8 Chapman, Randall G. (1989). 9 Holbert, N. B., “Research: The Ways

of Academe and Business,” Business Horizons (February 1976), 38.

10 Honomichl, Jack, “ICR/International Communications Research,” Marketing News 36 ( June 11, 2002), 47.

11 Excerpt reprinted with permission from Paul E. Green, Abba M. Krieger, and Terry G. Varra, “Evaluating New Products,” Marketing Research: A Magazine of Management and Applications (Winter 1997), 17–18.

Chapter 7 1 Cassidy, Hilary, “Many Paths to Cool,

but big Gains for All,” Brandweek 46 ( June 20, 2005), S53.

2 Niemi, Wayne, “Schoenfeld to Leave as Vans CEO; As Its Deal with VF Corp. Closes, the Skate Brand Gains a New President and a New Focus on Apparel,” Footwear News ( July 5, 2004), 2.

3 McLaughlin, Lisa, “The New Roll Model,” Time 164 ( July 26, 2004), 74.

4 Sayre, Shay, Qualitative Methods for Marketplace Research (Thousand Oaks, CA: Sage, 2001).

5 Sayre, Shay, (2001); Morse, Janice M. and Lyn Richards, Read Me First for a User’s Guide to Qualitative Methods (Thousand Oaks, CA: Sage, 2002).

6 See, for example, May, Carl, “Methodological Pluralism: British Sociology and the Evidence-Based State: A Reply to Payne et al.,” Sociology 39 ( July 2005), 519–528; Achenbaum, A. A., “When Good Research Goes Bad,” Marketing Research 13 (Winter 2001), 13–15; Wade, K. R., “We Have Come Upon the Enemy: And They Are Us,” Marketing Research 14 (Summer 2002), 39; Neill, James, “Qualitative versus Quantitative Research: Key Points in a Classic Debate,” http://wilderdom.com/research/ QualitativeVersusQuantitative Research.html, accessed February 6, 2009.

7 Babin, Barry J., William R. Darden, and Mitch Griffin, “Work and/ or Fun: Measuring Hedonic and Utilitarian Shopping Value,” Journal of Consumer Research 20 (March 1994), 644–656.

8 Stengal, J. R., A. L. Dixon, and C. T. Allen, “Listening Begins at Home,” Harvard Business Review (November 2003), 106–116.

9 Semon, Thomas T., “You Get What You Pay for: It May Be Bad MR,” Marketing News 36 (April 15, 2002), 7.

10 Thompson, Craig J., “Interpreting Consumers: A Hermeneutical Framework for Deriving Marketing Insights from the Tests of Consumers’ Consumption Stories,” Journal of Marketing Research 34 (November 1997), 438–455; Woodside, Arch G., H. M. Pattinson, and K. E. Miller, “Advancing Hermeneutic Research for Interpreting Interfirm New Product Development,” Journal of Business and Industrial Marketing 20 (2005) 364–379.

11 Thompson, Craig J., “Interpreting Consumers: A Hermeneutical Framework for Deriving Marketing Insights from the Tests of Consumers’ Consumption Stories,” Journal of Marketing Research 34 (November 1997), 438–455 (see pp. 443–444 for quotation).

12 While we refer to a hermeneutic unit as being text-based here for simplicity, they can actually also be developed

660 Endnotes

using pictures, videotapes, or artifacts as well. Software such as Atlas-TI will allow files containing pictures, videos, and text to be combined into a hermeneutic unit.

13 Morse, Janice M. and Lyn Richards (2002).

14 See Feldman, Stephen P., “Playing with the Pieces: Deconstruction and the Loss of Moral Culture,” Journal of Management Studies 35 ( January 1998), 60–80.

15 “Futurespeak,” American Demographics 26 (April 2004), 44.

16 Louella, Miles, “Living Their Lives,” Marketing (UK) (December 11, 2003), 27–28.

17 Reid, D. M., “Changes in Japan’s Post-Bubble Business Environment: Implications for Foreign-Affiliated Companies,” Journal of International Marketing 7, no. 3 (1999), 38–63.

18 Morse, Janice M. and Lyn Richards (2002).

19 Strauss, A. L. and J. Corbin, Basics of Qualitative Research (Newbury Park, CA: Sage Publications, 1990).

20 Glaser, B. G., and Strauss, A. L., The Discovery of Grounded Theory: Strategies for Qualitative Research. (New York: Aldine Publishing Company, 1967).

21 Geiger, S. and D. Turley, “Personal Selling as a Knowledge-Based Activity: Communities of Practice in the Sales Force,” Irish Journal of Management 26 (2005), 61–70.

22 Beverland, M., “The Components of Prestige Brands,” Journal of Business Research 59 (February 2006) 251–258; Beverland, M., “Brand Value, Convictions, Flexibility and New Zealand Wine,” Business Horizons 47 (September/October 2004), 53–61.

23 Harwood, Jonathan, “Philip Morris Develops Smokeless Cigarette,” Marketing Week 28 (March 31, 2005), 5.

24 Creamer, Mathew, “Slowly, Marketers Learn How to Let Go and Let Blog,” Advertising Age 76 (October 31, 2005), 1–35.

25 Fass, Allison, “Collective Opinion,” Forbes 176 (November 28, 2005), 76–79.

26 O’Loughlin, Sandra, “Real Women Have Lingerie,” Brandweek 46 (November 14, 2005), 22–24.

27 See Palan, K. M. and R. E. Wilkes, “Adolescent-Parent Interaction in Family Decision Making,” Journal of Consumer Research 24 (September 1997), 159–170; Haytko, Diana L. and Julie Baker, “It’s All at the Mall: Exploring Adolescent Girls’ Experiences,” Journal of Retailing 80 (Spring 2004), 67–83.

28 Godes, David and Dina Mayzlin “Using On-Line Conversations to Study Word-of-Mouth Communications,” Marketing Science 23 (2004), 545–560.

29 Babin, Barry J., William R. Darden and James S. Boles, “Salesperson Stereotypes, Consumer Emotions,

and Their Impact on Information Processing,” Journal of the Academy of Marketing Science 23 (Spring 1995), 94–105.

30 Murphy, Ian, “Aided by Research, Harley Goes Whole Hog,” Marketing News 30 (December 2, 1996), 16–17.

31 Philip Kotler, “Behavioral Models for Analyzing Buyers,” Journal of Marketing (October 1965), pp. 37–45.

32 Alsop, Ronald, “Advertisers Put Consumers on the Couch,” Wall Street Journal (May 13, 1998), 19.

33 Huxley, John, “Horrific, but Not the Worst We’ve Suffered.” Sydney Morning Herald (February 11, 2009).

Chapter 8 1 The North American Industry

Classification System (NAICS), http://www.census.gov/eos/www/ naics/.

2 “Breakfast Sandwich Boom,” Chain Leader (November 2005), http:// web2.infotrac.galegroup.com; Perlik, Allison,“Fast Starts,” Restaurants & Institutions ( January 15, 2006), http:// web2.infotrac.galegroup.com.

3 Grow, Brian, “Yes, Ma’am, That Part Is in Stock,” BusinessWeek (August 1, 2005), http://web2.infotrac .galegroup.com; and Servigistics, “Servigistics Pricing: Maximizing the Profitability of Your Service Network,” http://www.servigistics .com, accessed February 7, 2006.

4 “OTC Cosmetics & Toiletries in China 2010: A Market Analysis,” Business Wire (October 26, 2010), http://www.advfn.com/ news_Research-Markets-OTC- Cosmetics-Toiletries-in-China-2010- The-Total-Cosmetic_44945800.html, accessed May 12, 2011.

5 Charles, Susan K., “Custom Content Delivery,” Online Magazine (March– April 2004), http://galenet.galegroup .com; Fleming, Lee, “Digital Delivery: Pushing Content to the Desktop,” Digital Information Group ( January 31, 1997); “How Smart Agents Will Change Selling,” Forbes ASAP (August 28, 1995), 95.

6 https://www.cia.gov/library/ publications/the-world-factbook/ geos/ez.html, accessed April 2, 2011.

7 “Seeking New Beer Drinkers in the High Andes,” Global Agenda, (September 6, 2005), p. 1; “China Ranked Largest Beer Consumer in 2004,” Kyodo News International, (December 15, 2005), Knight Ridder Tribune Business News, p. 1; Switzer, J, “No Beer, Please – We’re Ozzies,” Maclean’s, 124 (February 7, 2011), 40.

8 “Seeking New Beer Drinkers in the High Andes,” Global Agenda ( July 20, 2005), http://galenet.galegroup .com; “China Ranked Largest Beer Consumer in 2004,” Kyodo News International (December 15, 2005), http://galenet.galegroup.com.

9 This section is based on Levy, Michael and Barton Weitz, Retail Management (Homewood, IL: Richard D. Irwin, 1992), 357–358.

10 Data from the “About Us” section of the Capital One website, http:// www.capitalone.com, accessed February 9, 2006.

11 Rao, Srikumar S., “Technology: The Hot Zone,” Forbes (November 18, 1996).

12 IBM Business Intelligence Data Mining Product Discovery, http:// www.ibm.com.

13 Wasserman, Todd, Gerry Khermouch and Jeff Green, “Mining Everyone’s Business,” Brandweek (February 28, 2000), 34.

14 Andriotis, AnnaMaria “Macy’s Catalogs Shoppers’ Habits,” Wall Street Journal (March 23, 2011), B5.”

15 Totty, Michael, “Making Searches Work at Work,” Wall Street Journal (December 19, 2005), http://online .wsj.com.

16 “Hispanic-Owned Businesses: Growth Projections, 2004–2010,” HispanicBusiness.com Store, http:// www.hbinc.com, accessed February 7, 2006.

17 Neff, Jack, “Wal-Mart Takes Stock in RetailLink System,” Advertising Age (May 21, 2001), 6.

18 See Federal Grants Wire, “National Trade Data Bank (NTDB),” http:// www.federalgrantswire.com, accessed February 6, 2006; and STATUSA, “What Information Is Available under GLOBUS and NTBD?” and “GLOBUS & NTDB,” http://www .stat-usa.gov, accessed February 6, 2006.

19 Based on Brown, Warren, “Pain at the Pump Doesn’t Faze New-Car Buyers,” Washington Post ( January 29, 2006), http://www.washingtonpost .com; Wells, Melanie, “Snowboarding Secrets,” Forbes (February 14, 2005), http://web5.infotrac.galegroup.com; Halliday, Jean, “Automakers Scrap SUVs, Tout Hybrids,” Advertising Age (September 26, 2005), http://web5 .infotrac.galegroup.com.

Chapter 9 1 “About In-Stat,” In-Stat, http://

www.instat.com/index.asp; Nissen, Keith, “In-Depth Analysis: The Media Phone Has Arrived!” In-Stat, http:// www.instat.com/promos/09/dl/ media_phone_3ufewaCr.pdf.

2 Vascellaro, Jessica E., “Who’ll Give Me $50 for This Purse from Nana?” Wall Street Journal (December 28, 2005), http://online.wsj.com; “Survey Reveals Majority of Americans Receive Unwanted Gifts,” Survey.com news release (December 19, 2005), http://www .survey.com.

3 Excerpts from Arlen, Michael J., Thirty Seconds (New York: Farrar, Straus and Giroux, Inc., 1979, 1980),

185–186. This material first appeared in the New Yorker.

4 However, the popularity of marketing research has affected the willingness of respondents to participate in surveys. People are increasingly refusing to participate.

5 Tuckel, Peter and Harry O’Neill, “The Vanishing Respondent in Telephone Surveys,” (paper presented at the 56th annual conference of the American Association of Public Opinion Research [AAPOR], Montreal, Canada, May 17–20, 2001).

6 Cull, William L., Karen G. O’Connor, Sanford Sharp, and Suk-fong S. Tang, “Response Rates and Response Bias for 50 Surveys of Pediatricians,” Health Services Research (February 2005), downloaded from http:// galenet.galegroup.com.

7 Lee, Eunkyu, Michael Y. Hu, and Rex S. Toh, “Respondent Noncooperation in Surveys and Diaries: An Analysis of Item Non-Response and Panel Attrition,” International Journal of Market Research (Autumn 2004), downloaded from http://web7 .infotrac.galegroup.com.

8 Douglas Aircraft, Consumer Research (undated), p. 13.

9 For an interesting study of extremity bias, see Baumgartner, Hans and Jan-Benedict E. M. Steenkamp, “Response Styles in Marketing Research: A Cross-National Investigation,” Journal of Marketing Research (May 2001), 143–156.

10 Turner, Charles F., Maria A. Villarroel, James R. Chromy, Elizabeth Eggleston, and Susan M. Rogers, “Same-Gender Sex among U.S. Adults: Trends across the Twentieth Century and during the 1990s,” Public Opinion Quarterly (Fall 2005), downloaded from http://web7 .infotrac.galegroup.com.

11 The term questionnaire technically refers only to mail and self- administered surveys, and the term interview schedule is used for interviews by telephone or face-to-face. However, we will use questionnaire to refer to all three forms of communications in this book.

12 Sobel, Bill, “Poll Reveals Men More Likely Than Women to Keep Their New Year’s Resolutions” (December 29, 2008), http://www.sobelmedia .com/2008/12/29/poll-reveals-men- more-likely-than-women-to-keep- their-new-years-resolutions, accessed March 30, 2009.

13 Ohlemacher, Stephen, “Study Finds That Marriage Builds Wealth,” Yahoo! News ( January 18, 2006), http:// news.yahoo.com; Charles Pierret, “The National Longitudinal Survey of Youth: 1979 Cohort at 25,” Monthly Labor Review (February 2005), 3–7.

14 Chang, H. H, “Critical Factors and Benefits in the Implementation of Customer Relationship Management,” Total Quality Management 18(5) (2007), 483–508.

Endnotes 661

15 Weisberg, Karen, “Change Maker,” Food Service Director ( January 15, 2006), downloaded from http:// web7.infotrac.galegroup.com.

16 Gavin, David A., “Competing on the Eight Dimensions of Quality,” Harvard Business Review (November– December 1987), 101–8.

17 Forelle, Charles, “Many Colleges Ignore New SAT Writing Test,” Wall Street Journal (December 7, 2005), http://online.wsj.com; “Kaplan’s New SAT Survey Results,” Kaplan Inc., College Admissions, Kaplan website, http://www.kaptest.com, accessed February 14, 2006.

Chapter 10 1 Warwick, Donald T. and Charles A.

Lininger, The Sample Survey: Theory and Practice (New York: McGraw-Hill, 1975), 2.

2 Lockley, L. C., “Notes on the History of Marketing Research,” Journal of Marketing (April 1950), 733.

3 Hof, Robert D., “The Power of Us,” BusinessWeek ( June 20, 2005), http:// web2.infotrac.galegroup.com.

4 For a complete discussion of conducting surveys in Hispanic neighborhoods, see Hernandes, Sigfredo A. and Carol J. Kaufman, “Marketing Research in Hispanic Barrios: A Guide to Survey Research,” Marketing Research (March 1990), 11–27.

5 Curtin, Richard, Stanley Presser, and Eleanor Singer, “Changes in Telephone Survey Nonresponse over the Past Quarter Century,” Public Opinion Quarterly (Spring 2005), http://web3.infotrac .galegroup.com.

6 Cuneo, Alice Z., “Researchers Flail as Public Cuts the Cord,” Advertising Age (November 15, 2004), http://web3 .infotrac.galegroup.com.

7 “Blumberg, Stephen J. and Julian V. Luke, “Wireless Substitution: Early Release of Estimates from the National Health Interview Survey, July–December 2010,” http://www .cdc.gov/nchs/data/nhis/earlyrelease/ wireless201106.htm, accessed July 9, 2011.

8 Hembroff, Larry A., Debra Rusz, Ann Rafferty, Harry McGee, and Nathaniel Ehrlich, “The Cost- Effectiveness of Alternative Advance Mailings in a Telephone Survey,” Public Opinion Quarterly (Summer 2005), http://web3.infotrac .galegroup.com.

9 Brennan, Mike, Susan Benson, and Zane Kearns, “The Effect of Introductions on Telephone Survey Participation Rates,” International Journal of Market Research 47, no. 1 (2005), 65–74.

10 Dillman, Don A., Mail and Internet Surveys: The Tailored Design Method (New York: John Wiley and Sons, 2000), 173.

11 Schaefer, David R. and Don A. Dillman, “Development of a Standard E-Mail Methodology: Results of an Experiment,” Public Opinion Quarterly 62, no. 3 (Fall 1998), 378.

12 Ibid. 13 For a complete discussion of fax

surveys, see the excellent article by Dickson, John P. and Douglas L. Maclachlan, “Fax Surveys: Return Patterns and Comparison with Mail Surveys,” Journal of Marketing Research (February 1996), 108–113.

14 Merriman, Joyce A., “Your Feedback Is Requested,” American Family Physician (October 1, 2005), http:// web3.infotrac.galegroup.com.

15 Dillmann, D. A. (2000), 369–372. 16 Göritz, Anja S., “Recruitment for

On-Line Access Panels,” International Journal of Market Research 46, no. 4, (2004), 411–425.

17 Fricker, Scott, Mirta Galesic, Roger Tourangeau, and Ting Yan, “An Experimental Comparison of Web and Telephone Surveys,” Public Opinion Quarterly (Fall 2005), http:// web3.infotrac.galegroup.com.

18 See Nielsen, Jakob, “Keep Online Surveys Short,” Alertbox (February 2, 2004), http://www.useit.com; “About Jakob Nielsen,” http://www .useit.com, accessed February 21, 2006; and Nielsen Norman Group, “About Nielsen Norman Group,” http://www.nngroup.com, accessed February 21, 2006.

19 See Kilbourne, Lawrene, “Avoid the Field of Dreams Fallacy,” Quirk’s Marketing Research Review ( January 2005), 70, 72–73.

20 Mary Lisbeth D’Amico, “Call Security,” Wall Street Journal (February 13, 2006), http://online .wsj.com.

21 For an interesting empirical study, see Akaah, Ishmael P. and Edward A. Riordan, “The Incidence of Unethical Practices in Marketing Research: An Empirical Investigation,” Journal of the Academy of Marketing Sciences (Spring 1990), 143–152.

22 Based on “Do-Not-Call List Reduces Telemarketing, Poll Finds,” Wall Street Journal ( January 12, 2006), http:// online.wsj.com.

Chapter 11 1 Four Seasons Hotel Chicago, http://

www.fourseasons.com/chicagofs/ dining.html; Mystery Shopping Providers Association, http://www .mysteryshop.org; Michelson, M., “Taking the Mystery Out of Mystery Shopping,” Mystery Shopping Providers Association, www.mspa-eu .org/about/MysteryShopping1.ppt.

2 Selltiz, Claire, Lawrence S. Wrightsman, and Stuart W. Cook, Research Methods in Social Relations (New York: Holt, Rinehart and Winston, 1976), 251.

3 Campbell, Angus, Philip E. Converse, and Willard L. Rodgers, The Quality of American Life (New York: Russell Sage Foundation, 1976), 112. Although weather conditions did not correlate with perceived quality of life, the comfort variable did show a relationship with the index of wellbeing. This association might be confounded by the fact that ventilation and/or air-conditioning equipment is less common in less affluent homes. Income was previously found to correlate with quality of life.

4 Abrams, Bill, The Observational Research Handbook (Chicago: NTC Business Books, 2000), 2, 105.

6 “Inside TV Ratings,” Nielsen Media Research, http://www.nielsenmedia .com, accessed February 21, 2009.

7 “The Portable People Meter System,” Arbitron, http://www.arbitron.com, accessed February 24, 2006.

8 “About the PreTesting Company” and “Television,” PreTesting Company, http://www.pretesting .com, accessed February 24, 2006.

9 “Accurate Web Site Visitor Measurement Crippled by Cookie Blocking and Deletion,” Jupiter Media news release, (March 14, 2005), http:// www.jupitermedia.com; See also Johnson, Steve, “Who’s in Charge of the Web Site Ratings Anyway?” Chicago Tribune (February 26, 2006), sec. 1, p. 18.

10 Kiley, David, “Google: Searching for an Edge in Ads,” BusinessWeek ( January 30, 2006), downloaded from http://web3.infotrac.galegroup. com; See also Sanders, Pieter and Bram Lebo, “Click Tracking: A Fool’s Paradise?” Brandweek ( June 6, 2005), http://web3.infotrac.galegroup.com.

11 Neff, Jack, “Aging Population Brushes Off Coloring,” Advertising Age ( July 25, 2005), downloaded from http:// web5.infotrac.galegroup.com.

12 Stringer, Kortney, “Eye-Tracking Technology for Marketers,” Detroit Free Press (August 1, 2005), downloaded from http://galenet.galegroup.com.

13 Herbert B. Krugman’s statement as quoted in “Live, Simultaneous Study of Stimulus, Response Is Physiological Measurement’s Great Virtue,” Marketing News (May 15, 1981), 1, 20.

14 Based on “Mazda Turns to Eye- Tracking to Assist Revamp of European Site,” New Media Age (November 3, 2005), downloaded from http://galenet.galegroup .com; and “Persuasion Is the New Focus,” Revolution (February 21, 2006), downloaded from the Media Coverage page of the Syzygy website, http://www.syzygy.co.uk.

15 Adapted with permission from Rayner, Bruce, “Product Development, Now Hear This!” Electronic Business (August 1997).

Chapter 12 1 Kohlhoff, C. and R. Steele,

“Evaluating SOAP for High Performance Business Applications: Real-Time Trading Systems.” Proceedings of WWW2003, May 20–24, 2003, Budapest, Hungary, accessed from http:// staff.it.uts.edu.au/~rsteele/ EvaluatingSOAP.pdf.

2 Based on McNatt, D. Brian and Timothy A. Judge, “Self-Efficacy Intervention, Job Attitudes, and Turnover: A Field Experiment with Employees in Role Transition,” Human Relations 61, no. 6 ( June 2008), 783–810.

3 Shadish, William R., Thomas D. Cook, and Donald T. Campbell, Experimental and Quasi Experimental Designs for Generalized Causal Inference (Geneva, IL: Houghton Mifflin, 2002).

4 Ellingstad, Vernon and Norman W. Heimstra, Methods in the Study of Human Behavior (Monterey, CA: Brooks/Cole, 1974).

5 Anderson, Barry F., The Psychological Experiment: An Introduction to the Scientific Method (Belmont, CA: Brooks/Cole, 1971), 28, 42–44.

6 Reitter, Robert N., “Comment: American Media and the Smoking- Related Behaviors of Asian Adolescents,” Journal of Advertising Research 43 (March 2003), 12–13.

7 Lach, Jennifer, “Up in Smoke,” American Demographics 22 (March 2000), 26.

8 Mitchell, Vincent-Wayne and Sarah Haggett, “Sun-Sign Astrology in Market Segmentation: An Empirical Investigation,” Journal of Consumer Marketing 14, no. 2 (1997), 113–131.

9 Roethlisberger, F. J. and W. J. Dickson, Management and the Worker (Harvard University Press: Cambridge, MA, 1939).

10 Shiv, Baba, Ziv Carmon, and Dan Aneley, “Placebo Effects of Marketing Actions: Consumers May Get What They Pay for,” Journal of Marketing Research 42 (November 2005), 383–393.

11 Tybout, Alice M. and Gerald Zaltman, “Ethics in Marketing Research: Their Practical Relevance,” Journal of Marketing Research 21 (November 1974), 357–368.

12 Peterson, Robert A., “On the Use of Students in Social Science Research: Evidence from a Second Order Meta Analysis,” Journal of Consumer Research 28 (December 2001), 450–461.

13 Shadish, William R., Thomas D. Cook, and Donald T. Campbell (2002).

662 Endnotes

Chapter 13 1 Babin, Barry J. and Jill Attaway,

“Atmospheric Affect as a Tool for Creating Value and Gaining Share of Customer,” Journal of Business Research 49 (August 2000), 91–99; Verhoef, P. C., “Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development,” Journal of Marketing 67 (October 2003), 30–45.

2 Periatt, J. A., S. A. LeMay, and S. Chakrabarty, “The Selling Orientation-Customer Orientation (SOCO) Scale: Cross-Validation of the Revised Version,” Journal of Personal Selling and Sales Management 24 (Winter 2004), 49–54.

3 Anderson, Barry F., The Psychology Experiment. (Monterey, CA: Brooks/ Cole, 1971), 26.

4 Kerlinger, Fred N., Foundations of Behavioral Research (New York: Holt, Rinehart and Winston, 1973).

5 Cohen, Jacob, “Things I Have Learned (So Far),” American Psychologist 45 (December 1990), 1304–1312.

6 Arnold, Catherine, “Satisfaction’s the Name of the Game,” Marketing News 38 (October 15, 2004), 39–45. Also, see http://www.theacsi.org.

7 In more advanced applications such as those involving structural equations analysis, a distinction can be made between reflective composites and formative indexes. See Hair, J. F., W. C. Black, B. J. Babin, R. Anderson, and R. Tatham, Multivariate Data Analysis, 6th ed. (Upper Saddle River, NJ: Prentice Hall, 2006).

8 Bart, Yakov, Venkatesh Shankar, Fareena Sultan, and Glen L. Urban, “Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large- Scale Exploratory Study,” Journal of Marketing 69 (October 2005), 133–152.

9 Cronbach, Lee J. and Richard J. Shavelson, “My Current Thoughts on Coefficient Alpha and Successor Procedures,” Educational and Psychological Measurement 64 ( June 2004), http://epm.sagepub.com/cgi/ content/short/64/3/391.

10 Hair et al. (2006). 11 Wells, Chris, “The War of the

Razors,” Esquire (February 1980), 3. 12 Babin, Barry J., William R. Darden,

and Mitch Griffin, “Work and/ or Fun: Measuring Hedonic and Utilitarian Shopping Value,” Journal of Consumer Research 20 (March 1994), 644–656.

13 Hair et al. (2006). 14 Cox, Keith K. and Ben M. Enis, The

Marketing Research Process (Pacific Palisades, CA: Goodyear, 1972); Kerlinger, Fred N., Foundations of Behavioral Research, 3rd ed. (Ft. Worth: Holt, Rinehart and Winston, 1986).

15 Headley, Dean E., Brent D. Bowen, and Jacqueline R. Liedtke. This case, originally titled “Navigating through Airline Quality,” was reviewed and accepted for publication by the Society for Case Research.

Chapter 14 1 Anhalt, Karen Nickel, “Whiskas

Campaign Recruits a Tiny Tiger,” Advertising Age International (October 19, 1998), 41.

2 Breeden, Richard, “Owners, Executives Cite Small Firms’ Advantages,” Wall Street Journal ( January 3, 2006), http://online .wsj.com; “SMB State of the Union Study,” AllBusiness.com (Winter 2005), news and press page, http:// www.allbusiness.com/press/ barometer.pdf.

3 Likert, Rensis, “A Technique for the Measurement of Attitudes,” Archives of Psychology 19 (1931), 44–53.

4 Osgood, Charles, George Suci, and Percy Tannenbaum, The Measurement of Meaning (Urbana: University of Illinois Press, 1957). Seven-point scales were used in the original work; however, subsequent researchers have modified the scale to have five points, nine points, and so on.

5 Menezes, Dennis and Norbert F. Elbert, “Alternative Semantic Scaling Formats for Measuring Store Image: An Evaluation,” Journal of Marketing Research (February 1979), 80–87.

6 Costanzo, Chris, “How Consumer Research Drives Web Site Design,” American Banker (April 19, 2005), http://galenet.galegroup.com.

7 “Technology Still Matters to Start- Ups Say Venture Capitalists and Other Industry Influencers,” Roeder- Johnson Corp. news release ( January 24, 2006), http://finance.yahoo.com; “Importance of Unique Technology to Start-Up Companies: A Survey,” Roeder-Johnson Corp. ( January 2006), http://www.roederjohnson.com.

Chapter 15 1 White, Joseph B., “The Price of

Safety,” Wall Street Journal (December 5, 2005), http://online.wsj.com; “J.D. Power and Associates Reports: Premium Surround Sound Systems and HD Radio Garner High Consumer Interest Based on Their Market Price, while Consumers Prefer One-Time Fee over the Monthly Fee Associated with Satellite Radio,” J.D. Power and Associates news release (August 18, 2005), http://www.jdpower.com.

2 Smith, Robert, David Olah, Bruce Hansen, and Dan Cumbo, “The Effect of Quesionnaire Length on Participant Response Rate: A Case Study in the U.S. Cabinet Industry,” Forest Products Journal

(November–December 2003), http:// galenet.galegroup.com.

3 “Insurers Question Methods in U.S. Treasury Survey on Terror Backstop,” A. M. Best Newswire (April 12, 2005), http://galenet.galegroup.com.

4 “Mothers Misunderstand Questions on Feeding Questionnaire,” medical letter on the CDC and FDA (September 5, 2004), http://galenet .galegroup.com.

5 Donahue, Amy K. and Joanne M. Miller, “Citizen Preferences and Paying for Police,” Journal of Urban Affairs 27, no. 4 (2005): 419–35.

6 Weber, Nathan, “Research: A Survey Shows How Media Influence Our Decorating and Cooking Choices,” HFN, the Weekly Newspaper for the Home Furnishing Network (December 5, 2005), http://galenet .galegroup.com.

7 Payne, Stanley L., The Art of Asking Questions (Princeton, NJ: Princeton University Press, 1951), 185. The reader who wants a more detailed account of question wording is referred to this classic book on that topic.

8 Roll, Charles W., Jr. and Albert H. Cantril, Polls: Their Use and Misuse in Politics (New York: Basic Books, 1972), 106–7.

9 “Hilarious Republican Senate Leadership Survey,” The Misanthropic Principle: The Blog of a Bipolar Misanthrope, http:// misanthropicscott.wordpress .com/2008/04/19/hilarious- republican-senate-leadership-survey/, accessed March 9, 2009.

10 Payne, Stanley L. (1951), 102–3. 11 Dillman, Don A., Mail and Internet

Surveys: The Tailored Design Method (New York: John Wiley and Sons, 2000), 357–61.

12 Young, Sarah J. and Craig M. Ross, “Web Questionnaires: A Glimpse of Survey Research in the Future,” Parks & Recreation 35, no. 6 ( June 2000), 30.

13 Michel, Matt “Controversy Redux,” CASRO Journal, http://www .decisionanalyst.com/publ_art/ contredux.htm, accessed February 8, 2001.

14 Ghaleb Almekhlafi, Abdurrahman, “Preservice Teachers’ Attitudes and Perceptions of the Utility of Web- Based Instruction in the United Arab Emirates,” International Journal of Instructional Media 32, no. 3 (2005): 269–84.

15 Harzing, Anne-Wil, “Does the Use of English-Language Questionnaires in Cross-National Research Obscure National Differences?” International Journal of Cross Cultural Management 5, no. 2 (2005): 213–24.

16 Cateora, Philip R., International Marketing (Homewood, IL: Richard D. Irwin, 1990), 387–89.

17 “Hospitals, Feds Design Survey to Identify Culture That Encourages

Patient Safety,” Health Care Strategic Management (February 2005), http:// galenet.galegroup.com; “Hospital Survey on Patient Safety Culture,” Agency for Healthcare Research and Quality, http://www.ahrq.gov/qual/ hospculture, accessed March 7, 2006.

Chapter 16 1 “Rheault, Magali, “Lack of

Money Tops List of Americans’ Financial Worries” ( July 22, 2011), http://www.gallup.com/ poll/148625/lack-money-tops-list- americans-financial-worries.aspx, accessed August 1, 2011.

2 Kinne, Susan and Tari D. Topolski, “Inclusion of People with Disabilities in Telephone Health Surveillance Surveys,” American Journal of Public Health 95, no. 3 (March 2005): 512–517.

3 Brock, Sabra E., “Marketing Research in Asia: Problems, Opportunities, and Lessons,” Marketing Research (September 1989), 47.

4 Yeganeh, Hamid, Zhan Su, Elie Virgile, and M. Chrysostome, “A Critical Review of Epistemological and Methodological Issues in Cross-Cultural Research,” Journal of Comparative International Management (December 2004), http://web2 .infotrac.galegroup.com.

5 Sigenman, Lee, Steven A. Tuch, and Jack K. Martin, “What’s in a Name? Preference for ‘Black’ versus ‘African- American’ among Americans of African Descent,” Public Opinion Quarterly (Fall 2005), http://web2 .infotrac.galegroup.com.

6 Rideout, Bruce E., Katherine Hushen, Dawn McGinty, Stephanie Perkins, and Jennifer Tate, “Endorsement of the New Ecological Paradigm in Systematic and E-Mail Samples of College Students,” Journal of Environmental Education (Winter 2005), http://web2.infotrac .galegroup.com.

7 SurveySite, “What We Do: Quantitative Research,” http://www.surveysite.com, accessed March 15, 2006.

8 “Frequently Asked Questions about Conducting Online Research: New Methodologies for Traditional Techniques,” Council of American Survey Research Organizations (CASRO) (1998), http://www .casro.org.

9 “Harris Poll Online Panel,” http:// www.harrisinteractive.com/ MethodsTools/DataCollection/ HarrisPollOnlinePanel.aspx, accessed August 1, 2011.

10 Ibid. 11 “Frequently Asked Questions about

Conducting Online Research” (1998).

12 “Internet Sampling Solutions,” Survey Sampling International, http://www.ssisamples.com, accessed March 15, 2006.

Endnotes 663

13 Based on Gene Mueller, “It’s Hard to Figure Number of Anglers,” Washington Times (March 20, 2005), http://web3.infotrac.galegroup .com; Atlantic Coastal Cooperative Statistics Program, “About Us: Committees,” http://www.accsp.org, accessed March 16, 2006; Atlantic States Marine Fisheries Commission, “About Us,” http://www.asmfc.org, accessed March 16, 2006.

14 Material for this case is from Scientific Telephone Samples User’s Manual, Scientific Telephone Samples, Santa Ana, CA.

Chapter 17 1 Based on Gerdes, Geoffrey R.,

Jack K. Walton II, May X. Liu, Darrel W. Parke, and Namirembe Mukasa,“Trends in the Use of Payment Instruments in the United States,” Federal Reserve Bulletin (Spring 2005), http://web2.infotrac .galegroup.com.

2 Most of the statistical material in this book assumes that the population parameters are unknown, which is the typical situation in most applied research projects.

3 The reasons for this are related to the concept of degrees of freedom, which will be explained later. At this point, disregard the intuitive notion of division by n, because it produces a biased estimate of the population variance.

4 In practice, most survey researchers will not use this exact formula. A modification of the formula, Z 5 (X 2 )/S, using the sample standard deviation in an adjusted form, is frequently used.

5 Hayes, William L., Statistics (New York: Holt, Rinehart and Winston, 1963), 193.

6 Wonnacott, Thomas H. and Ronald J. Wonnacott, Introductory Statistics, 2nd ed. (New York: Wiley, 1972), 125.

7 Note that the derivation of this formula is (1) E 5 ZSX; (2) E 5 ZS/1n; (3)1n ZS/E; (4) (n) 5 (ZS/E)2.

8 Based on Bialik, Carl, “A Survey Probes the Back Seats of Taxis, with Dubious Results,” Wall Street Journal ( January 28, 2005), http://online. wsj.com; “Taxis Hailed as Black Hole for Lost Cell Phones and PDAs, as Confidential Data Gets Taken for a Ride,” Pointsec Mobile Technologies news release ( January 24, 2005), http://www.pointsec.com.

Chapter 18 1 Askia, http://www.askia.com,

accessed April 4, 2009. 2 Sauerbeck, Laura R., Jane C. Khoury,

Daniel Woo, Brett M. Kissela, Charles J. Moomaw, and Joseph P. Broderick, “Smoking Cessation after Stroke: Education and Its Effect on Behavior,” Journal of Neuroscience

Nursing (December 2005), downloaded from http://web1 .infotrac.galegroup.com.

3 This section relies heavily on Interviewer’s Manual, rev. ed. (Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan, 1976).

4 Ibid., p. 11. 5 Ibid., pp. 11–13. Reprinted by

permission. 6 Oliver, Daniel G., Julianne

M. Serovich, and Tina L. Mason, “Constraints and Opportunities with Interview Transcription: Towards Reflection in Qualitative Research,” Social Forces (December 2005), downloaded from http://web1 .infotrac.galegroup.com.

7 Viewpoint Learning, http://www .viewpointlearning.com, accessed June 22, 2009.

8 Ripley, Birch G. “Confessions of an Industrial Marketing Research Executive Interviewer,” Marketing News (September 10, 1976), 20.

9 Eng, Susanna and Gardner, Susan, “Conducting Surveys on a Shoestring Budget,” American Libraries, 36 (February 2005), 38-39.

Chapter 19 1 http://www.sensorylogic.com,

accessed August 4, 2011. 2 Braunsberger, Karin, B. R. Buckler,

and David J. Ortinau, “Categorizing Cognitive Responses: An Empirical Investigation of the Cognitive Intent Congruency Between Independent Raters and Original Subject Raters,” Journal of the Academy of Marketing Science 33 (Fall 2005), 620–632.

3 These imputation methods are beyond the scope of this text. For more see Hair et al., Multivariate Data Analysis (Upper Saddle River, NJ: Prentice Hall, 2006), 39–73, 709–740.

Chapter 20 1 Dolliver, Mark, “Plow Under Your

Hops and Plant Some Vines,” Adweek 46 ( July 25, 2005), 36–38.

2 Fisher, Mark, “Beer Surges in Popularity—At the Expense of Wine,” Dayton Daily News (August 1, 2008), http://www.encyclopedia .com/doc/1P2-16950875.html, accessed March 22, 2009.

3 Kirsche, M. L., “Targeting Boomers Could Boost Fizzling Out Beer Sales,” Drug Store News 27 ( June 6 2005), 81.

4 Longo, Don, “Drink Up,” Progressive Grocer 84 (October 15 2005), 52–58.

5 “Pets Really Are Members of the Family,” http://www.harrisinteractive .com/vault/HI-Harris-Poll-

Pet-Ownership-2011-06-10.pdf, accessed August 4, 2011.

6 Rasmussen Reports, National Survey of 1,000 Adults (March 17–18 2009), http://www.rasmussenreports.com/ premium_content/econ_crosstabs/ march_2009/crosstabs_aig_ march_17_18_2009, accessed March 22, 2009.

7 See Dubinsky, Alan J., Rajan Nataraajan, and Wen-Yeh Huang, “Consumers’ Moral Philosophies: Identifying the Idealist and the Relativist,” Journal of Business Research 58 (December 2005), 1690–1701; Deal, Ken, “Deeper into the Trees,” Marketing Research 17 (Summer 2005), 38–40.

8 Adapted from Yavas, Ugur and Emin Babakus, “What Do Guests Look for in a Hotel? A Multi-Attribute Approach,” Services Marketing Quarterly 25, no. 2 (2003), 6–14.

9 http://www.wineinstitute.org/ communications/statistics, accessed February 6, 2006.

10 The data analysis tool must be added to the conventional Excel install by unpacking the data tool. This can be done by clicking on tools and then clicking on add-ins and following the instructions. See http://www .microsoft.com for more instructions on how to accomplish this.

11 Iuso, Bill, “Concept Testing: An Appropriate Approach,” Journal of Marketing Research 12 (May 1975), 230.

12 Diamon, Sidney, “Market Research Latest Target in Ad Claim,” Advertising Age ( January 25, 1982), 52. Reprinted with permission by Crain Communications, Inc.

13 Adapted with permission from Prince, Melvin, Consumer Research for Management Decisions (New York: John Wiley and Sons, 1982), 163–166.

Chapter 21 1 Technically, the t-distribution

should be used when the population variance is unknown and the standard deviation is estimated from sample data. However, with large samples, the t-distribution approximates the Z-distribution, so the two will generally yield the same result.

2 See a comprehensive statistics text for a more detailed explanation.

3 A more complex discussion of the differences between parametric and nonparametric statistics appears in Appendix 22A.

4 Gross, Daniel, “Life in the Fast Lane: Ferrari Sales Surge in the U.S.,” Yahoo! Finance (August 2, 2011), http://finance.yahoo.com/blogs/ daniel-gross/life-fast-lane-ferrari- sales-surge-u-155253417.html, accessed August 10, 2011.

5 In most cases, low p-values support hypotheses. However, if the hypothesis is that the observations

will be equal to the theoretical expectations for a given distribution (this would be the null case), then a high p-value would be desired to support the hypothesis. Generally, this is not good form for a hypothesis. Exceptions to this rule exist. One of the most common is when a researcher compares some matrix of values with some alternative matrix of values with a goodness-of-fit test. Particularly in advanced applications (beyond the scope of this book), the researcher may wish to test whether or not the two matrices are the same within sampling error. In this case, the researcher would need an insignificant p-value (above ) to support the hypothesis.

Chapter 22 1 Vermeir, I. and P. Van Kenhove,

“Gender Differences in Double Standards,” Journal of Business Ethics 81 (2008), 281–295.

2 Vermeir and Van Kenhove (2008). 3 Tests for complex experimental

designs are covered in Appendix 22B. 4 The formula is not shown here but it

can be found in most basic statistics books.

5 See, for example, Armstrong-Stassen, M., “Designated Redundant but Escaping Lay-Off: A Special Group of Lay-Off Survivors,” Journal of Occupational and Organizational Psychology 75 (March 2002), 1–13.

6 This is the “statistical alternative” hypothesis.

7 Sukhdial, Ajay, Damon Aiken, and Lynn Kahle, “Are You Old School? A Scale for Measuring Sports Fans’ Old-School Orientation,” Journal of Advertising Research 42 ( July/August 2002), 71–81.

Chapter 23 1 Greenhaus, J. H. and N. J. Beutell,

“Sources of Conflict Between Work and Family Roles,” Academy of Management Review, 10, no. 1 (1985), 76.

2 Boyar, S. L., C. P. Maertz Jr., A. W. Pearson, and S. Keough, “Work- Family Conflict: A Model of Linkages Between Work and Family Domain Variables and Turnover Intentions,” Journal of Managerial Issues 15, no. 2 (2003), 175.

3 Beutell, N. J. and U. Wittig-Berman, “Predictors of Work-Family Conflict and Satisfaction with Family, Job, Career, and Life,” Psychological Reports 85 (1999), 893–903.

4 For a discussion of the other measures of association, see the appendix to this chapter and J. D. Gibbons, Nonparametric Methods for Quantitative Analysis (New York: Holt, Rinehart and Winston, 1976).

5 Bott, J. P., D. J. Svyantek, S. A. Goodman, and D. S. Bernal,

664 Endnotes

“Expanding the Performance Domain: Who Says Nice Guys Finish Last?” International Journal of Organizational Analysis, 11, no. 2 (2003), 137–152.

6 Bagozzi, R. P., “Salesforce Performance and Satisfaction as a Function of Individual Difference, Interpersonal and Situational Factors,” Journal of Marketing Research (November 1978), 517–531.

7 Recall that the mean for a standardized variable is equal to 0.

8 For more on this topic, see Hair, J. F., W. C. Black, B. J. Babin, R. Tathum, and R. Anderson, Multivariate Data Analysis, 6th ed. (Upper Saddle River, NJ: Prentice Hall, 2006).

Chapter 24 1 Goulding, Christina, “Romancing

the Past: Heritage Visitors and the Nostalgic Consumer,” Psychology and Marketing 18 ( June 2001), 565–592.

2 Tesoriero, H. W., “Babes in 80s Toyland,” Time 160 (November 11, 2002), 14.

3 “Nostalgia, Education Hot Trends in Toys,” Mass Market Retailers 21 (February 23, 2004), 47, Thomson- Gale Database.

4 Betts, Kate, “A 1950s State of Mind,” Time (April 15, 2004), 4.

5 Osborn, Suzanne Barry, “It’s Yesterday Once More: Companies Use Nostalgia to Entice Consumers,” Chain Store Age ( June 2001), 32.

6 Peterson, Karyn M., “Entertaining the Future: Licensing Execs on Last

Year’s Lessons and the Challenge of What’s Next,” Playthings (February 1, 2009), http://www.playthings.com/ article/CA6635647.html, accessed April 20, 2009.

7 See Holak, S. L. and W. Havlena, “Feelings, Fun and Memories: An Examination of the Emotional Components of Nostalgia,” Journal of Business Research 42, no. 3 (1998), 217–226.

8 Muehling, Darrel D. and David E. Sprott, “The Power of Reflection,” Journal of Advertising 33 (Fall 2004), 25–35.

9 Holak, S. L. and W. Havlena (1998). 10 When the actual regression model

is illustrated as an explanation of the actual dependent variable in a population, Y

i is used and an

error term (e i ) is included because

the sample parameters cannot be expected to perfectly predict and explain the actual value of the dependent variable in the population. When we use a regression equation to represent its ability to predict sample values of the dependent variable from the estimated parameter coefficients, Ŷ

i is used to represent

predicted values of Y i and no error

term is included since the actual amount of error in any given observation is unknown.

11 School enrollment statistics can often be found using the Internet and either searching through government statistics or examining the website for the local school district or school board.

12 The constant term has disappeared since it is equal to 0 when the regression coefficients are standardized.

13 For more on this topic, see Hair, J. F., W. C. Black, B. J. Babin, and R. Anderson, Multivariate Data Analysis (Upper Saddle River, NJ: Prentice Hall, 2010).

14 Cox, A. D., D. Cox, and R. D. Anderson, “Reassessing the Pleasures of Store Shopping,” Journal of Business Research 58 (March 2005), 250–259.

15 Closs, D. J., M. Swink, and A. Nair, “The Role of Information Connectivity in Making Flexible Logistics Programs Successful,” International Journal of Physical Distribution & Logistics Management 35, no. 4 (2005), 258–277.

16 Morrison, Mark, A. Sweeney, and T. Heffernan, “Learning Styles of On-Campus and Off-Campus Marketing Students: The Challenge for Marketing Educators,” Journal of Marketing Education 25 (December 2003), 208–217.

17 Zachary Paul Neal, “Culinary Deserts, Gastronomic Oases: A Classification of U.S. Cities,” (2006), Urban Studies, 43(1): 1–21.

Chapter 25 1 North, Tim, “Business Report

Writing Tips,” http://www .betterwritingskills.com, downloaded April 28, 2009.

2 The original version of this chapter was written by John Bush, Oklahoma State University, and appeared in William G. Zikmund, Business Research Methods (Hinsdale, IL: Dryden Press, 1984).

3 “A Speech Tip,” Communication Briefings 14, no. 2 (1995), 3.

4 These guidelines, adapted with permission from Marjorie Brody (President, Brody Communications, 1200 Melrose Ave., Melrose Park, PA 19126), appeared in “How to Gesture when Speaking,” Communication Briefings 14, no. 11 (1995), 4.

5 “Tips of the Month,” Communication Briefings 24, no. 7 (May 2005), 1.

6 Based on Bridis, Ted, “Study: Shoppers Naïve about Online Pricing,” Information Week ( June 1, 2005), downloaded from http:// web2.infotrac.galegroup .com; (APPC),”Annenberg Study Shows Americans Vulnerable to Exploitation in the Online and Offline Marketplace,” Annenberg Public Policy Center news release ( June 1, 2005), http://www .annenbergpublicpolicycenter .org; Turow, Joseph, Lauren Feldman, and Kimberly Meltzer, “Open to Exploitation: American Shoppers Online and Offline,” APPC report, June 2005, downloaded from http://www .annenbergpublicpolicycenter.org.

Endnotes 665

666

Page numbers followed by f indicate figures.

A Absolute causality, 56 Abstract level, 40 Acquiescence bias, 192 Administrative error, 193 Advocacy research, 99 Analysis of variance (ANOVA),

542–547, 543f, 544f, 545f, 591–594

for complex experimental designs, 556–559

ANOVA. See Analysis of variance

Applied business research, 5–6 Aristotle, 492 Attitude, 311 Attitude measurement, 310–332

behavioral attention, 323–324

as hypothetical constructs, 311–312

measurement scale selection, 326–329

ranking, 324–325 ranking scales, 313–322, 323f techniques for, 312–313

Attribute, 299

B Back translation, 361 Backward linkage, 59 Balanced rating scale, 327 Basic business research, 5–6 Basic data analysis, 483–505

computer programs, 497–499, 498f

cross-tabulation, 486–491

data transformation, 491–494, 492f

descriptive analysis, 484–485, 485f

graphic data methods, 496–497, 496f

interpretation, 500 rank order, 494–496, 495f tabular data methods,

496–497, 496f tabulation, 486

Basic experimental design, 274 Behavioral differential, 324 Berra, Yogi, 529 Between-groups variance, 545 Between-subjects design, 270 Bierce, Ambrose, 615 Bivariate statistical analysis, 507,

528–559, 530f, 560–581 analysis of variance, 542–547,

543f, 544f, 545f cross-tabulation tables,

531–533 f-test, 546–547 measure of association, 561 paired samples t-test, 539 regression analysis, 566–577, 570f,

574f, 575f simple correlation coefficient,

561–566, 562f, 564f, 565f test of difference, 529–530 t-test, 534–538, 537f z-test, 540–541

Blake, William, 258 Blocking variables, 255 Box and whisker plots, 498 Briefing session, 443 Buffett, Warren, 81 Business ethics, 88 Business intelligence, 17 Business opportunity, 49 Business problems, 49

Business research, definition of, 5 Business research, human side of,

73–104 business research jobs

director of research, 79 large firm, 78f, 79 midsize firms, 77–78 small firms, 77

client sponsor, rights of open relationship, 99 privacy, 100

conflict sources criticism, 80 intuitive decision making,

81–82 money, 80 past experience, 81–82 reduction of, 82–83, 84f time, 80–81

cross-functional teams, 83–84 custom research, 86 descriptive research, 92 ethical issues in, 87–101 general rights, 88–89 organizational structure, 76–85 researcher, rights of

confidentiality, 98 honesty in errors, 98 honesty in results, 96–97, 97f mixing sales, 93–94 pseudo-research, 94–95 push polls, 95–96 service monitoring, 96

research participant, rights of active research, 89–90 experimental designs, 91 passive research, 90–91 protection from harm, 92–93

research suppliers syndicated service, 85

standardized research service, 85

Business research, role of, 2–18, 15f applied business research, 5–6 basic business research, 5–6 benefits vs. costs, 11–12, 12f communication technologies,

12–13 course of action

evaluation, 9–10 evaluation research, 9 performance-monitoring

research, 9 selection, 8–9

data availability, 10–11, 12f decision, nature of, 11, 12f definition of, 5 global business research, 13–14 managerial value of, 7–9 nature of, 4–7 need for, 10–12 opportunities identification, 8 problems identification, 8 scientific method, 6–7, 6f in the twenty-first century, 12–14 time constraints, 10, 12f

Business research process, 48–72 alternatives to, 60 ambiguity, 50–51 casual research, 54–58 certainty, 49 data analysis

coding, 68 editing, 68

data gathering, 67 decision linking, 64 decision making, 49, 51f explanatory research, 62 managerial decision situation, 62 pilot studies, 63 previous research, 62–63 reports, 68 research design, 64–66

“best,” 65–66

INDEX

research method, 65 research objectives, 60–64, 61f,

63–64 research program strategy, 68–69 sampling, 66–67 stages in, 59–68, 60f types of

descriptive research, 53–54 explanatory research, 52–53 uncertainty and, 58–59, 58f

uncertainty, 50

C Callbacks, 211 Carroll, Lewis, 345 Case studies, 139–140 Categorical variable, 119 Category scales, 314–315 CATI. See Computer-assisted

telephone interviewing Causal inference, 55 Causal research

causality, 55 concomitant variation, 55 degrees of, 56–57 experiments, 57–58, 57f nonspurious association,

55–56, 56f temporal sequence, 55

Cell, 259 Census, 385 Central-limit theorem, 424–426,

424f, 425f, 426f Central location interviewing, 215 Checkboxes, 356 Checklist question, 339 Chi-square test, 521–524 Choice, 313 Click-through rate, 245 Cluster analysis, 599–601, 599f, 600f Cluster sampling, 398 Code book, 475 Coding, 458–482

code book, 475 code construction, 469–470 coding qualitative responses,

465–468, 466f, 467f coding scheme, 472–475 computerized survey data

processing, 476 data file, 468–469 definition of, 465 dummy coding, 466 error checking, 476 open-ended questions, 471–472,

473f–475f precoding fixed-alternative

questions, 470–471, 470f Coefficient alpha, 302 Coefficient of determination, 564 Cohort effect, 272 Collages, 152 Communication process, 611 Comparative rating scale, 327 Completely randomized design,

278–282

Composite measures, 300, 300f Composite scale, 316–317 Comprehensive cases, 636–644 Computer-assisted telephone

interviewing (CATI), 216 Concept, definition of, 292 Conclusion section, 619 Concomitant variation, 55 Conditional causality, 56 Confidence interval estimate, 427 Confidence level, 427 Confidentiality, 89 Conflict of interest, 98 Confound, 262 Constancy of conditions, 266 Constant, 118 Constant-sum scale, 320 Construct, 293 Construct validity, 304 Consumer panel, 197 Content analysis, 243 Content providers, 31 Content validity, 304 Contingency table, 487 Continuous variable, 119 Continuous measures, 299 Contributory causality, 57 Contrived observation, 241 Control group, 258 Convenience sampling, 392–393 Convergent validity, 305 Cookies, 33 Correlation coefficient, 561 Correlation matrix, 565–566 Correspondence rules, 293 Cosby, Bill, 121 Counterbalancing, 266 Counterbiasing statement, 343 Covariance, 561 Cover letter, 220, 221f Criterion validity, 304 Critical values, 512 CRM. See Customer relationship

management Cross-checks, 162 Cross-functional teams, 84–85 Cross-sectional study, 195–197 Cross-tabulation, 486–491, 487f Cross-validation research, 14 Curbstoning, 453 Customer discovery, 169 Customer relationship management

(CRM), 23 Custom research, 86

D Data, definition of, 17 Data analysis, 68 Data analysis, stages of, 459–460, 459f Database marketing, 170 Databases, 24 Data conversion, 161 Data entry, 476 Data file, 467 Data integrity, 460 Data mining, 168–170

Data-processing error, 193 Data quality, 20 Data reduction technique, 596 Data transformation, 491–494, 492f Data warehousing, 24 Data wholesalers, 27 Debriefing, 91 Decision statement, 107 Decision support systems (DSS),

23–24, 23f, 25f Deductive reasoning, 43 Degrees of freedom, 517 Deliverables, 60 Demand characteristics, 263–266 Demand effect, 263–267 Dependence techniques, 584 Dependent variable, 119 Depth interviews, 149–150, 151f Descriptive analysis, 484–485, 485f Descriptive research, 53–54 Descriptive statistics, 410 Determination-choice question, 339 Diagnostic analysis, 54 Dialog boxes, 227 Direct observation, 239 Discrete measures, 298 Discriminant analysis, 592–594 Discriminant validity, 305 Discussion guide, 145 Disguised question, 194 Disproportional sampling, 397–398 Do-not-call legislation, 90 Door-in-the-face compliance

technique, 445 Door-to-door interviews, 210 Double-barreled questions, 344–345 Drop-down box, 356 Drop-off method, 223 Drucker, Peter, 138 DSS. See Decision support systems Dummy coding, 466 Dummy tables, 127 Dummy variable, 587

E Editing, 458–482

coding process facilitation, 464 for completeness, 463–464 field editing, 461 in-house editing, 461 pitfalls of, 465

Einstein, Albert, 41, 395 Elaboration analysis, 490 Electronic data interchange, 29 E-mail survey, 224 Emerson, Ralph Waldo, 611 Empirical level, 40 Empirical testing, 41–42, 42f Environmental scanning, 32 Error trapping, 359 Ethical dilemma, 88 Ethnography, 138 Evaluation research, 9 Experimental condition, 255 Experimental group, 258 Experimental research, 253–286, 256f

blocking variables, 255 classification of

alternative designs, 276–278, 277f complex experimental design,

278–282 time series design, 278, 279f

control, 266 demand characteristics, 263–266 designing of

dependent variable, 260 independent variable, 257–260 test units, 260–263

ethical issues in, 267 experimental subjects, 255 external validity, 273–274 Hawthorne effect, 264 interaction effect, 257 main effect, 256–257 practical design issues

field experiments, 268–270, 269f

laboratory experiments, 267–268

subjects, 255 validity issues, 271–274

Experimental treatment, 258 Experimental variable, 57 Explanatory research, 52–53 External data, 171 External validity, 273 Extraneous variables, 262–263 Extremity bias, 192 Eye-tracking monitor, 247

F Face validity, 303 Factor analysis, 595–599, 596f Factorial design, 280–282, 281f Factor loading, 596 Factor rotation, 596 Fax surveys, 223–224 Field editing, 461 Field experiments, 268–270, 269f Field interviewing service, 442 Fieldwork, 441–456, 442

good interviewing principles basics, 449–450 required practices, 450

in-house training initial contact, 443–444 probing, 446–447 question asking, 445–446 response recording, 447–449 telephone interviews, 444

management of, 451–452 nature of, 442 supervision for, 452–454 team for, 442–443

Filter question, 348 Fixed-alternative questions, 336, 338 Focus blog, 147 Focus group, 63 Focus group interview, 142, 146f Foot-in-the-door compliance

technique, 445 Forced answering software, 359

Index 667

Forced-choice rating scale, 328 Forecast analyst, 78 Forward linkage, 59 Free-association techniques,

151–153 Frequency-determination

question, 339 Frequency distribution,

411–412, 411f Frugging, 93–94 F-statistic, manual calculation of,

553–555 F-test, 546–547 Funded business research, 126 Funnel technique, 347

G General linear model (GLM), 585 GLM. See General linear model Global information systems, 22 Global research firm, 87f Goethe, Johann Wolfgang, 258 Goodness-of-fit, 521–524 Grand mean, 545 Graphic aids, 620 Graphic rating scales, 321–322, 321f Grounded theory, 139 Gudder, S., 583

H Happer, Marion, 74 Hawthorne effect, 264 Hermeneutics, 137 Hermeneutic unit, 137 Hidden observation, 237 Histogram, 485 History effect, 271–272 Human subjects review

committee, 93 Hypothesis, 41, 42f Hypothesis test of a

proportion, 524 Hypothetical constructs, 311–312

I Idealism, 88 Importance-performance analysis,

491, 491f Impute, 463 Independent sample t-test, 534 Independent variable, 119 Index numbers, 494 Index of retail saturation, 168 Inductive reasoning, 44 Inferential statistics, 410 Information, definition of, 17 Information completeness, 20–21 Information systems, 17–36

behavioral tracking, 26 business intelligence, 18 computerized data archives,

27, 27f data, 18 databases, 24

data warehousing, 24 decision support system, 23–24,

23f, 25f electronic data interchange, 29 external distributors, 26–27, 28f financial databases, 28 global information system, 22 information, 18 input management, 24 internal records, 24 Internet and

accessing data, 30 collecting data, 30–31 definition of, 29 hosting, 30 information technology, 32–33 interactive media, 31–32 Internet2, 33 Intranet, 33 navigating of, 31 smart agent software, 33 usability and, 30–31

knowledge management, 21–22 networks, 29 outside vendors, 26–27, 28f proprietary business research, 24 salesperson input, 25 scanner data, 26 statistical databases, 27–28 valuable information

completeness, 20–21 quality, 20 relevance, 19–20 timeliness, 20

video databases, 29 Informed consent, 89 In-house editing, 461 In-house research, 74 Instrumentation effect, 272 Interaction effect, 257 Interactive help desk, 359 Interactive medium, 31 Interdependence techniques, 584 Internal and proprietary data,

170–171 Internal consistency, 302 Internal validity, 271 Internet, 29–33 Internet2, 33 Internet hosting, 30 Internet questionnaires,

355–359, 357f Internet survey, 225 Interpretation, 500 Interquartile range, 498 Interrogative techniques, 113 Intersubjective certifiability, 134 Interval scales, 297 Interviewer bias, 192 Interviewer cheating, 193, 453 Interviewer error, 193 Intranet, 33 Introduction section, 618 Inverse (negative) relationship, 563 Item nonresponse, 209, 463

J Judgment sampling, 393–394

K Kerry, John, 67 Keyword search, 31 Knowledge, definition of, 21 Knowledge management, 21–22

L Laboratory experiments, 267–268 Laddering, 151 Ladder of abstraction, 40, 40f Landon, Alf, 67 Lang, Andrew, 98 Latent construct, 40 Leading question, 342 Levenstein, Aaron, 563 Lewin, Kurt, 38 Likert scale, 315–316 Literature review, 63 Loaded question, 342 Longitudinal studies, 196–197,

196f, 199f Lubbock, John, 241

M Mail survey, 217–219 Main effect, 256 Mall intercept interviews, 211 Managerial action standard, 122 Manager of decision support

systems, 78 Manipulation, 57 Manipulation check, 271 MANOVA. See multivariable analysis

of variance Marginal, 487 Market-basket analysis, 169 Marketing-oriented research, 7 Market tracking, 164 Marx, Groucho, 242 Maturation effect, 272 McGovern, George, 80 Mean, 413 Measurement, definition of, 288, 289f Measurement concepts, 288–309 Measure of association, 561 Mechanical observation, 243–249 Median, 415 Median split, 493 Mixed-mode survey, 229 Mode, 415 Model building, 165–168 Moderator, 145 Moderator variable, 490 Monadic rating scale, 327 Moral standards, 88 Mortality effect, 273 Multicollinearity, 590 Multidimensional scaling, 601, 601f Multiple-grid question, 350 Multiple regression analysis, 586–590

Multistage area sampling, 399–400, 400f

Multivariable analysis of variance (MANOVA), 591–594

Multivariate statistical analysis, 507, 582–609

analysis of dependence, 586–594 analysis of interdependence,

595–602, 596f, 599f, 600f, 601f

ANOVA, 591–594 definition of, 583 MANOVA, 591–594 measurement scales, 585 techniques for, 584

Mutually exclusive, 339

N Neural networks, 169 Newton, Isaac, 160 No contacts, 189 Nominal scales, 293–295 Nonparametric statistics, 516 Nonprobability sampling, 392, 402f

convenience sampling, 392–393 judgment sampling, 393–394 quota sampling, 394–395 snowball sampling, 395

Nonrespondent error, 459 Nonrespondents, 188f, 189 Nonresponse error, 188f, 189 Nonspurious association, 55–56 Normal distribution, 418–422 Nuisance variables, 261 Numerical scales, 319

O Observation, 152, 236–238 Observation methods, 235–252, 237f

in business research, 236–238 content analysis, 243 of human behavior, 238f

complementary evidence, 238–239

ethical issues in, 241–242 interviewing, 241

mechanical observation physiological reactions,

247–249 scanner-based research,

246–247 television monitoring, 244 website traffic, 245

nature of, 237–238 of physical objects, 242–243

Observer bias, 241 Ogilvy, David, 341 Online focus group, 147 Open-ended boxes, 356 Open-ended response questions,

336–338 Operationalizing, 42, 292 Optical scanning system, 476 Opt-in lists, 405 Oral presentation, 628–629

668 Index

Order bias, 347 Ordinal scales, 295–296 Ordinary least-squares (OLS)

analysis method, 571 Outlier, 499 Outside agency, 74

P Paired comparisons, 324–325 Paired-samples t-test, 539 Parametric statistics, 516 Parker, Dorothy, 136 Partial correlation, 588 Participant-observation, 138 Pasteur, Louis, 236 Performance-monitoring research, 9 Personal interviews, 206–212 Phenomenology, 136–137 Piggyback, 142 Pilot study, 63 Pirandello, Luigi, 328 Pirsig, Robert M., 44–46 Pivot question, 348 Placebo, 91, 265 Placebo effect, 265 Plug value, 463 Point estimate, 427 Pooled estimate of the standard

error, 535 Pope, Alexander, 422 Popper, Karl R., 38, 43 Population distribution, 422 Population element, 385 Population parameters, 410 Population (universe), 385 Pop-up boxes, 358 Preliminary tabulation, 360 Pretest, 63 Pretesting, 231 Primary sampling unit, 390 Probability sampling, 392, 402f

cluster sampling, 398 disproportional sampling, 397–398 multistage area sampling,

399–400, 400f proportional sampling, 397–398 simple random sampling, 396 stratified sampling, 397 systematic sampling, 396

Probing, 114 Problem definition, 106–130

problem complexity, 108f dramatic changes, 110 situation frequency, 109–110 symptoms ambiguity, 110–111

process of, 112f business decision, 112–115 interview, 113 managerial statements,

116–118, 117f relevant variables, 118–120, 120f research objectives, 120–121 steps, 111 symptoms, 116f unit of analysis, 118

research proposal

as contract, 125–126 funded business research, 126 outcome anticipation,

126–127, 127f as a planning tool, 123–124, 124f

time spent on, 123 Production-oriented research, 7 Product-oriented research, 7 Proportional sampling, 397–398 Proportions, 412 Propositions, 41 Proprietary business research, 24 Protective technique, 152 Pseudo-research, 94–95 Psychogalvanometer, 248 Pull technology, 32 Pupilometer, 248 Push button, 356 Push polls, 95–96 Push technology, 32 P-value, 509, 510f

Q Quadrant analysis, 491, 491f Qualitative business research, 132 Qualitative data, 135 Qualitative research tools, 131–158

case studies, 139–140 common techniques in, 141f

collages, 152 conversations, 150 depth interviews, 149–150, 151f discussion guide, 145 focus group interview,

141–143, 146f free-association method,

151–153 group composition, 143–144 group moderator, 145 interactive media and, 147 laddering, 151 observation, 152 online vs. face-to-face, 148 protective technique, 152 scientific decision process,

154–155 social networking, 151 thematic appreciation test,

152–153 contrasting methods,

134–135, 135f environmental conditions,

144–145 ethnography, 138 exploratory research, 153–155 exploratory vs. confirmatory

research, 135–136 grounded theory, 139 phenomenology, 136–137 uses of, 132–133 vs. quantitative research, 133–134

Quantitative business research, 133–134

Quantitative data, 135 Quasiexperimental design, 274–276 Questionnaire design, 333–372

accuracy, 335–336 best layout, 350–359 best question sequence,

347–350, 349f for global markets, 361 guidelines for, 341–347 hospital questionnaire, 365f–368f McDonald's Spanish language

questionnaire, 371f pretesting, 360–361 question wording

advertising, 373–375 for children, 381 for demographics, 380 for education, 381 for goods, 376–380 for income, 381–382 for marital status, 381 ownership, 376 product usage, 376 for services, 376–380

relevancy, 335 travel questionnaire, 369f–371f

Quota sampling, 394–395

R Radio button, 356 Random digit dialing, 215 Randomization, 261 Randomized-block design, 280, 280f Random sampling error, 188, 188f,

390–391 Ranking, 313 Rank order, 494–496, 495f Rating, 313 Ration scales, 298–299 Ratio scales, 297 Raw data, 459 Record, 467 Refusals, 189 Relativism, 88 Relevance, of information, 19–20 Reliability, 301–302 Repeated measures, 260 Replication, 154 Report format, 613–620, 614f Research analyst, 78 Research assistants, 78 Research design, 64–66 Researcher-dependent, 132 Research generalist, 83 Research methodology section, 618 Research objectives, 60, 120–121 Research organizations, list of, 75f Research project, 68–69 Research proposal, 123–127 Research questions, 120–121 Research report, 613 Research results communication,

610–634 communication model, 611–613,

611f, 612f Internet reports, 629 Intranet reports, 629 oral presentation, 628–629 report format, 613–620, 614f

use of graphic aids charts, 622–628, 623f, 624f,

625f, 626f, 627f tables, 620–621, 620f, 621f,

622f Research suppliers, 85 Respondent error, 188f, 189 Respondents, 185 Response bias, 190 Response latency, 239 Response rate, 219 Results section, 619 Reverse coding, 300, 316 Reverse directory, 389 Rule of parsimony, 596

S Sample, 385 Sample bias, 188, 188f Sample distribution, 422 Sample selection error, 193 Sample size determination, 409–440

central limit theorem, 424–426, 424f, 425f, 426f

data usability central tendency measures,

413–415, 414f dispersion measures, 415–418,

416f, 418f frequency distribution,

411–412, 411f proportions, 412

factors for, 431–433 judgment, 436 normal distribution, 418–422 parameter estimation, 427–430 population distribution, 422 population size influence, 433 for proportions, 433–434 random error, 430–431, 430f sample distribution, 422 for sample proportions,

435–436, 435f sampling distribution, 422 types of, 423f

Sample statistics, 410 Sample survey, 185 Sampling, 66–67 Sampling designs, 384–408

accurate results, 386 appropriateness of design, 401–403 Internet sampling, 404–405 nonprobability sampling,

392–395, 402f nonsampling errors, 390–391 practical concepts, 389f

sampling frame, 388–390 target population, 387–388

probability sampling, 395–400, 402f

probability vs. nonprobability, 392 random sampling, 390–391 reasons for, 385–386 terminology, 385 test unit destruction, 386–387

Sampling distribution, 422

Index 669

Sampling frame, 388–390 Sampling frame error, 390 Sampling unit, 390 Santayana, George, 212 Scale measurement, 294f, 296f

criteria for coefficient alpha, 302 internal consistency, 302 reliability, 301–302 split-half method, 302 test-retest method, 302–303

index measures, 299–301 interval scales, 297 mathematical analysis of, 298–299 nominal scales, 293–295 ordinal scales, 295–296 ratio scales, 297–298 reliability vs. validity, 305 sensitivity, 305–306 validity, 303–305

Scales, 292 Scaling concepts, 288–309 Scanner-based consumer panel, 247 Scanner data, 26 Scientific method, 6–7, 6f, 44 Search engine, 31 Secondary data, 160 Secondary data research, 159–182

advantages of, 160 disadvantages of, 160–162 global research, 177–179, 179f objectives of, 164f

database marketing, 170 data mining, 168–170, 168f environmental scanning,

164–165 fact-finding, 162–165, 163f market potential, 166, 166f model building, 165–168 sales forecasting, 167–168 trend analysis, 164

sources of, 170–177 books, 174 commercial sources, 175–177 for external data, 171 government resources, 174 for internal and proprietary

data, 171 Internet, 173, 173f libraries, 171, 172f media resources, 174–175 producers, 174 single-source data, 177 trade association sources, 175 vendors, 174

Secondary sampling unit, 390 Selection effect, 273 Self-selection bias, 190 Semantic differential, 317–318,

317f, 318f Sensitivity, 305–306 Shelley, Percy Bysshe, 489 Significance level, 509 Simple attitude scales, 313–314

Simple (bivariate) linear regression, 568

Simple-dichotomy question, 338 Simple random sampling, 396 Single-source data, 177 Site analysis techniques, 168 Situation analysis, 112 Snowball sampling, 395 Social desirability bias, 192 Sophocles, 514 Sorting, 313 Spencer, Herbert, 335 Spinoza, Benedict, 42 Split-ballot technique, 343–344 Split-half method, 302 Spyware, 91 Standard deviation, 417 Standard error of the mean, 422 Standardized normal distribution,

419, 419f, 420f, 421f Standardized regression

coefficient, 569 Standardized research service, 85 Stapel scales, 319–320, 320f Statistical base, 488 Status bar, 356 Stratified sampling, 397 String characters, 467 Structured question, 194 Subjective, 133–134 Subjects, 255 Sugging, 93–94 Summated scales, 300 Survey, 65 Survey research

advantages of, 186–187, 230f disadvantages of, 230f errors in, 188f

administrative error, 193 data protecting error, 193 deliberate falsification, 190–191 interviewer cheating, 193 interviewer error, 193 no contacts, 189 nonrepsonse error, 188f, 189 random sampling error, 188, 188f refusals, 189 respondent error, 188f, 189 response bias, 190 sample bias, 188, 188f sample selection error, 193 self-selection bias, 190 systematic error, 188, 188f types of, 192 unconscious

misinterpretation, 191 ethical issues in, 231 external customers, 198 internal customers, 198 interviews, 206 noninteractive media, 206 overview of, 184–204 personal interviews

advantages of, 207–209

callbacks, 211 central location interviewing,

215 disadvantages of, 209–210 door-to-door interviews, 210 mail intercept interviews, 211 telephone interviews, 212–215

characteristics, 212–215 quality management,

197–201, 201f respondent communication,

205–234 selection of, 229–231 self-administered

questionnaires, 217f e-mail survey, 224 fax surveys, 223–224 Internet survey, 225 kiosk interactive surveys,

228–229 mail survey, 217–219, 220–223 response rate, 219, 222f text message surveys, 229

survey methods consumer panel, 197 longitudinal studies, 196–197,

196f, 199f temporal classification,

195–197 using surveys, 185–187

Symptoms, 49 Syndicated service, 85 Syrus, Publius, 65 Systematic error, 188, 188f, 261 Systematic sampling, 396

T Tabulation, 486 Tachistoscope, 268 TAT. See Thematic appreciation test T-distribution, 517–521, 518f Telephone interviews, 212–215 Television monitoring, 244 Temporal sequence, 55 Tertiary sampling unit, 390 Testing effect, 272 Test-market, 57 Test of differences, 529–530 Test-retest method, 302–303 Test tabulation, 472 Test units, 260–263 Thematic appreciation test (TAT),

152–153 Themes, 139–140 Theory, definition of, 38 Theory building, 37–47, 43f

goals of, 38–39 research concepts, 39–41 research constructs, 39–41 research hypotheses, 41–42 research propositions, 41–42 scientific method, 44–46 theory, definition of, 38

understanding of, 42–44 verifying theory, 43

Thoreau, Henry David, 196 Thurstone interval scale, 322 Timeliness, of information, 20 Time series design, 278, 279f Totally exhaustive, 339 Total quality management,

197–201 Tracking study, 197 Traditional questionnaires, 350–354,

351f, 352f, 353f, 354f T-test, 517, 534–538 Twain, Mark, 174 Type I error, 514 Type II error, 514

U Unbalanced rating scale, 328 Undisguised question, 194 Uniform resource locator (URL), 31 Unit of analysis, 118 Univariate statistical analysis,

506–527 chi-square test, 521–524 goodness-of-fit, 521–524 hypothesis testing, 507–515, 510f,

511f, 512f hypothesis test of a

proportion, 524 t-distribution, 517–521, 518f technique choice, 514–516

Unobtrusive methods, 67 Unstructured question, 194 URL. See Uniform resource locator

V Validity, 303–305 Value labels, 467 Variable piping software, 358 Variables, 42, 118 Variance, 417 Variate, 583 Visible observation, 237 Voice-pitch analysis, 249

W Washington, Booker T., 461 Welcome screen, 225 Wilde, Oscar, 324 Wilson, Woodraw, 498 Within-groups variance, 545 Within-subject design, 270 World Wide Web (WWW), 31 WWW. See World Wide Web Wycherley, William, 148

Z Z-test of differences of proportion,

540–541

670 Index

Brief Contents��
Contents��
Preface��
Part 1: Introduction

Ch 1: The Role of Business Research��

Learning Outcomes��
Chapter Vignette: ESPN Hits a Home Run by Leveraging the Power of Its Business Research��
Introduction��
The Nature of Business Research��
Managerial Value of Business Research��
When is Business Research Needed?��
Business Research in the Twenty-First Century��
Overview
Summary
Key Terms and Concepts
Questions for Review and Critical Thinking��
Research Activities

Ch 2: Information Systems and Knowledge Management��

Learning Outcomes��
Chapter Vignette: Delivery, Data, and UPS��
Introduction
Information, Data, and Intelligence��
The Characteristics of Valuable Information��
Knowledge Management��
Global Information Systems��
Decision Support Systems��
The Internet and Research��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 3: Theory Building��

Learning Outcomes��
Chapter Vignette: Theory and Practice��
Introduction��
Research Concepts, Constructs, Propositions, Variables, and Hypotheses��
Understanding Theory��
The Scientific Method��
Practical Value of Theories��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 4: The Business Research Process: An Overview��

Learning Outcomes��
Chapter Vignette: Getting (and Keeping) Up to Speed: Hoover's Helps HP��
Introduction��
Decision Making��
Types of Business Research��
Stages in the Research Process��
The Research Program Strategy��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 5: The Human Side of Business Research: Organizational and Ethical Issues��

Learning Outcomes��
Chapter Vignette: I Can't Share This Report!��
Introduction��
Organizational Structure of Business Research��
Research Suppliers and Contractors��
Ethical Issues in Business Research��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Part 2: Beginning Stages of the Research Process

Ch 6: Problem Definition: The Foundation of Business Research��

Learning Outcomes��
Chapter Vignette: Deland Trucking Has a "Recruitment" Problem��
Introduction��
The Problem-Definition Process��
Clarity in Research Questions and Hypotheses��
How Much Time should be Spent on Problem Definition?
The Research Proposal��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 7: Qualitative Research Tools��

Learning Outcomes��
Chapter Vignette: What's in the Van?��
Introduction��
What is Qualitative Research?
Qualitative "versus" Quantitative Research
Orientations to Qualitative Research��
Common Techniques Used in Qualitative Research��
Exploratory Research in Science and in Practice��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 8: Secondary Data Research in a Digital Age��

Learning Outcomes��
Chapter Vignette: Business Facts on a Grand Scale��
Introduction��
Typical Objectives for Secondary Data Research Designs��
Sources of Secondary Data��
Single-Source Data-Integrated Information��
Sources for Global Research��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Part 3: Research Methods for Collecting Primary Data

Ch 9: Survey Research: An Overview��

Learning Outcomes��
Chapter Vignette: Media Phones-The Next Wave of Communication Technology?��
Introduction��
Using Surveys��
Errors in Survey Research��
Classifying Survey Research Methods��
Total Quality Management and Customer Satisfaction Surveys��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 10: Survey Research: Communicating with Respondents��

Learning Outcomes��
Chapter Vignette: Mobile Surveys Catching on, and Catching Respondents "On the Go"!
Introduction��
Interviews as Interactive Communication��
Personal Interviews��
Telephone Interviews��
Self-Administered Questionnaires��
Self-Administered Questionnaires Using Other Forms of Distribution��
Selecting the Appropriate Survey Research Design��
Pretesting��
Ethical Issues in Survey Research��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 11: Observation Methods��

Learning Outcomes��
Chapter Vignette: Mystery Diner at Seasons Restaurant��
Introduction��
Observation in Business Research��
Observation of Human Behavior��
Observation of Physical Objects��
Content Analysis��
Mechanical Observation��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 12: Experimental Research��

Learning Outcomes��
Chapter Vignette: Testing Web Protocols for Financial Markets��
Introduction��
Creating an Experiment��
Designing an Experiment to Minimize Experimental Error��
Demand Characteristics��
Establishing Control��
Ethical Issues in Experimentation��
Practical Experimental Design Issues��
Issues of Experimental Validity��
Classification of Experimental Designs��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Part 4: Measurement Concepts

Ch 13: Measurement and Scaling Concepts��

Learning Outcomes��
Chapter Vignette: Money Matters?��
Introduction��
What Do I Measure?��
Levels of Scale Measurement��
Index Measures��
Three Criteria for Good Measurement��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 14: Attitude Measurement��

Learning Outcomes��
Chapter Vignette: Heat and Smoke-What Keeps Them Happy?��
Introduction��
Attitudes as Hypothetical Constructs��
Techniques for Measuring Attitudes��
Attitude Rating Scales��
Measuring Behavioral Intention��
Ranking��
Other Methods of Attitude Measurement��
Selecting a Measurement Scale: Some Practical Decisions��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activity��

Ch 15: Questionnaire Design��

Learning Outcomes��
Chapter Vignette: J.D. Power Asks: It's Interesting, but Do You Really Want It?��
Introduction��
Questionnaire Quality and Design: Basic Considerations��
Wording Questions��
Guidelines for Constructing Questions��
What is the Best Question Sequence?��
What is the Best Layout?��
How Much Pretesting and Revising are Necessary?��
Designing Questionnaires for Global Markets��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activity��
Appendix 15A: Question Wording and Measurement Scales for Commonly Researched Topics

Part 5: Sampling and Fieldwork

Ch 16: Sampling Designs and Sampling Procedures��

Learning Outcomes��
Chapter Vignette: Changing Pocketbook Problems for Today's Families��
Introduction��
Sampling Terminology��
Why Sample?��
Practical Sampling Concepts��
Random Sampling and Nonsampling Errors��
Probability versus Nonprobability Sampling��
Nonprobability Sampling��
Probability Sampling��
What is the Appropriate Sample Design?
Internet Sampling is Unique��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 17: Determination of Sample Size: A Review of Statistical Theory��

Learning Outcomes��
Chapter Vignette: Federal Reserve Finds Cards are Replacing Cash��
Introduction��
Making Data Usable��
The Normal Distribution��
Population Distribution, Sample Distribution, and Sampling Distribution��
Central-Limit Theorem��
Estimation of Parameters��
Sample Size��
A Reminder about Statistics��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 18: Fieldwork��

Learning Outcomes��
Chapter Vignette: Software for Fieldwork? Ask Askia��
Introduction��
The Nature of Fieldwork��
Who Conducts the Fieldwork?��
In-House Training for Inexperienced Interviewers��
Principles of Good Interviewing��
Fieldwork Management��
Supervision of Fieldworkers��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activity��

Part 6: Data Analysis and Presentation

Ch 19: Editing and Coding: Transforming Raw Data into Information��

Learning Outcomes��
Chapter Vignette: Coding What a Person's Face "Says"��
Introduction��
Stages of Data Analysis��
Editing��
Coding��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 20: Basic Data Analysis: Descriptive Statistics��

Learning Outcomes��
Chapter Vignette: Choose Your "Poison"��
Introduction��
The Nature of Descriptive Analysis��
Tabulation��
Cross-Tabulation��
Data Transformation��
Calculating Rank Order��
Tabular and Graphic Methods of Displaying Data��
Computer Programs for Analysis��
Interpretation��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 21: Univariate Statistical Analysis��

Learning Outcomes��
Chapter Vignette: Well, are They Satisfied or Not?��
Introduction��
Hypothesis Testing��
Choosing the Appropriate Statistical Technique��
The t-Distribution��
The Chi-Square Test for Goodness of Fit��
Hypothesis Test of a Proportion��
Additional Applications of Hypothesis Testing��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 22: Bivariate Statistical Analysis: Differences between Two Variables

Learning Outcomes��
Chapter Vignette: Gender Differences and Double Standards in Ethical Perceptions��
Introduction��
What is the Appropriate Test of Difference?��
Cross-Tabulation Tables: The X2 Test for Goodness-of-Fit
The t-Test for Comparing Two Means��
The Z-Test for Comparing Two Proportions��
Analysis of Variance (ANOVA)��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��
Appendix 22A: Manual Calculation of an F-Statistic��
Appendix 22B: ANOVA for Complex Experimental Designs��

Ch 23: Bivariate Statistical Analysis: Measures of Association��

Learning Outcomes��
Chapter Vignette: Bringing Your Work to Your Home (and Bringing Your Home to Work)��
Introduction��
The Basics��
Simple Correlation Coefficient��
Regression Analysis��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��
Appendix 23A: Arithmetic behind OLS

Ch 24: Multivariate Statistical Analysis��

Learning Outcomes��
Chapter Vignette: Cowabunga Never Goes Out of Style��
Introduction��
Classifying Multivariate Techniques��
Analysis of Dependence��
Analysis of Interdependence��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Ch 25: Communicating Research Results: Report Generation, Oral Presentation, and Follow-Up��

Learning Outcomes��
Chapter Vignette: A Business Report Title (and Nothing Else)-Tips to Get Started��
Introduction��
Insights from the Communications Model��
The Report in Context��
Report Format��
Effective Use of Graphic Aids��
The Oral Presentation��
Reports on the Internet or Intranet��
The Research Follow-Up��
Summary��
Key Terms and Concepts��
Questions for Review and Critical Thinking��
Research Activities��

Part 7: Comprehensive Cases with Computerized Databases��

Comprehensive Cases

Case 1: Running the Numbers: Does It Pay?��
Case 2: Attiring Situation��
Case 3: Values and the Automobile Market��
Case 4: TABH, INC., Automotive Consulting��
Case 5: The Atlanta Braves��
Case 6: Knowing the Way��

Glossary of Frequently Used Symbols��
Glossary��
Endnotes��
Index��