Marketing Research Data Analysis

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MarketingResearchCh10.pdf

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Chapter 10

Preparing Data for

Quantitative Analysis

Learning Objectives

• Describe the process for data preparation and analysis

• Discuss validation, editing, and coding of survey data

• Explain data entry procedures and how to detect errors

• Describe data tabulation and analysis approaches

Value of Preparing Data for Analysis

• Data preparation process - Converting data from a source into usable code for data analysis

– Important in:

• Assessing and controlling data integrity

• Ensuring data quality by detecting potential response and nonresponse biases created by interviewer or respondent errors

• Dealing with inconsistent data from different sources

• Converting data in multiple formats to a single format that can be analyzed

Exhibit 10.1 - Overview of Data Preparation and Analysis

Validation

• Determines whether a survey’s interviews or observations were conducted correctly and are free of fraud or bias

– Curbstoning: Cheating or falsification in the data collection process

Areas Covered by Validation

Fraud Screening Procedure

Completeness Courtesy

Editing

• Raw data is checked for mistakes made by either the interviewer or the respondent

• By reviewing completed interviews from primary research, the researcher can check several areas of concern

– Asking proper questions and recording answers accurately

– Correct screening of respondents and complete and accurate recording of open-ended questions

Coding

• Grouping and assigning values to various responses from survey instruments

– Codes are numerical

– Gets tedious if certain issues are not addressed prior to collecting the data

• Well-planned and constructed questionnaire reduces the time spent on coding

Coding (continued)

• Four-step process to develop codes for responses

– Generate a list of as many potential responses as possible

– Consolidate responses

– Assign a numerical value as a code

– Assign a coded value to each response

Data Entry

• Tasks involved with the direct input of the coded data into some specified software package

– Allows the research analyst to manipulate and transform the raw data into useful information

• Involves:

– Error detection

– Missing data

– Organizing data

Error Detection

• Identifies errors from data entry or other sources

• Approaches

– Determine if the software used will perform error edit routines

– Review a printed representation of the entered data

– Run a tabulation of all survey questions to examine the responses for completeness and accuracy

Missing Data

• Situation in which respondents do not provide an answer to a question

• Counter approaches

– Replace missing value with a value from a similar respondent

– Use the answers to other similar questions as a guide in determining the replacement value

Missing Data (continued)

– Use mean of a subsample of the respondents with similar characteristics that answered the question to determine a replacement value

– Use mean of the entire sample that answered the question as a replacement value

• Not recommended as it reduces overall variance in the question

Data Tabulation

• Counting the number of observations or cases that are classified into certain categories

• Forms

– One-way tabulation: Categorization of single variables existing in a study

– Cross-tabulation: Simultaneously treating two or more variables in a study

• Categorizes the number of respondents who have answered two or more questions

One-Way Tabulation

• Purposes

– Determine the amount of nonresponse to individual questions

– Locate mistakes in data entry

– Communicate the results of a research project

• Illustrated by constructing a one-way frequency table

One-Way Tabulation (continued)

• In reviewing the output, look for:

– Indications of missing data

– Determining valid percentages

– Summary statistics

Descriptive Statistics

• Used to summarize and describe the data obtained from a sample of respondents

• Measures used to describe data

– Central tendency

– Dispersion

Graphical Illustration of Data

• Next step following development of frequency tables is to translate them into graphical illustrations

• Powerful for communicating key research results generated from preliminary data analysis

Marketing Research in Action Deli Depot

• Should the Deli Depot questionnaire have screening questions?

• Run a frequency count on variable X3– Competent Employees

– Do the customers perceive employees to be competent?

Marketing Research in Action Deli Depot (continued)

• Consider the guidelines on questionnaire design

– How would you improve the Deli Depot questionnaire?