Discussion Question #3

albany20

Babin_EssentialsMarketingResearch_7e_Ch12_PowerPoint.pptx

Home >Biology homework help >Discussion Question #3

Chapter 12 Sampling and Statistical Theory

12-‹#›

Learning Outcomes

After studying this chapter, you should

Explain reasons for taking a sample rather than a complete census

Describe the process of identifying a target population and selecting a sampling frame to represent it with a sample

Compare random sampling and systematic (nonsampling) errors with an emphasis on how online access can reduce or increase error

Identify the types of nonprobability sampling, including their advantages and disadvantages

Summarize various types of probability samples

Discuss how to choose an appropriate sample design

LEARNING OUTCOMES

12-‹#›

Introduction

A sample is a subset of some larger population that is measured or observed in some way to infer what the entire population is like

Purpose of sampling is to estimate an unknown characteristic of a population

Population (universe) is any complete group

Sampling is defined in terms of the population being studied

A census is an investigation of all the individual elements making up the population—a total enumeration rather than a sample

LEARNING OUTCOMES

12-‹#›

Why Sample?

Pragmatic reasons

Sampling cuts costs, reduces labor requirements, and gathers vital information quickly

Accurate and reliable results

A sample on occasion is more accurate than a census

Increased volume of work in a census may lead to interviewer mistakes, tabulation errors, and other nonsampling errors

Destruction of test units

Occurs in the process of the research project

Provides the case against using a census

LO01

LEARNING OUTCOMES

12-‹#›

Learning Objective 01

Defining the Target Population (1 of 2)

Once the decision to sample has been made, the first question concerns identifying the target population

The population must be defined accurately for the research to produce good results

One approach for defining the target population is to ask and answer questions about crucial population characteristics

Is a list available that matches our population?

Who are we not interested in?

Should the study include multiple populations?

Answers to these questions help researchers and decision-makers focus on the right populations of potential respondents

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

Defining the Target Population (2 of 2)

The sample is implemented using the tangible, identifiable characteristics that also define the population

If the population members cannot be reached by an appropriate communication method, they cannot be part of a sample

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

EXHIBIT 12.2 Stages in the Selection of a Sample

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

The Sampling Frame

A sampling frame is a list of elements from which the sample may be drawn

Also called the working population

Sampling frame error occurs when certain sample elements are excluded or when the entire population is not accurately represented in the sampling frame

Almost every list excludes some members of the population

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

Sampling Services

Sampling services are firms specializing in providing lists or databases of specific populations

Also called list brokers

Equifax City Directory provides complete, comprehensive, and accurate business and residential information

A reverse directory provides listings by city and street address or by phone number

Useful when a researcher wishes to survey only a certain geographical area

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

Online Panels

Online panels are lists of respondents who have agreed to participate in marketing research

Generally contain millions of potential respondents

The more specific the profile requested, the more expensive the panel

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

Sampling Frames for International Marketing Research

The availability of sampling frames around the world varies dramatically

Not every country’s government conducts a census of the population

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

Sampling Units

The sampling unit is a single element or group of elements subject to selection in the sample

If the target population has first been divided into units, additional terminology must be used

Primary sampling unit (PSU) designates units selected in the first stage of sampling

Secondary sampling or tertiary sampling units describes units in successive stages of sampling

When there is no list of population elements the sampling unit is generally something other than the population element

LO02

LEARNING OUTCOMES

12-‹#›

Learning Objective 02

Random Sampling and Nonsampling Errors

Statistical error occurs when a difference exists between the value of a sample statistic and the value of the corresponding population parameter

Two basic causes of differences

Random sampling errors

Systematic (nonsampling) errors

Random sampling error is the difference between the sample result and the result of a census conducted using identical procedures

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Random Sampling Error

Random sampling error is a technical term that refers only to statistical fluctuations that occur because of chance variations in the elements selected for the sample

A function of sample size

As sample size increases, random sampling error decreases

Margin of error is determined by the sample size

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

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

Sample biases account for a large portion of errors in marketing research

Errors due to sample selection problems are nonsampling errors

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Systematic But Not Obvious Sampling Error

Internet surveys allow researchers to reach a large sample rapidly

Both an advantage and a disadvantage

Due to the flood of online questionnaires, frequent Internet users may be more selective about which surveys they bother answering

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Website Visitors (1 of 2)

These unrestricted samples are clearly not random samples

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

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Website Visitors (2 of 2)

Survey software can be used to trigger a pop-up survey for every Nth visitor or on information gathered on the respondent’s Web behavior

Randomly selecting Website visitors can cause a potential problem

May over-represent the more frequent visitors to the site

Programming techniques and technologies (cookies, registration data, or pre-screening) can help accomplish more representative sampling based on site traffic

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Panel Samples

Consumer panels provide a practical sampling frame in many situations

There is some concern regarding the representativeness of these samples

Researchers must take more steps to ensure that the sampling units do indeed represent the population

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Opting In

Opt in refers to giving permission to receive selected e-mail from a company with an Internet presence

Spamming is not tolerated by experienced Internet users and can backfire

Sites like Amazon’s Mechanical Turk provide another opportunity for respondents to opt in to surveys

These respondents participate as an unscreened, paid respondent; therefore, not random

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Less Than Perfectly Representative Samples

Random sampling errors and systematic errors associated with the sampling process may combine to yield a sample that is less than perfectly representative of the population

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

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

EXHIBIT 12.3 Errors Associated with Sampling

LO03

LEARNING OUTCOMES

12-‹#›

Learning Objective 03

Probability Versus Nonprobability Sampling

The main alternative sampling plans may be grouped into two categories

Probability techniques

Probability sampling - every population element has a known, nonzero probability of selection

Simple random sample is the best-known probability sample

Nonprobability techniques

Nonprobability sampling - probability of any member of the population being chosen is unknown

The selection of sampling units is quite arbitrary

Nonprobability samples are pragmatic and are used in market research

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

EXHIBIT 12.4 Summarizing Sampling Processes

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Convenience Sampling

Convenience sampling refers to sampling by obtaining people or units that are conveniently available

Used to obtain results quickly and economically

Used obtaining a sample through other means is impractical or impossible

Employed when research is looking at cross-cultural differences in organizational or consumer behavior

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Judgment Sampling

Judgment sampling refers to a nonprobability technique in which an experienced individual selects the sample based on his or her judgment

Also called purposive sampling

The consumer price index (CPI) is based on a judgment sample

Test-market cities often are selected because they are viewed as typical cities whose demographic profiles closely match the national profile

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Quota Sampling

Quota sampling is used to ensure that the various subgroups in a population are represented on pertinent sample characteristics to the exact extent that the investigators desire

In quota sampling, the interviewer has a quota to achieve

Aggregating the various interview quotas yields a sample representing the desired proportion of the subgroups

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Quota Sampling: Possible Sources of Bias

Respondents are selected according to a convenience sampling procedure rather than on a probability basis (as in stratified sampling)

The haphazard selection of subjects may introduce bias

Quota samples tend to include people who are easily found, willing to be interviewed, and middle class

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Advantages of Quota Sampling

The major advantages

Speed of data collection

Lower costs

Convenience

Careful supervision of the data collection may provide a representative sample for analyzing the various subgroups within a population

May be appropriate when the researcher knows that a certain demographic group is more likely to refuse to cooperate with a survey

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Snowball Sampling

Snowball sampling involves using some process for selecting a few initial respondents and then uses those respondents to seek out additional respondents

This technique is used to locate members of rare populations by referrals

Reduced costs and sample sizes are clear-cut advantages of snowball sampling

Possible bias due to the referred member being similar to the first person who made the referral

LO04

LEARNING OUTCOMES

12-‹#›

Learning Objective 04

Probability Sampling

Based on chance selection procedures

Eliminate the bias inherent in nonprobability sampling procedures because the probability sampling process is random

Randomness characterizes a procedure whose outcome cannot be predicted because it depends on chance

Randomness is the basis of all probability sampling techniques

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

Simple Random Sampling

Simple random sampling is a sampling procedure that assures that each element in the population will have an equal chance of being included in the sample

Sample selection when populations consist of large numbers of elements

Utilizes tables of random numbers or computer-generated random numbers

In contrast to other, more complex types of probability sampling, this process is simple in that only one stage of sample selection is required

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

Systematic Sampling

Systematic sampling is a procedure in which an initial starting point is selected by a random process; then every nth number on the list is selected

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

Stratified Sampling

Stratified Sampling is 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

Provides a more efficient sample than would be possible with simple random sampling

Ensures that the sample will accurately reflect the population on the basis of the criterion or criteria used for stratification

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

Proportional Versus Disproportional Sampling

Proportional stratified sampling means the number of sampling units drawn from each stratum is in proportion to the relative population size of the stratum

In a disproportional stratified sample, the sample size for each stratum is not allocated in proportion to the population size but is dictated by analytical considerations

Ensures an adequate number of sampling units in every stratum

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

EXHIBIT 12.5 Disproportional Sampling: Hypothetical Example

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

Cluster Sampling

Cluster sampling is 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

The area sample is the most popular type of cluster sample

Cluster samples become attractive when lists of a sample population are not available

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

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

EXHIBIT 12.6 Examples of Clusters

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

Multistage Area Sampling

Multistage area sampling is a cluster sampling approach involving multiple steps that combine some of the probability techniques already described

Researchers may take as many steps as necessary to achieve a representative sample

LO05

LEARNING OUTCOMES

12-‹#›

Learning Objective 05

What Is the Appropriate Sample Design? (1 of 2)

Degree of accuracy

Cost savings is a trade off for a reduction in accuracy

Resources

If the researcher’s financial and human resources are restricted, certain options will have to be eliminated

Time

Time constraints restrict sampling techniques to simpler methods

LO06

LEARNING OUTCOMES

12-‹#›

Learning Objective 06

What Is the Appropriate Sample Design? (2 of 2)

Advance knowledge of the population

A lack of adequate lists may automatically rule out systematic sampling, stratified sampling, or other sampling designs

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

Discussion Question #3

image1.jpeg

image2.png

image3.emf

image4.png

image5.emf

image6.emf

image7.png