Chapter 12 Sampling Designs and Sampling Procedures

1. Chapter 12Sampling Designs and Sampling Procedures

2. LEARNING OUTCOMES After studying this chapter, you should be able to • Define sample, population, population element, and census • Explain reasons for taking a sample rather than a complete census • Describe the process of identifying a target population and selecting a sampling frame • Compare random sampling and systematic (nonsampling) errors • Identify the types of nonprobability sampling, including their advantages and disadvantages

3. LEARNING OUTCOMES (cont’d) After studying this chapter, you should be able to • Summarize the advantages and disadvantages of the various types of probability samples • Discuss how to choose an appropriate sample design, as well as challenges for Internet sampling

4. Sampling Terminology • Sample • A subset, or some part, of a larger population. • 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.

5. Why Sample? • Pragmatic Reasons • Budget and time constraints • Limited access to total population • Accurate and Reliable Results • Samples can yield reasonably accurate information • Strong similarities in population elements makes sampling possible • Sampling may be more accurate than a census • Destruction of Test Units • Sampling reduces the costs of research in finite populations.

6. Practical Sampling Concepts • Defining the Target Population • What is the relevant population? • Whom do we want to talk to? • Population is operationally defined by specific and explicit tangible characteristics. • The Sampling Frame (Working Population) • A list of target population elements from which a sample may be drawn; also called working population.

7. Practical Sampling Concepts (cont’d) • Sampling Frame Sources • Sampling services (list brokers) • Provide lists or databases of the names, addresses, phone numbers, and e-mail addresses of specific populations. • 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. • Sampling Frame Error • Occurs when certain sample elements are not listed or are not accurately represented in a sampling frame.

8. EXHIBIT 12.1Stages in the Selectionof a Sample

9. EXHIBIT 12.2Mailing List Directory Page

10. Sampling Units • Sampling Unit • A single element or group of elements subject to selection in the sample. • Primary Sampling Unit (PSU) • A unit selected in the first stage of sampling. • Secondary Sampling Unit • A unit selected in the second stage of sampling. • Tertiary Sampling Unit • A unit selected in the third stage of sampling.

11. Random Sampling and Nonsampling Errors • Random Sampling Error • The difference between the sample result and the result of a census conducted using identical procedures. • A statistical fluctuation that occurs because of chance variations in the elements selected for a sample. • Systematic Sampling Error • Systematic (nonsampling) error results from nonsampling factors, primarily the nature of a study’s design and the correctness of execution. • It is not due to chance fluctuation.

12. Random Sampling and Nonsampling Errors (cont’d) • 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.

13. EXHIBIT 12.3Errors Associated with Sampling Source: Adapted from Keith K. Cox and Ben M. Enis, The Marketing Research Process (Pacific Palisades, CA: Goodyear, 1972); and Danny N. Bellenger and Barnet A. Greenberg, Marketing Research: A Management Information Approach (Homewood, IL: Richard D. Irwin, 1978), pp. 154–155.

14. Probability versus Nonprobability Sampling • 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.

15. Nonprobability Sampling • Convenience Sampling • Obtaining those people or units that are most conveniently available • Judgment (Purposive) Sampling • An experienced individual selects the sample based on personal judgment about some appropriate characteristic of the sample member. • Quota Sampling • Ensures that various subgroups of a population will be represented on pertinent characteristics to the exact extent that the investigator desires.

16. Nonprobability Sampling (cont’d) • Possible Sources Of Bias • Respondents chosen because they were: • Similar to interviewer • Easily found • Willing to be interviewed • Middle-class • Needed to fill out the sample quota • Advantages of Quota Sampling • Speed of data collection • Lower costs • Convenience

17. Nonprobability Sampling (cont’d) • 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.

18. Probability Sampling • Simple Random Sampling • Assures each element in the population of an equal chance of being included in the sample. • Systematic Sampling • A starting point is selected by a random process and then every nth number on the list is selected. • Stratified Sampling • Simple random subsamples that are more or less equal on some characteristic are drawn from within each stratum of the population.

19. Proportional versus Disproportional Sampling • Proportional Stratified Sample • The number of sampling units drawn from each stratum is in proportion to the population size of that stratum. • Disproportional Stratified Sample • The sample size for each stratum is allocated according to analytical considerations.

20. Cluster Sampling • 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.

21. EXHIBIT 12.6Examples of Clusters

22. Multistage Area Sampling • Multistage Area Sampling • Involves using a combination of two or more probability sampling techniques. • Typically, geographic areas are randomly selected in progressively smaller (lower-population) units. • Researchers may take as many steps as necessary to achieve a representative sample. • Progressively smaller geographic areas are chosen until a single housing unit is selected for interviewing.

23. EXHIBIT 12.5Illustration of Multistage Area Sampling Source: From Interviewer’s Manual, Revised Edition (Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan, 1976), p. 36. Reprinted by permission.

24. Degree of Accuracy Adaptation Appropriate Sample Design Resources Knowledge of Population Time What Is the Appropriate Sample Design?

25. EXHIBIT 12.6Comparison of Sampling Techniques: Nonprobability Samples

26. EXHIBIT 12.7Comparison of Sampling Techniques: Probability Samples

27. EXHIBIT 12.10Comparison of Sampling Techniques: Probability Samples (cont’d)

28. Internet Sampling is Unique • Advantages • Internet surveys can rapidly reach a large sample. • Speed is both an advantage and a disadvantage: • Sample size requirements met almost instantaneously. • Survey must be kept open long enough for all sample units to participate. • Hard-to-reach subjects may participate • Internet samples may be representative of a target population if the target population is visitors to a particular Web site • Disadvantage • Lack of computer ownership and Internet access among certain segments of the population

29. Internet Sampling Sources WebsiteVisitors PanelSamples Recruited Ad Hoc Samples Opt-inLists

30. Internet Sampling Is Unique (cont’d) • Web Site Visitors • Unrestricted samples are clearly convenience samples • Randomly selecting visitors for a questionnaire request randomly "pops up" • Samples may over-represent more frequent visitors • Filters (e.g., cookies, passwords, and sign-ins) can identify unique visitors

31. Internet Sampling Is Unique (cont’d) • Panel Samples • Typically yield a high response rate • Members may be compensated for their time with a sweepstake or a small, cash incentive. • Database on Members • Demographic and other information from previous questionnaires • Select quota samples based on: • Product ownership • Lifestyle • Other characteristics.

32. Internet Sampling Is Unique (cont’d) • Recruited Ad Hoc Samples • Sample is recruited from databases of e-mail addresses compiled from customer/client lists, advertising banners, online sweepstakes, and registration forms. • Opt-in Lists • Subjects give permission to receive selected e-mail, such as questionnaires, from a company with an Internet presence.

33. Sample Population (universe) Population element Census Sampling frame Reverse directory Sampling frame error Sampling unit Primary sampling unit (PSU) Secondary sampling unit Tertiary sampling unit Random sampling error Systematic (nonsampling) error Nonresponse error Probability sampling Nonprobability sampling Convenience sampling Judgment (purposive) sampling Quota sampling Snowball sampling Simple random sampling Systematic sampling Periodicity Stratified sampling Key Terms and Concepts

34. Proportional stratified sample Disproportional stratified sample Cluster sampling Multistage area sampling Opt in Key Terms and Concepts (cont’d)