1. Chapter 12 Sampling 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 Population (universe) Population Element
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 Accurate and Reliable Results
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 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 12.1 Stages in the Selection of a Sample

9. EXHIBIT 12.2 Mailing List Directory Page

10. Sampling Units Sampling Unit Primary Sampling Unit (PSU)
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 12.3 Errors 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
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 Systematic 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 12.6 Examples of Clusters

22. 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 12.5 Illustration 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. What Is the Appropriate Sample Design?
Adaptation
Knowledge of Population
Time
Resources
Degree of Accuracy

25. EXHIBIT 12.6 Comparison of Sampling Techniques: Nonprobability Samples

26. EXHIBIT 12.7 Comparison of Sampling Techniques: Probability Samples

27. EXHIBIT 12.10 Comparison 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
Website Visitors
Panel Samples
Recruited Ad Hoc Samples
Opt-in Lists

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 addresses compiled from customer/client lists, advertising banners, online sweepstakes, and registration forms.
Opt-in Lists
Subjects give permission to receive selected , such as questionnaires, from a company with an Internet presence.

33. Key Terms and Concepts 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

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