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Business Research Methods, 9th ed.Chapter 16

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William G. Zikmund, Barry J. Babin, Jon C. Carr, Mitch Griffin

William G. Zikmund, Barry J. Babin, Jon C. Carr, Mitch Griffin

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  • 1. Business Research Methods William G. Zikmund Chapter 16: Sample Designs and Sampling Procedures
  • 2. Sampling Terminology • Sample • Population or universe • Population element • Census
  • 3. Sample • Subset of a larger population
  • 4. Population • Any complete group – People – Sales territories – Stores
  • 5. Census • Investigation of all individual elements that make up a population
  • 6. Define the target population Select a sampling frame Conduct fieldwork Determine if a probability or nonprobability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Stages in the Selection of a Sample
  • 7. Target Population • Relevant population • Operationally define • Comic book reader?
  • 8. Sampling Frame • A list of elements from which the sample may be drawn • Working population • Mailing lists - data base marketers • Sampling frame error
  • 9. Sampling Units • Group selected for the sample • Primary Sampling Units (PSU) • Secondary Sampling Units • Tertiary Sampling Units
  • 10. Random Sampling Error • The difference between the sample results and the result of a census conducted using identical procedures • Statistical fluctuation due to chance variations
  • 11. Systematic Errors • Nonsampling errors • Unrepresentative sample results • Not due to chance • Due to study design or imperfections in execution
  • 12. Errors Associated with Sampling • Sampling frame error • Random sampling error • Nonresponse error
  • 13. Two Major Categories of Sampling • Probability sampling • Known, nonzero probability for every element • Nonprobability sampling • Probability of selecting any particular member is unknown
  • 14. Nonprobability Sampling • Convenience • Judgment • Quota • Snowball
  • 15. Probability Sampling • Simple random sample • Systematic sample • Stratified sample • Cluster sample • Multistage area sample
  • 16. Convenience Sampling • Also called haphazard or accidental sampling • The sampling procedure of obtaining the people or units that are most conveniently available
  • 17. Judgment Sampling • Also called purposive sampling • An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member
  • 18. Quota Sampling • Ensures that the various subgroups in a population are represented on pertinent sample characteristics • To the exact extent that the investigators desire • It should not be confused with stratified sampling.
  • 19. Snowball Sampling • A variety of procedures • Initial respondents are selected by probability methods • Additional respondents are obtained from information provided by the initial respondents
  • 20. Simple Random Sampling • A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample
  • 21. Systematic Sampling • A simple process • Every nth name from the list will be drawn
  • 22. Stratified Sampling • Probability sample • Subsamples are drawn within different strata • Each stratum is more or less equal on some characteristic • Do not confuse with quota sample
  • 23. Cluster Sampling • The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. • The primary sampling unit is no longer the individual element in the population • The primary sampling unit is a larger cluster of elements located in proximity to one another
  • 24. Population Element Possible Clusters in the United States U.S. adult population States Counties Metropolitan Statistical Area Census tracts Blocks Households Examples of Clusters
  • 25. Population Element Possible Clusters in the United States College seniors Colleges Manufacturing firms Counties Metropolitan Statistical Areas Localities Plants Examples of Clusters
  • 26. Population Element Possible Clusters in the United States Airline travelers Airports Planes Sports fans Football stadiums Basketball arenas Baseball parks Examples of Clusters
  • 27. What is the Appropriate Sample Design? • Degree of accuracy • Resources • Time • Advanced knowledge of the population • National versus local • Need for statistical analysis
  • 28. Internet Sampling is Unique • Internet surveys allow researchers to rapidly reach a large sample. • Speed is both an advantage and a disadvantage. • Sample size requirements can be met overnight or almost instantaneously. • Survey should be kept open long enough so all sample units can participate.
  • 29. Internet Sampling • Major disadvantage – lack of computer ownership and Internet access among certain segments of the population • Yet Internet samples may be representative of a target populations. – target population - visitors to a particular Web site. • Hard to reach subjects may participate
  • 30. Web Site Visitors • Unrestricted samples are clearly convenience samples • Randomly selecting visitors • Questionnaire request randomly "pops up" • Over- representing the more frequent visitors
  • 31. 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, or other characteristics. • Probability Samples from Large Panels
  • 32. Internet Samples • Recruited Ad Hoc Samples • Opt-in Lists

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