Chapter 12 Sampling Plans

1. Chapter 12Sampling Plans

2. Basic Sampling Concepts • Population • The aggregate of cases in which a researcher is interested • Sampling • Selection of a portion of the population (a sample) to represent the entire population  • Eligibility criteria • The characteristics that define the population • Inclusion criteria • Exclusion criteria

3. Basic Sampling Concepts (cont.) • Strata • Subpopulations of a population (e.g., male/female) • Target population • The entire population of interest • Accessible population • The portion of the target population that is accessible to the researcher, from which a sample is drawn

4. Sampling Goal in Quantitative Research • Representative sample • A sample whose key characteristics closely approximate those of the population—a sampling goal in quantitative research • More easily achieved with: • Probability sampling • Homogeneous populations • Larger samples

5. Sampling Problems in Quantitative Research • Sampling bias • The systematic over- or under-representation of segments of the population on key variables when the sample is not representative • Sampling error • Differences between sample values and population values

6. Types of Sampling Designs • Probability sampling • Involves random selection of elements: each element has an equal, independent chance of being selected • Nonprobability sampling • Does not involve selection of elements at random

7. Question Is the following statement True or False? • The difference between sample values and population values is referred to as the sampling bias.

8. Answer • False • The sampling bias is the systematic over- or under-representation of segments of the population on key variables when the sample is not representative. Sampling error is the difference between sample values and population values.

9. Types of Nonprobability Sampling—Quantitative Research • Conveniencesampling • Snowball (network) sampling • Quota sampling • Purposive sampling

10. Convenience Sampling • Use of the most conveniently available people • Most widely used approach by quantitative researchers • Most vulnerable to sampling biases

11. Snowball Sampling • Referrals from other people already in a sample • Used to identify people with distinctive characteristics • Used by both quantitative and qualitative researchers

12. Quota Sampling • Convenience sampling within specified strata of the population • Enhances representativeness of sample • Infrequently used, despite being a fairly easy method of enhancing representativeness

15. Question Which type of sampling is most vulnerable to bias? • Convenience sampling • Snowball sampling • Quota sampling • Purposive sampling

16. Answer a. Convenience sampling • Although it is the most widely use approach for quantitative researchers, convenience sampling is the most vulnerable to sampling biases. Snowball, quota, and purposive sampling are less subject to bias.

17. Consecutive Sampling • Involves taking all of the people from an accessible population who meet the eligibility criteria over a specific time interval, or for a specified sample size • A strong nonprobability approach for “rolling enrollment” type accessible populations • Risk of bias low unless there are seasonal or temporal fluctuations

18. Purposive (Judgmental) Sampling • Sample members are hand-picked by researcher to achieve certain goals • Used more often by qualitative than quantitative researchers • Can be used in quantitative studies to select experts or to achieve other goals

19. Types of Probability Sampling • Simple random sampling • Stratified random sampling • Cluster (multistage) sampling • Systematic sampling

20. Simple Random Sampling • Uses a sampling frame– a list of all population elements • Involves random selection of elements from the sampling frame • Not to be confused with random assignment to groups in experiments • Cumbersome; not used in large, national surveys

21. Stratified Random Sampling • Population is first divided into strata, then random selection is done from the stratified sampling frames • Enhances representativeness • Can sample proportionately or disproportionately from the strata

22. Cluster (Multistage) Sampling • Successive random sampling of units from larger to smaller units (e.g., states, then zip codes, then households) • Widely used in national surveys • Larger sampling error than in simple random sampling, but more efficient

23. Question Is the following statement True or False? • Stratified random sampling is associated with a larger sampling error but it is more efficient.

24. Answer • False • Stratified random sampling enhances representativeness; cluster sampling is associated with a larger sampling error but is considered more efficient.

25. Sample Size • The number of study participants in the final sample • Sample size adequacy is a key determinant of sample quality in quantitative research. • Sample size needs can and should be estimated through power analysis.

27. Sampling in Qualitative Research • Selection of sample members guided by desire for information-rich sources • “Representativeness” not a key issue • Random selection not considered productive

28. Methods of Sampling in Qualitative Research • Convenience (volunteer) sampling • Snowball sampling • Purposive sampling • Theoretical sampling

29. Types of Purposive Sampling in Qualitative Research (Examples) • Maximum variation sampling • Extreme/deviant case sampling • Typical case sampling • Criterion sampling • Sampling confirming and disconfirming cases

30. Theoretical Sampling • Preferred sampling method in grounded theory research • Involves selecting sample members who best facilitate and contribute to development of the emerging theory

31. Question Is the following statement True or False? • Sampling in qualitative research is guided more by the desire for rich sources of information than by the need for random selection.

32. Answer • True • Selection of sample members for qualitative research is guided by the desire for information-rich sources. The representativeness of the sample is not a key aspect and random selection is not considered productive.

33. Sample Size in Qualitative Research • No explicit, formal criteria • Sample size determined by informational needs • Decisions to stop sampling guided by data saturation • Data quality can affect sample size.

34. Sampling in the Main Qualitative Traditions Ethnography • Mingling with many members of the culture—a “big net” approach • Informal conversations with 25 to 50 informants • Multiple interviews with smaller number of key informants

35. Sampling in Phenomenology • Relies on very small samples (often 10 or fewer) • Participants must have experienced phenomenon of interest

36. Sampling in Grounded Theory • Typically involves samples of 20 to 40 people • Selection of participants who can best contribute to emerging theory (usually theoretical sampling)

37. End of Presentation