1. Sampling 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
Convenience sampling
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)