Subjects, Participants, and Sampling

1. Subjects, Participants,and Sampling

2. The proof of the pudding is in the eating. By a small sample we may judge of the whole piece.Miguel de Cervantes Saavedra Spanish Writer, 1547-1616

3. Definitions • • Subject or participant: A person from whom data are collected. • – Subject is the term often used in a quantitative context; participant is used in a qualitative context. • • Sample: The collective group of subjects or participants from whom data are collected • • Population: A large group of individuals to whom the results of a study are to be generalized.

4. Two Types of Sampling Procedures • • Probability: Statistically-driven sampling techniques where the probability of being selected is known. The purpose is to select a group of subjects representative of the population. (Think quantitative) • • Non-Probability: Pragmatically-driven sampling techniques where the probability of being selected is unknown. The purpose is to select particularly knowledgeable participants. (Think qualitative)

5. Sampling for Quantitative Research Studies

6. Goals for Quantitative Sampling • • To select a sample that is representative of the population you will generalize your results to. • • To reduce sampling error and bias • – Sampling error: The difference between the “true” result and the “observed” result that can be attributed to using samples rather than populations. • – Sampling bias: The difference between the “observed” and “true” results that can be attributed to errors made by the researcher.

7. Strategies for Quantitative Sampling • • Simple Random • • Stratified Sampling • • Cluster Sampling • • Convenience Sampling

8. Simple Random • • A number is assigned to each subject in the population and a table of random numbers or a computer is used to select subjects randomly from the population.

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10. Systematic Sampling • • Proportional Stratified Sampling: The proportion of subjects in each strata in the population are reflected in the proportions of subjects in each strata of the sample. • • Disproportional Stratified Sampling: The proportions of subjects in each strata in the sample are the same regardless of the proportions of subjects in the strata of the population.

11. Example: Stratified Sample

12. Cluster Sampling • • Similar to random sampling except that naturally occurring groups are randomly selected first, then subjects are randomly selected from the sampled groups. • – Typical educational clusters are districts, schools, or classrooms.

13. Convenience Sampling • • Typical of much educational (and other) research given the constraints under which it is conducted. • • The major concern is the limited ability to generalize the results from the sample to a population the audience cares about.

14. Steps in Quantitative Sampling Key first step: Define the target population. Who do you want to generalize your results to?

15. Sampling for QualitativeResearch Studies

16. Goals for Qualitative Sampling • • To select participants that are particularly knowledgeable about the topic/phenomenon you are researching. • – Who does an investigator or reporter interview? People who are most knowledgeable or have the closest experience with the issue.

17. Strategies for Qualitative Sampling • • Typical Case: Selecting a representative participant. • • Extreme Case: Selecting a unique or atypical participant. • • Maximum Variation: Selecting at least two participants who represent extreme cases. • • Snowball (aka Network): Selecting participants from recommendations of other participants. • • Critical Case: Selecting the most important participants related to the phenomenon.

18. Evaluating Sampling in Research Studies

19. Criteria for Evaluating Sampling • • The subjects or participants were clearly described. • • The population was clearly defined. • • The sampling procedure was clearly described. • • The sampling procedure was appropriate for the problem being investigated. • • The selection of subjects was free of bias. • • Adequate sample sizes were used. • • The return rate was reported and analyzed. • • The qualitative study had knowledgeable participants.

20. Risks Associated with Volunteers • • Different characteristics between volunteers and non-volunteers can lead to non-representative responses. • – Educational level • – Socio-economic status • – Need for social approval • – Ability to socialize • – Conformity • • Commonly used due to availability and convenience.

21. Sample Sizes for Experiments • • For experimental designs, sample size is a function of level of significance, effect size, and power. Change one of these, and the minimum sample size will change. • – For a t-test at the 0.05 level of significance, a power of 0.80, and a small effect size, the minimum sample to produce a statistically significant result is 393 in each group for a total of 786 participants. With a large effect size, the minimum sample would be 26 in each group for a total of 52 participants.

22. Sample Sizes for Experiments ES = 0.6 1.0 ES = 0.5 minimum power 0.8 ES = 0.4 0.6 Power 0.4 0.2 0.0 0 50 100 150 200 Sample Size Per Group

23. Sample Sizes for Correlations • • For correlational designs, sample size is also a function of level of significance, effect size, and power. • – For a statistically significant Pearson product-moment correlation at a 0.05 level of significance, a power of 0.80, and a medium effect size, you need 85 people.

24. Sample Sizes for Surveys • • The minimum sample size for surveys is a function of: • – The margin of error you are willing to accept. This is usually set at 5%. • – The confidence interval you set. Typical choices are 90%, 95%, or 99%. Go with 95%. • – The population size you are targeting. Usually it is quite large, and any number over 20,000 has little effect on sample size. • • For the assumptions above you would need a sample of 384. • – A sample size of ≈ 1,000 will give a margin of error of 3%. • – A sample size of ≈ 100 will give a margin of error of 10%.