1. Selection of Research Participants: Sampling Procedures
Chapter 6
Selection of Research Participants: Sampling Procedures

2. Subject Selection and Sampling
This is considered highly important in social and behavioral research
Three basic questions to consider:
1. Are the research participants appropriate for the research question?
2. Are the research participants representative of the population of interest?
3. How many research participants should be used?

3. Technical Sampling Terms
Population – refers to an entire group or aggregate of people or elements having one or more common characteristics
Sample – a small subgroup of a population of interest thought to be representative of that population
Sampling – the process of selecting a subgroup or sample of the population
Sampling Frame – the accessible population or collection of elements from which the sample is actually drawn

4. Random Processes in Research
Random Selection
The purpose is to enable the researcher to generalize the results to a larger population. Thus, the researcher is concerned about the “representativeness” of the subjects in the sample
Random Assignment
The purpose is to enable the researcher to assume that groups are “equivalent” at the beginning of the study. This adds control to a study; it has nothing to do with the selection of the sample

5. Sample Selection Methods
Probability Sampling
Sampling techniques in which the probability of selecting each participant is known
Utilizes random processes, but does not guarantee the sample is representative of population
Estimates of sampling error are possible
Non Probability Sampling
Samples are not selected at random
Difficult to claim sample is representative of population
Intact groups, volunteers

6. Sample Selection Methods
Probability Sampling
Simple random sampling
Stratified random sampling
Systematic sampling
Cluster sampling
Non Probability Sampling
Purposive sampling
Convenience sampling

7. Simple Random Sampling
With simple random sampling, every member of the population has an equal probability of being selected for the sample. Also, the selection of one member of the population does not affect the chances of any other member being chosen (equal and independent)
Sampling with replacement vs. sampling without replacement
Usual procedure:
Fishbowl technique
Table of random numbers
Computer generated sampling

8. Stratified Random Sampling
A stratified random sample is one obtained by separating the population elements into non-overlapping sub-groups, called strata, and then selecting a simple random sample from each strata
No sampling unit can appear in more than one strata
A stratified sample will assure representation from each strata
The number of sampling units drawn from each strata depends upon the size of the sampling frame as well as each strata and whether the researcher wishes to maintain the same proportionality that is present in the population

9. Systematic Sampling
An alternative to simple random sampling in which the sampling units are selected in a series according to some predetermined sequence. The origin of the sequence should be controlled by chance
The researcher will choose 1/kth of the sampling frame with k being any constant. The first sampling unit is randomly selected by the investigator. Thereafter, every kth unit in the sampling frame is chosen
Simple random sampling is to be preferred, but systematic sampling is a practical and useful approximation to random sampling that is easier to perform

10. Cluster Sampling
Cluster sampling or area sampling is a simple random sample in which each sampling unit is a collection, or cluster, of elements (e.g., classrooms, schools, counties, city blocks)
The sampling unit is the “cluster”
Cluster sampling is an effective design when (1) a good frame listing population elements is not available, (2) the removal of elements from their cluster unit is not possible, or (3) it is impractical to conduct simple random sampling
The first task is to delineate or specify the cluster

11. Non Probability Sampling
The probability that an element will be chosen is not known, with the result being that a claim for representativeness of the population cannot be made
The researcher’s ability to generalize findings beyond the actual sample is greatly limited
But it is less expensive and less complicated
Convenience sampling and purposive sampling are common examples

12. Purposive Sampling
When members of the sample are purposively selected because they possess certain traits that are critical to the study
Limited generalizability
Example: Selecting elite athletes for a biomechanics study

13. Convenience Sampling
Refers to selecting research participants on the basis of being accessible and convenient to the researcher
Often involves use of volunteers
Limited generalizability
Example: Using fellow graduate students as research participants

14. Sample Size
Regardless of size, the crucial factor is whether or not the sample is representative of the population, thus how the sample is selected
Points to consider regarding sample size:
Nature of the study
Statistical considerations
Variability of population
Number of treatment groups
Practical factors

15. Nature of the study Descriptive, correlational, or experimental
Descriptive and correlational studies typically should have more research participants
Experimental studies often employ fewer research participants

16. Statistical considerations
How do you want to analyze the data? What statistical application will be used?
Power of the statistical test
Power is the probability that the test will reject the H0 when, in fact, the H0 is false
In general, the larger the sample size, the more power of the statistic being used
Generally N=30 is minimum needed to meet assumptions of many statistical procedures

17. Variability of population
Sample size is inversely related to sampling error
The larger the sample size, the smaller the sampling error and the greater likelihood that the sample is representative of the population
Little variability – small sample will suffice
High variability – sample size will be larger

18. Number of Treatment Groups
When samples are divided into smaller groups to be compared, it is important that the subgroups are of adequate size
Should be more concerned with “cell size” than total sample size

19. Practical Factors
Availability of research participants
Costs
Time

20. Comments About Sampling
Descriptive and correlational research are vitally concerned about the representativeness of the sample, usually necessitating larger sample sizes and more attention given to the sampling procedure
Experimental studies can often get by with small sample sizes, as long as internal validity is maintained
In practice, volunteer research participants are involved in a good portion of research. Be aware of the potential of systematic error being introduced in the study

21. Random Assignment
The purpose is to establish “group equivalency” before the introduction of the independent variable
Two basic methods
Independent groups design
Repeated measures design

22. Independent Groups Design
Each research participant is randomly assigned to one of the various treatment groups
Each subject participates in only one group

23. Repeated Measures Design
Subjects participate in more than one group (treatment condition)
In the simplest example, each research participant would be assigned to each level of the independent variable and then is measured after receiving the treatment
Counterbalancing is often used to control for possible order effect