Lecture Conducting and reading research in health and human performance (4/e): Chapter 6 - Ted A. Baumgartner, Larry D. Hensley

Chapter 6 - Selection of research participants: Sampling procedures. This chapter includes contents: Subject selection and sampling, technical sampling terms, random processes in research, simple random sampling, systematic sampling,...

Selection of Research Participants:

Sampling Procedures

 This is considered highly important in social and

behavioral research

 Three basic questions to consider:

1 Are the research participants appropriate for the

 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

 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

 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

 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

 Probability Sampling

 Simple random sampling

 Stratified 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:

 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

 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

 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

 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

 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

 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

 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

 Descriptive, correlational, or experimental

 Descriptive and correlational studies typically should have more research participants

 Experimental studies often employ fewer research participants

 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 H0when, 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

representative of the population

 Little variability – small sample will suffice

 High variability – sample size will be larger

 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

 Availability of research participants

 Costs

 Time

 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

 The purpose is to establish “group equivalency” before the introduction of the independent

variable

 Two basic methods

 Independent groups design

 Repeated measures design

Independent Groups Design  

 Each research participant is randomly assigned

to one of the various treatment groups

 Each subject participates in only one group

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

order effect