Selection of Research Participants: Sampling Procedures

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

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?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Practical Factors 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

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

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 Counterbalancing is often used to control for possible order effect