Population Sampling in Research

PE 357

Participants?

• The research question will dictate the type of participants selected for the study

• Also need to match the participants to the instrumentation and methods

How Many Participants

• 1) need to have enough to assure reliability of results (often termed the “power” of the study)

• 2) need to factor in participant mortality (e.g., injury, dropout)

Terms• Population refers to an entire group or elements

with common characteristics

• Sampling is the process whereby a small proportion or subgroup of a population is selected for analysis

• Sample refers to the small subgroup which is thought to be representative of the larger population

Steps in the sampling process

• 1) Identify the target population

• 2) Identify the accessible population

• 3) Determine the size of the sample needed

• 4) Select the sampling technique

• 5) Implement the plan

Critical Factor

• The sample needs to be representative of your population of interest

• Generalizability (external validity) of your results is dependent on this factor!

Randomization

• 1) ensures representativeness

• 2) unbiased selection

• 3) to equalize characteristics across experimental and control conditions

Terms

• Random selection - sample is representative of larger population

• Random assignment - involves equalizing experimental groupings (essential for internal validity of a study)

Sample Selection

• Two types:

• Probability sampling - sampling when the probability is known - rely on randomness

• Nonprobability sampling - probability is not known (e.g., purposive sampling, convenience sampling, quota sampling)

Probability Sampling

• Most unbiased but difficult method

• Can use:– Fishbowl technique (with or without

replacement)– Random number table– computer programs

Stratified Random Sampling

• Population is divided into various subgroups based on characteristics and then randomized

• may be more effective to achieve real representativeness

Systematic Sampling

• General procedure is to select every kth participant from the population

• If the population elements are in random order then this will be very representative of random sampling

Cluster Sampling

• Useful when normal random sampling is difficult

• Use a cluster of a group as a sample

• Can be misleading but is achievable and convenient

Nonprobability Sampling

• Using a convenience sample (e.g., a class)

• Purposive sampling - delimiting to a specific group on purpose

Matching

• Reduce error (improve equalization) across groups by matching certain characteristics in participants

• Useful for limited matching

How many participants?Revisiting sample size

• Becomes very important during statistical tests

• Power of the test is the ability to reject the null hypothesis

• Powerful studies (big N) can detect small differences between groups

Sample Attrition

• Need to record drop out rates

• Can really affect your results

• Generalizability back to the population

• Part of the solution is to demonstrate no significant differences between drop outs and adherers