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Population Sampling in Research

Population Sampling in Research

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Population Sampling in Research

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  1. Population Sampling in Research PE 357

  2. 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

  3. 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)

  4. 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

  5. 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

  6. Critical Factor • The sample needs to be representative of your population of interest • Generalizability (external validity) of your results is dependent on this factor!

  7. Randomization • 1) ensures representativeness • 2) unbiased selection • 3) to equalize characteristics across experimental and control conditions

  8. Terms • Random selection - sample is representative of larger population • Random assignment - involves equalizing experimental groupings (essential for internal validity of a study)

  9. 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)

  10. Probability Sampling • Most unbiased but difficult method • Can use: • Fishbowl technique (with or without replacement) • Random number table • computer programs

  11. Stratified Random Sampling • Population is divided into various subgroups based on characteristics and then randomized • may be more effective to achieve real representativeness

  12. 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

  13. 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

  14. Nonprobability Sampling • Using a convenience sample (e.g., a class) • Purposive sampling - delimiting to a specific group on purpose

  15. Matching • Reduce error (improve equalization) across groups by matching certain characteristics in participants • Useful for limited matching

  16. 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

  17. 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