HPED 2030 Lecture Notes - Lecture 13: Stratified Sampling, Sample Size Determination, Type I And Type Ii Errors

• School: Mount Royal University
• Department: Health and Physical Education
• Course: HPED 2030
• Professor: Sommer Christie

Document Summary

Sampling: population all-inclusive group, defined by researcher, key trait of population being studied, sample representative subset of the population, contains key traits of the population. Probability sampling (random selection: simple/random sampling, selection probability equal for each member, use random number table or computer. Impartial and unbiased: systematic sampling, every xth member selected, stratified sampling, ensure subgroups fairly represented, males/females - order population and then randomly sample within each group. Non-probability sampling (not equal opportunity: convenience sampling, takes subjects wherever can be found, very convenient for researcher, quota sampling, ensures subgroups represented from wherever you can find them. Practice: random sampling is not always possible, sometimes difficult to obtain true random sample. Generalizability: weird western, educated, industrialized, rich, democratic, reader must decide whether legitimate inferences or generalizations can be made. Purposive sampling (judgement sample: preselected criteria based on the research question, sample size may or may not be fixed (ex.