KINE 2049 Lecture Notes - Lecture 8: Multistage Sampling, Systematic Sampling, Nonprobability Sampling

• School: York University
• Department: Kinesiology & Health Science
• Course: KINE 2049
• Professor: Merv Mosher

Document Summary

Sample size: generally a large sample produces better data and therefore more valid conclusions, the (cid:272)riti(cid:272)al fa(cid:272)tor is (cid:449)hether or (cid:374)ot the is represe(cid:374)tati(cid:448)e of the , sample size is also important, conventional wisdom n>30. Selection methods for probability sampling: random selection, every subject has an equal chance of being selected, the selection of one does not bias the chance of others, simple random sampling, ex. Is widely scattered: multi-stage sampling, same as cluster w/ extra step random sampling in the cluster. Selection methods for non-probability sampling: deliberate/purposive sampling, selects subjects w/ specific characteristics, convenience sampling, subjects are accessible and convenient for the researcher to contact, (cid:862)a(cid:272)(cid:272)ide(cid:374)tal(cid:863) sa(cid:373)pli(cid:374)g, self-selected/volunteers. Reviewing the research literature: help you turn research idea into an interesting research question tell you if a research idea into an interesting research question. General purposes: develop new ideas to study the topic, utilize pre(cid:448)ious resear(cid:272)her"s re(cid:272)o(cid:373)(cid:373)e(cid:374)datio(cid:374)s.