Class Notes (1,100,000)
US (470,000)
UMN (4,000)
KIN (20)
Alex K (10)
Lecture 9

KIN 3982 Lecture Notes - Lecture 9: Cluster Sampling, Snowball Sampling

Course Code
KIN 3982
Alex K

This preview shows half of the first page. to view the full 2 pages of the document.
Basic Terms and Concepts
Population: complete set of people with a specified set of characteristics
Unbiased sample: potential participants have an equal chance of being
Convenience samples: people who meet criteria and who are easily
o Advantages are cost and logistics
Study sample: subset of the accessible population who participate in the
Selection Criteria
Studies should define the target population
Inclusion criteria: main characteristics of the target population that pertain
to the research question
Exclusion criteria: subset of individuals who are excluded due to:
o Potential lack of success for follow-up rates
o Poor quality of the data
o Acceptability of randomized treatment
o Safety concerns
Sampling Errors
Two main ways of reducing sampling error:
o Make sample more homogenous (e.g., random sampling stratified
sample) or
o Increase sample size
Study sample is often biased due to self-selection into study
Consider generalizability of your sample
Stratified Random Sampling
Divide the population into groups based on identifiable criteria (e.g., gender)
Select participants randomly from each stratum
Use an equal percentage (not absolute number) of participants from each
o Ex., exercise intervention with men and women
Two goals:
o Recruit a sample that adequately represents the target population
o Recruit enough participants to meet the sample size requirements
Main types of recruitment methods
You're Reading a Preview

Unlock to view full version