Cluster sample
Definition
A cluster sample is a sampling method where the population is divided into groups, or clusters, and a random sample of these clusters is selected. All members of the chosen clusters are then included in the sample.
5 Must Know Facts For Your Next Test
- Cluster sampling involves dividing the population into distinct groups called clusters.
- Clusters are often based on geographic or naturally occurring boundaries.
- Once clusters are selected, all individuals within those clusters are included in the sample.
- Cluster sampling can be more cost-effective and practical for large populations.
- It differs from stratified sampling, where samples are drawn from each stratum rather than entire groups.
Review Questions
- What is a key difference between cluster sampling and stratified sampling?
- Why might researchers choose to use cluster sampling over other methods?
- How are clusters typically formed in cluster sampling?
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Related terms
Simple Random Sample: A sampling method where every member of the population has an equal chance of being selected.
Stratified Sample: A sampling method that divides the population into subgroups (strata) and takes a random sample from each subgroup.
Systematic Sample: A sampling method where every nth member of a population is selected after a random starting point.
