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

1. Cluster sampling involves dividing the population into distinct groups called clusters.
2. Clusters are often based on geographic or naturally occurring boundaries.
3. Once clusters are selected, all individuals within those clusters are included in the sample.
4. Cluster sampling can be more cost-effective and practical for large populations.
5. 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?

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.