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 included in the final sample.

5 Must Know Facts For Your Next Test

1. Cluster sampling is often used when a population is large and geographically dispersed.
2. It can reduce travel and administrative costs compared to simple random sampling.
3. Clusters should ideally be heterogeneous internally but homogeneous between each other to reduce bias.
4. This method may introduce higher sampling error if the clusters are not representative of the population.
5. Cluster sampling can be conducted in one-stage (selecting all members from chosen clusters) or two-stage (further sub-sampling within chosen clusters).

Review Questions

• What are two main reasons for using cluster sampling?
• How does cluster sampling differ from stratified sampling?
• What are potential sources of bias in cluster sampling?

Related terms

Simple Random Sample: A sampling method where every individual has an equal chance of being selected.

Stratified Sample: A sampling method where the population is divided into strata, and random samples are taken from each stratum.

Systematic Sample: A sampling method where every k-th element in a list is selected after a random starting point.