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.
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Cluster sampling is often used when a population is large and geographically dispersed.
It can reduce travel and administrative costs compared to simple random sampling.
Clusters should ideally be heterogeneous internally but homogeneous between each other to reduce bias.
This method may introduce higher sampling error if the clusters are not representative of the population.
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?