What does cluster sampling involve?

Cluster sampling involves the process of dividing a population into separate groups, known as clusters, and then randomly selecting entire clusters for participation in the study. This sampling technique is particularly useful when a population is large and spread out over a wide geographical area, as it allows researchers to collect data more efficiently and cost-effectively by focusing on specific clusters rather than trying to sample individuals from the entire population.

By randomizing the selection of clusters, this method also helps ensure that the sample reflects the diversity of the population, as each cluster can contain various individuals with different characteristics. This distinct approach contrasts sharply with other sampling methods that might utilize a more uniform or convenience-based selection process. In summary, cluster sampling is an effective way to gather data from large populations while maintaining a level of randomness and representation.