What is cluster sampling best defined as?

Cluster sampling is best defined as a sampling technique that uses clusters of participants because it involves dividing the population into distinct groups (or clusters) and then randomly selecting entire clusters for the sample, rather than selecting individual members throughout the entire population. This approach is particularly useful in situations where a population is large and geographically dispersed, making it more practical to study entire clusters rather than gathering individuals from across the entire population.

In this technique, once the clusters are formed, a random selection process is applied to choose which of these clusters will be included in the sample. Within the selected clusters, researchers may then collect data from all individuals or a random sample from those within the clusters. This method helps to simplify the sampling process and reduce logistical challenges, such as costs and time involved in reaching scattered individuals throughout the population.

While cluster sampling does involve dividing a population into groups—a characteristic that some other sampling methods may share—its unique aspect lies in the random selection of entire clusters rather than individual units or using a stratified approach. Thus, it is the methodology of choosing whole clusters that distinguishes it in the context of sampling techniques.