What describes cluster sampling?

Cluster sampling is a method used when researchers select participants from naturally occurring groups or clusters. In this approach, the population is divided into distinct groups, often based on geography or other natural divisions. Instead of sampling individuals from the entire population directly, researchers randomly select whole clusters. This technique is especially useful when the population is large and dispersed, allowing for more practical and cost-effective sampling.

Using cluster sampling allows researchers to reduce travel and administrative costs while still obtaining a representative sample. The natural grouping of participants means that the sample can capture the variations within the population, as each cluster can encapsulate a mix of characteristics.

The other methods listed do not align with the principles of cluster sampling. Random selection from a pool of individuals typically refers to simple random sampling. Choosing individuals based on researcher preference introduces bias and does not adhere to the principles of probability sampling. Completely random selection without structure is more akin to simple random sampling and lacks the organizational methodology inherent in cluster sampling.