What is a defining characteristic of cluster sampling?

A defining characteristic of cluster sampling is that the researcher identifies existing subgroups, or clusters, within the population before sampling. This method involves dividing the population into clusters, often based on geographic areas or naturally occurring groups, and then randomly selecting entire clusters for analysis rather than sampling individual members.

This approach is particularly useful when it is impractical or costly to do simple random sampling across the entire population. By focusing on clusters, researchers can efficiently gather data while still obtaining a representative sample of the overall population.

In contrast, the other options do not capture the essence of cluster sampling. Randomization as mentioned in one option refers more to the process of sampling without specifying the structure used, such as clusters. The idea of each individual being independently selected aligns more with simple random sampling rather than the inherently grouped nature of cluster sampling. Lastly, surveying all participants would contradict the principles of sampling, where a subset is examined to draw conclusions about the larger population.