Which sampling method identifies specific subgroups instead of individual participants?

Cluster sampling is the method that identifies specific subgroups rather than individual participants. This technique involves dividing a population into separate groups, known as clusters, which are often based on geographical or natural groupings. The researcher then randomly selects entire clusters to include in the sample.

The advantage of this approach is that it can be more practical and cost-effective, especially when dealing with large populations spread over a wide area. By focusing on clusters, researchers can gather data from multiple individuals within those clusters while only needing to draw a small number of these groups.

In contrast, methods such as systematic sampling and simple random sampling focus on selecting individual participants from the population, either in a random manner or at fixed intervals. Non-probability sampling, while it may select subgroups, does not do so systematically and typically lacks the random element that characterizes cluster sampling. Thus, cluster sampling is specifically designed to hone in on subgroups within a broader population framework.