What sampling method involves the selection of entire clusters or groups instead of individual participants?

The choice of cluster sampling as the correct sampling method is founded on its distinctive approach to data collection. In cluster sampling, the population is divided into distinct groups, or clusters, and then entire clusters are randomly selected for inclusion in the sample. This method is particularly beneficial when the population is large and geographically dispersed, as it can significantly reduce the time and costs associated with data collection by allowing researchers to gather information from fewer, but comprehensive, groups.

The efficiency of cluster sampling lies in its ability to attain a sample that is representative of the overall population through selected clusters, thereby maintaining the variability and diversity inherent within each group. By focusing on groups rather than individuals, researchers can capture necessary data without the logistical challenges that often accompany individual sampling.

Other sampling methods like stratified sampling, simple random sampling, and systematic sampling each emphasize different strategies. Stratified sampling divides the population into subgroups based on characteristics before sampling, while simple random sampling ensures that each individual has an equal chance of being selected, and systematic sampling employs a fixed interval method for selecting participants. However, none of these methods involve selecting entire clusters as cluster sampling does. Thus, cluster sampling is uniquely tailored for scenarios where grouping reduces the complexity and cost of gathering data.