Which sampling method involves selecting entire clusters, such as classrooms, and then sampling within those clusters?

• A. Simple random sampling
• B. Stratified sampling
• C. Cluster sampling
• D. Systematic sampling

Answer: (C)

This method uses intact groups as the primary units of selection, then collects data from members within those chosen groups. You start by identifying clusters that already exist, like classrooms or neighborhoods, then you pick some of those clusters and sample within them. It’s a practical approach when it’s easier to work with groups than with individuals scattered across a broad area, and it can save time and resources while still giving representative data if the clusters are similar to the whole population.

Why this fits your scenario: selecting entire clusters (classrooms) and then sampling inside those clusters is exactly how this method operates—you don’t randomize individuals from the entire population at once, but rather focus on whole groups and then sample inside them.

In contrast, simple random sampling would involve picking individuals at random from the entire population without using groups; stratified sampling would divide the population into strata and sample from each stratum to ensure representation across groups; systematic sampling would select every kth individual from a list.