Which sampling method involves dividing the population into groups (clusters) and then selecting a random sample of clusters?

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

Answer: (C)

Dividing the population into groups (clusters) and then randomly selecting a sample of those clusters is cluster sampling. The key idea is to take advantage of natural groupings to make data collection easier and cheaper: you pick a few clusters at random and study all members within the chosen clusters (one-stage) or sample within them (two-stage). This approach is especially useful when a complete listing of individuals is impractical but lists of clusters are available.

This method differs from stratified sampling, where the population is split into homogeneous subgroups (strata) and you sample from each stratum to ensure representation across the whole population. It also differs from systematic sampling, which selects units using a fixed interval (like every kth person on a list), and from simple random sampling, where individuals are chosen purely by chance without regard to any grouping.