Cluster sampling is useful if your population is particularly large or generic. When using this method, you take a random sample or stratified sample from within a cluster of the population.
For example, if you’re studying students participating in Greek Life in universities across the United States, you might choose to narrow it down to a sub-sample, or a cluster, of a single school’s Greek system, and then take a simple random sample or stratified sample from within that cluster. Ultimately, cluster sampling helps you keep your sample size feasible while remaining representative of the population.
Watch this video from Rahul Patwari to get an understanding of how cluster sampling works.
Let’s review some of the pros and cons of cluster sampling.
| Pros | Cons |
|---|---|
| More cost efficient than other sampling methods | Less precise than other sampling methods |
| Allows for more generalizations because of the random nature of sampling | |
| Faster than other methods |
