How is multistage sampling different from cluster sampling?

• A. Multistage sampling uses multiple stages of drawing lower-level groups from higher-level groups
• B. They are the same method
• C. Multistage sampling uses only single stage
• D. Multistage sampling avoids clustering altogether

Answer: (A)

Multistage sampling is about sampling through several steps, moving from broader units to narrower ones at each stage. At each step you draw a random sample of the next-level units within the units you’ve already selected, so you end up with individuals through a chain of selections. For example, you might first pick school districts, then within those districts pick schools, then within those schools pick classrooms or students. This layered approach is exactly what the description conveys: multiple stages of drawing lower-level groups from higher-level groups.

This differs from simple cluster sampling, where the process starts by dividing the population into clusters and selecting some clusters to study. Within those chosen clusters you may survey everyone or take a further sampling stage, but the defining idea is starting with clusters as the primary units. The multistage approach generalizes that idea to several levels, rather than stopping after the first clustering.

So the best description is that multistage sampling uses multiple stages of drawing lower-level groups from higher-level groups.