A market researcher would move step by step from large geographic units down to individual consumers, randomly sampling at each stage to keep the process practical yet still representative of the whole state.

Step‑by‑step multistage cluster plan

Imagine the goal is: “Sample consumers in all cities with population >10,000 in a large state.”

Stage 1 – Select cities (first‑level clusters)

  • List all cities in the state with population over 10,000.
  • Use random sampling to select a subset of these cities; these selected cities are the first‑stage clusters.
  • This immediately reduces travel and data‑collection costs, while still covering different parts of the state.

Stage 2 – Select city blocks within cities

  • In each chosen city, divide the city into many city blocks (these are the second‑level clusters).
  • Randomly select a number of blocks in each sampled city; these are the clusters typically used in marketing research because they are compact and easy to work in.

Stage 3 – Select households within blocks

  • For each selected block, create or obtain a list of addresses or dwellings.
  • Use simple random sampling or systematic sampling (for example, “every 3rd household on the list”) to pick a set of households from each block rather than interviewing everyone there.

Stage 4 – Select individuals within households

  • At each sampled household, if more than one eligible consumer lives there, randomly select one adult (for example, “the household member with the most recent birthday”) to be interviewed.
  • This last step keeps the final unit of analysis clearly defined and avoids bias toward larger households.

Why this is multistage cluster sampling

  • It is cluster sampling because the population is naturally grouped into geographic clusters (cities, then blocks) and the researcher samples by clusters instead of individuals scattered everywhere.
  • It is multistage because the sampling happens in several levels:
    1. Cities selected.
    2. Blocks within those cities selected.
    3. Households within blocks selected.
    4. Individuals within households selected.

In practice, this design lets a marketing researcher reach a large, geographically spread consumer population in a state efficiently, while still maintaining randomness and statistical validity at each stage.

TL;DR:
Start by randomly picking eligible cities, then randomly choose blocks in those cities, then randomly sample households in each selected block, and finally randomly select one consumer in each sampled household to survey. This multistage approach uses city blocks as intermediate clusters to make statewide consumer research feasible and cost‑effective.

Information gathered from public forums or data available on the internet and portrayed here.