Multi-Stage Sampling
Updated 2026-09-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Multi-stage sampling is a probability design in which the population is divided into successive levels and units are selected step by step, for example provinces, then counties within selected provinces, then households within selected counties. Each stage uses its own sampling frame, which makes the method practical when no complete list of final units exists. The qualification is that clustering raises variance relative to simple random sampling, so analysis must account for the design effect and stage-specific selection probabilities.
SCAFFOLDING EFFECT
Reduce cognitive load
- Use Feasibility Trade-off: pick stages that match the frames you can actually obtain. - Use Variance Planning: budget for larger samples because clustering inflates standard errors.
Anchor fast decisions
Building a complete frame of every final unit is often impossible or ruinously expensive, but frames of large clusters usually exist. Sampling clusters first concentrates field effort in a few locations, and nesting later stages inside them cuts travel and listing costs at the price of correlated observations within each cluster.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- wiki.mbalib.comhttps://wiki.mbalib.com/wiki/%E5%A4%9A%E9%98%B6%E6%AE%B5%E6%8A%BD%E6%A0%B7verified
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