方法对比

	自适应整群抽样	多阶段抽样
领域	调查方法论	调查方法论
方法族	Process / pipeline	Process / pipeline
起源年份	1990	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
提出者	Steven K. Thompson	Leslie Kish; William G. Cochran
类型	Probability-based adaptive sampling design	Probability sampling design
开创性文献	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495
别名	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
相关	6	5
摘要	Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups.	Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage.