방법 비교

	적응형 군집 표본 추출	다단계 표본 추출
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	1990	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
창시자	Steven K. Thompson	Leslie Kish; William G. Cochran
유형	Probability-based adaptive sampling design	Probability sampling design
원전	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
별칭	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
관련	6	5
요약	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.