방법 비교

	적응형 군집 표본 추출	체계적 표본 추출
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	1990	Mid-20th century (Cochran 1953; Kish 1965)
창시자	Steven K. Thompson	William G. Cochran; formalized in survey sampling theory
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.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
별칭	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval 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.	Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.