방법 비교

	적응형 군집 표본 추출	포획-재포획 개체수 추정	Respondent-Driven Sampling
분야	조사방법론	조사방법론	조사방법론
계열	Process / pipeline	Regression model	Process / pipeline
기원 연도	1990	1978	1997
창시자	Steven Thompson	Otis, Burnham, White & Anderson	Douglas Heckathorn
유형	Probability-based adaptive design	Probabilistic population size estimator	Probabilistic chain-referral sampling design
원전	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Otis, D. L., Burnham, K. P., White, G. C., & Anderson, D. R. (1978). Statistical inference from capture data on closed animal populations. Wildlife Monographs, 62, 3–135.	Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems, 44(2), 174–199.
별칭	Adaptive Cluster Sampling, Sequential Adaptive Sampling, Network Sampling, Adaptif Küme Örneklemesi	Mark-Recapture, Tag-Recapture, Mark-Release-Recapture, İşaretle-Yeniden Yakala	Chain-Referral Sampling, Peer-Referral Sampling, Network-Based Sampling, Katılımcı Güdümlü Örnekleme
관련	3	2	3
요약	Adaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.	Capture-recapture (also known as mark-recapture) is a statistical method for estimating the size of an unknown population by sampling it twice and tracking which individuals appear in both samples. Formally systematized for closed animal populations by Otis, Burnham, White, and Anderson in their landmark 1978 Wildlife Monographs paper, the method extends naturally to human populations, epidemiology, and incomplete administrative records.	Respondent-Driven Sampling (RDS) is a probabilistic chain-referral method designed to reach hidden or hard-to-reach populations that lack a sampling frame. Introduced by sociologist Douglas Heckathorn in 1997, RDS combines snowball recruitment with mathematical weighting based on participants' personal network sizes, allowing researchers to generate population-level estimates even when no complete membership list exists.