방법 비교

	적응형 스노우볼 샘플링	적응형 군집 표본 추출
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	1990s–2000s (as combined approach)	1990
창시자	Combines principles from S. K. Thompson (adaptive sampling, 1990) and L. A. Goodman (snowball sampling, 1961)	Steven K. Thompson
유형	Non-probability / adaptive sampling design	Probability-based adaptive sampling design
원전	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
별칭	adaptive referral sampling, adaptive chain-referral sampling, dynamic snowball sampling	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling
관련	4	6
요약	Adaptive snowball sampling is a hybrid sampling strategy that recruits initial participants (seeds) from a target population and then dynamically adjusts referral chains based on pre-specified criteria — such as population density, diversity, or theoretical saturation. Combining the chain-referral logic of snowball sampling with the responsive decision rules of adaptive sampling, it is particularly suited to studying rare, hidden, or hard-to-reach populations where conventional frames are unavailable.	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.