방법 비교

	적응형 군집 표본 추출	스노우볼 표본 추출
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	1990	1961
창시자	Steven K. Thompson	Leo A. Goodman
유형	Probability-based adaptive sampling design	Non-probability sampling technique
원전	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170.
별칭	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	chain-referral sampling, network sampling, respondent-driven sampling, referral sampling
관련	6	3
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.	Snowball sampling is a non-probability recruitment technique in which initial participants (seeds) refer the researcher to others who meet the study criteria, and those referrals in turn refer further participants. The sample grows incrementally — like a rolling snowball — until the required size or theoretical saturation is reached. It is the method of choice when a target population has no accessible sampling frame, such as undocumented migrants, illicit drug users, survivors of stigmatised experiences, or members of closed professional networks.