विधियों की तुलना करें

	अनुकूली क्लस्टर नमूनाकरण	स्नोबॉल सैंपलिंग
क्षेत्र	सर्वेक्षण पद्धति	सर्वेक्षण पद्धति
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1990	1961
प्रवर्तक	Steven K. Thompson	Leo A. Goodman
प्रकार	Probability-based adaptive sampling design	Non-probability sampling technique
मौलिक स्रोत	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.
उपनाम	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	chain-referral sampling, network sampling, respondent-driven sampling, referral sampling
संबंधित	6	3
सारांश	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.