השוואת שיטות

	דגימת אשכולות אדפטיבית (Adaptive Cluster Sampling	דגימה רב-שלבית
תחום	מתודולוגיית סקרים	מתודולוגיית סקרים
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1990	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
הוגה השיטה	Steven K. Thompson	Leslie Kish; William G. Cochran
סוג	Probability-based adaptive sampling design	Probability sampling design
מקור מכונן	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495
כינויים	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
קשורות	6	5
תקציר	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.	Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage.