Methoden vergleichen

	Adaptives Cluster-Sampling	Mehrstufige Stichprobenziehung – Mehrstufige Klumpenstichprobenziehung
Fachgebiet	Umfragemethodik	Umfragemethodik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
Urheber	Steven K. Thompson	Leslie Kish; William G. Cochran
Typ	Probability-based adaptive sampling design	Probability sampling design
Wegweisende Quelle	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
Aliasnamen	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
Verwandt	6	5
Zusammenfassung	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.