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Adaptiv klyngeudvælgelse×Flerstadieudvælgelse×
FagområdeSurveymetodologiSurveymetodologi
FamilieProcess / pipelineProcess / pipeline
Oprindelsesår19901950s–1960s (formalized in Kish 1965 and Cochran 1977)
OphavspersonSteven K. ThompsonLeslie Kish; William G. Cochran
TypeProbability-based adaptive sampling designProbability sampling design
Oprindelig kildeThompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495
AliasserACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive samplingmultistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
Relaterede65
ResuméAdaptive 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.
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ScholarGateSammenlign metoder: Adaptive Cluster Sampling · Multistage Sampling. Hentet 2026-06-15 fra https://scholargate.app/da/compare