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Adaptieve Cluster Sampling×Gestratificeerde steekproeftrekking×
VakgebiedSurveymethodologieSurveymethodologie
FamilieProcess / pipelineProcess / pipeline
Jaar van ontstaan19901977
GrondleggerSteven K. ThompsonWilliam G. Cochran
TypeProbability-based adaptive sampling designProbability-based survey sampling design
Oorspronkelijke bronThompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
AliassenACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive samplingProportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Verwant62
SamenvattingAdaptive 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.Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.
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ScholarGateMethoden vergelijken: Adaptive Cluster Sampling · Stratified Sampling. Geraadpleegd op 2026-06-15 via https://scholargate.app/nl/compare