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Adaptacyjne Próbkowanie Klastrowe×Badanie ekologiczne×
DziedzinaMetodologia badań sondażowychEpidemiologia
RodzinaProcess / pipelineProcess / pipeline
Rok powstania199019th century (Snow 1854); formalised mid-20th century
TwórcaSteven ThompsonVarious; foundational work by John Snow (1854) and systematised in modern form by Brian MacMahon and colleagues
TypProbability-based adaptive designObservational epidemiological study
Źródło pierwotneThompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗Morgenstern, H. (1995). Ecologic studies in epidemiology: concepts, principles, and methods. Annual Review of Public Health, 16(1), 61–81. DOI ↗
Inne nazwyAdaptive Cluster Sampling, Sequential Adaptive Sampling, Network Sampling, Adaptif Küme Örneklemesiaggregate study, correlational study, ecological correlation study, population-level study
Pokrewne35
PodsumowanieAdaptive Cluster Sampling (ACS) is a probability-based survey design introduced by Steven K. Thompson in 1990 for estimating the abundance or total of rare, clustered populations. Starting from an initial random sample, the design adaptively adds neighboring units whenever a sampled unit satisfies a predefined condition—such as exceeding a count threshold—thereby concentrating sampling effort exactly where the population of interest occurs. It is most appropriate for ecologists, epidemiologists, and social scientists studying geographically or socially clustered rare phenomena.An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or evaluate hypotheses about population-level associations between risk factors and disease.
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ScholarGatePorównaj metody: Adaptive Sampling · Ecological Study. Pobrano 2026-06-18 z https://scholargate.app/pl/compare