方法对比

	自适应整群抽样	自适应分层抽样
领域	调查方法论	调查方法论
方法族	Process / pipeline	Process / pipeline
起源年份	1990	1990s (formal development from Thompson 1990 onward)
提出者	Steven K. Thompson	Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others
类型	Probability-based adaptive sampling design	Probability-based adaptive sampling design
开创性文献	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
别名	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling
相关	6	6
摘要	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.	Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present.