Methoden vergleichen

	Adaptives Cluster-Sampling	Adaptive Stratified Sampling
Fachgebiet	Umfragemethodik	Umfragemethodik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990	1990s (formal development from Thompson 1990 onward)
Urheber	Steven K. Thompson	Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others
Typ	Probability-based adaptive sampling design	Probability-based adaptive sampling design
Wegweisende Quelle	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.
Aliasnamen	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling
Verwandt	6	6
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.	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.