Comparar métodos

	Muestreo Adaptativo por Conglomerados	Muestreo estratificado adaptativo
Campo	Metodología de encuestas	Metodología de encuestas
Familia	Process / pipeline	Process / pipeline
Año de origen	1990	1990s (formal development from Thompson 1990 onward)
Autor original	Steven K. Thompson	Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others
Tipo	Probability-based adaptive sampling design	Probability-based adaptive sampling design
Fuente seminal	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.
Alias	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling
Relacionados	6	6
Resumen	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.