Comparar métodos

	Muestreo estratificado adaptativo	Muestreo multietápico
Campo	Metodología de encuestas	Metodología de encuestas
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s (formal development from Thompson 1990 onward)	1950s–1960s (formalized in Kish 1965 and Cochran 1977)
Autor original	Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others	Leslie Kish; William G. Cochran
Tipo	Probability-based adaptive sampling design	Probability sampling design
Fuente seminal	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Kish, L. (1965). Survey Sampling. John Wiley & Sons. ISBN: 978-0471109495
Alias	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling	multistage cluster sampling, multi-stage sampling, nested sampling, hierarchical sampling
Relacionados	6	5
Resumen	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.	Multistage sampling is a probability-based design that selects a sample by working through two or more successive levels of a population hierarchy — for example, first selecting regions, then districts within those regions, then households within those districts. It makes large-scale surveys practical when a complete population list is unavailable or when the population is geographically dispersed, by concentrating fieldwork within a manageable number of sampled units at each stage.