Comparar métodos

	Muestreo estratificado adaptativo	Muestreo Estratificado Disproporcional
Campo	Metodología de encuestas	Metodología de encuestas
Familia	Process / pipeline	Process / pipeline
Año de origen	1990s (formal development from Thompson 1990 onward)	1934
Autor original	Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others	Jerzy Neyman
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.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Alias	ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling	disproportionate stratified sampling, unequal-probability stratified sampling, oversampling stratified design, non-proportional stratified sampling
Relacionados	6	6
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.	Disproportional stratified sampling divides the population into mutually exclusive strata and deliberately draws different proportions from each stratum — oversampling small or analytically important subgroups and undersampling large ones. Post-hoc weighting restores population-level representativeness when overall estimates are needed. First formalised by Jerzy Neyman in 1934, it is the standard approach when subgroup-level precision matters as much as total-population estimates.