Comparer des méthodes

	Échantillonnage adaptatif multi-stades	Échantillonnage en grappes
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1977 (multistage base); 1990-1992 (adaptive extensions by Thompson)	Early-to-mid 20th century; canonical treatment 1953/1977
Auteur d'origine	Steven K. Thompson (adaptive principles); William G. Cochran (multistage framework)	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Type	Probability-based adaptive sampling design	Probability sampling design
Source fondatrice	Thompson, S. K. (1992). Sampling. Wiley. ISBN: 978-0471548850	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Alias	AMS, adaptive multi-phase sampling, sequential multistage sampling, adaptive hierarchical sampling	cluster random sampling, area sampling, one-stage cluster sampling
Apparentées	5	5
Résumé	Adaptive multistage sampling combines the hierarchical efficiency of multistage designs with adaptive decision rules that adjust which units are sampled at later stages based on what is observed at earlier stages. It is used when a target characteristic is rare, clustered, or spatially heterogeneous and a fixed design would waste resources on uninformative areas of the population.	Cluster sampling is a probability sampling technique in which the population is divided into naturally occurring groups (clusters), a random sample of clusters is selected, and all — or a random subset of — members within each selected cluster are studied. It is especially practical when a complete population list is unavailable or when units are geographically dispersed, making individual random selection prohibitively expensive. One-stage cluster sampling surveys every member of selected clusters; two-stage designs add a second random draw within clusters.