Salīdzināt metodes

	Adaptīvā kopu izlase	Klasteru izlase
Nozare	Aptauju metodoloģija	Aptauju metodoloģija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1990	Early-to-mid 20th century; canonical treatment 1953/1977
Autors	Steven K. Thompson	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Tips	Probability-based adaptive sampling design	Probability sampling design
Pirmavots	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.). Wiley. ISBN: 978-0471162407
Citi nosaukumi	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling	cluster random sampling, area sampling, one-stage cluster sampling
Saistītās	6	5
Kopsavilkums	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.	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.