Módszerek összehasonlítása

	Többszintű rétegzett mintavétel	Cluster Sampling
Tudományterület	Kérdőíves felmérések módszertana	Kérdőíves felmérések módszertana
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	1950s–1970s	Early-to-mid 20th century; canonical treatment 1953/1977
Megalkotó	Formalized by Leslie Kish and William G. Cochran in the mid-20th century survey sampling literature	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Típus	Probability sampling design	Probability sampling design
Alapmű	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Alternatív nevek	hierarchical stratified sampling, nested stratified sampling, multilevel stratified design, stratified multilevel sampling	cluster random sampling, area sampling, one-stage cluster sampling
Kapcsolódó	6	5
Összefoglaló	Multi-level stratified sampling applies stratification at two or more hierarchical levels of a nested population structure — for example, first stratifying geographic regions, then stratifying schools within each region, then stratifying classrooms within each school. This layered control over the composition of the sample at every level reduces variance and supports analysis at each level of the hierarchy, making it a powerful design for large-scale educational, epidemiological, and organizational surveys.	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.