Comparar métodos

	Muestreo Estratificado Multinivel	Muestreo por conglomerados
Campo	Metodología de encuestas	Metodología de encuestas
Familia	Process / pipeline	Process / pipeline
Año de origen	1950s–1970s	Early-to-mid 20th century; canonical treatment 1953/1977
Autor original	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
Tipo	Probability sampling design	Probability sampling design
Fuente seminal	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
Alias	hierarchical stratified sampling, nested stratified sampling, multilevel stratified design, stratified multilevel sampling	cluster random sampling, area sampling, one-stage cluster sampling
Relacionados	6	5
Resumen	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.