Comparar métodos

	Muestreo por conglomerados multinivel	Muestreo Estratificado
Campo	Metodología de encuestas	Metodología de encuestas
Familia	Process / pipeline	Process / pipeline
Año de origen	1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s	1977
Autor original	W. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologists	William G. Cochran
Tipo	Probability sampling design	Probability-based survey sampling design
Fuente seminal	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Alias	hierarchical cluster sampling, nested cluster sampling, multi-stage cluster sampling, clustered multilevel sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Relacionados	6	2
Resumen	Multi-level cluster sampling is a probability sampling design for hierarchically structured populations — such as students nested within classrooms within schools within districts. Clusters are randomly selected at each level of the hierarchy before individual units are sampled within the final-level clusters. The design mirrors the natural nesting of real-world populations and enables efficient large-scale data collection while supporting multilevel statistical analysis.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.