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Amostragem por Conglomerados Multinível×Amostragem Estratificada×
ÁreaMetodologia de surveyMetodologia de survey
FamíliaProcess / pipelineProcess / pipeline
Ano de origem1950s-1970s (cluster sampling); multilevel extension formalized 1980s-1990s1977
Autor originalW. G. Cochran (cluster sampling foundations); extended into multilevel contexts by survey methodologistsWilliam G. Cochran
TipoProbability sampling designProbability-based survey sampling design
Fonte seminalCochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Outros nomeshierarchical cluster sampling, nested cluster sampling, multi-stage cluster sampling, clustered multilevel samplingProportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Relacionados62
ResumoMulti-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.
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ScholarGateComparar métodos: Multi-level Cluster Sampling · Stratified Sampling. Recuperado em 2026-06-15 de https://scholargate.app/pt/compare