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Muestreo doble×Muestreo por conglomerados×Muestreo por Conjuntos Ordenados×
CampoMuestreoMetodología de encuestasMuestreo
FamiliaProcess / pipelineProcess / pipelineProcess / pipeline
Año de origen1938Early-to-mid 20th century; canonical treatment 1953/19771952
Autor originalJerzy NeymanFormalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practiceGlenn A. McIntyre
TipoMulti-phase sampling designProbability sampling designSampling design methodology
Fuente seminalNeyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association, 33(201), 101–116. DOI ↗Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407McIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390. DOI ↗
AliasTwo-Phase Samplingcluster random sampling, area sampling, one-stage cluster samplingRSS
Relacionados454
ResumenDouble Sampling (also called two-phase or multistage sampling) is a survey design in which a large preliminary sample is collected using inexpensive methods or partial information, then a smaller subsample is drawn from it and measured in detail. Pioneered by Jerzy Neyman in 1938, it is particularly useful when a cheap surrogate measurement is available but true measurement is expensive.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.Ranked Set Sampling (RSS) is a data collection method introduced by G. A. McIntyre in 1952 that improves estimation efficiency when visual ranking of units is easier or cheaper than actual measurement. By deliberately selecting and measuring units that are ranked as most likely to yield desired outcomes, RSS reduces variance compared to simple random sampling while maintaining unbiasedness.
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ScholarGateComparar métodos: Double Sampling · Cluster Sampling · Ranked Set Sampling. Recuperado el 2026-06-17 de https://scholargate.app/es/compare