手法を比較

	クラスター抽出法	二重標本抽出法	系統抽出法
分野	調査方法論	標本抽出	調査方法論
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	Early-to-mid 20th century; canonical treatment 1953/1977	1938	Mid-20th century (Cochran 1953; Kish 1965)
提唱者	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Jerzy Neyman	William G. Cochran; formalized in survey sampling theory
種類	Probability sampling design	Multi-phase sampling design	Probability sampling design
原典	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	Neyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association, 33(201), 101–116.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
別名	cluster random sampling, area sampling, one-stage cluster sampling	Two-Phase Sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
関連	5	4	5
概要	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.	Double 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.	Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.