方法对比

	簇抽样	有序集合抽样	系统抽样
领域	调查方法论	抽样	调查方法论
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	Early-to-mid 20th century; canonical treatment 1953/1977	1952	Mid-20th century (Cochran 1953; Kish 1965)
提出者	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Glenn A. McIntyre	William G. Cochran; formalized in survey sampling theory
类型	Probability sampling design	Sampling design methodology	Probability sampling design
开创性文献	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407	McIntyre, G. A. (1952). A method for unbiased selective sampling using ranked sets. Australian Journal of Agricultural Research, 3(4), 385–390.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
别名	cluster random sampling, area sampling, one-stage cluster sampling	RSS	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
相关	5	4	5
摘要	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.	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.	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.