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Lĩnh vựcPhương pháp luận khảo sátPhương pháp luận khảo sát
HọProcess / pipelineProcess / pipeline
Năm ra đờiEarly-to-mid 20th century; canonical treatment 1953/1977Mid-20th century (Cochran 1953; Kish 1965)
Người khởi xướngFormalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practiceWilliam G. Cochran; formalized in survey sampling theory
LoạiProbability sampling designProbability sampling design
Công trình gốcCochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Tên gọi kháccluster random sampling, area sampling, one-stage cluster samplinginterval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Liên quan55
Tóm tắtCluster 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.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.
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ScholarGateSo sánh phương pháp: Cluster Sampling · Systematic Sampling. Truy cập ngày 2026-06-15 từ https://scholargate.app/vi/compare