방법 비교

	이중 표본 추출	군집 표본 추출	순위 집합 표본 추출
분야	표본추출	조사방법론	표본추출
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	1938	Early-to-mid 20th century; canonical treatment 1953/1977	1952
창시자	Jerzy Neyman	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Glenn A. McIntyre
유형	Multi-phase sampling design	Probability sampling design	Sampling design methodology
원전	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.). 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.
별칭	Two-Phase Sampling	cluster random sampling, area sampling, one-stage cluster sampling	RSS
관련	4	5	4
요약	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.	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.