Bandingkan metode

	Ranked Set Sampling	Sampling Klaster	Pengambilan Sampel Ganda	Sampel Bertingkat	Pencuplikan Sistematis
Bidang	Penarikan Sampel	Metodologi Survei	Penarikan Sampel	Metodologi Survei	Metodologi Survei
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1952	Early-to-mid 20th century; canonical treatment 1953/1977	1938	1977	Mid-20th century (Cochran 1953; Kish 1965)
Pencetus	Glenn A. McIntyre	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Jerzy Neyman	William G. Cochran	William G. Cochran; formalized in survey sampling theory
Tipe	Sampling design methodology	Probability sampling design	Multi-phase sampling design	Probability-based survey sampling design	Probability sampling design
Sumber perintis	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.). 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.). Wiley. ISBN: 978-0-471-16240-7	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Alias	RSS	cluster random sampling, area sampling, one-stage cluster sampling	Two-Phase Sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Terkait	4	5	4	2	5
Ringkasan	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.	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.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.	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.