Porovnat metody

	Řadové výběrové šetření	Náhodný výběr shluků	Dvojí výběr
Obor	Výběr vzorku	Metodologie dotazníkových šetření	Výběr vzorku
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1952	Early-to-mid 20th century; canonical treatment 1953/1977	1938
Tvůrce	Glenn A. McIntyre	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Jerzy Neyman
Typ	Sampling design methodology	Probability sampling design	Multi-phase sampling design
Původní zdroj	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.
Další názvy	RSS	cluster random sampling, area sampling, one-stage cluster sampling	Two-Phase Sampling
Příbuzné	4	5	4
Shrnutí	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.