Salīdzināt metodes

	Divu pakāpju izlase	Klasteru izlase	Sistemātiskā izlase
Nozare	Izlases veidošana	Aptauju metodoloģija	Aptauju metodoloģija
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	1938	Early-to-mid 20th century; canonical treatment 1953/1977	Mid-20th century (Cochran 1953; Kish 1965)
Autors	Jerzy Neyman	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	William G. Cochran; formalized in survey sampling theory
Tips	Multi-phase sampling design	Probability sampling design	Probability sampling design
Pirmavots	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	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Citi nosaukumi	Two-Phase Sampling	cluster random sampling, area sampling, one-stage cluster sampling	interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling
Saistītās	4	5	5
Kopsavilkums	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.	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.