Salīdzināt metodes

	Ranga kopu izlase	Klasteru izlase	Stratificētā izlase
Nozare	Izlases veidošana	Aptauju metodoloģija	Aptauju metodoloģija
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	1952	Early-to-mid 20th century; canonical treatment 1953/1977	1977
Autors	Glenn A. McIntyre	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	William G. Cochran
Tips	Sampling design methodology	Probability sampling design	Probability-based survey sampling design
Pirmavots	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	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Citi nosaukumi	RSS	cluster random sampling, area sampling, one-stage cluster sampling	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Saistītās	4	5	2
Kopsavilkums	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.	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.