Compara mètodes

	Mostreig per conglomerats	Mètode de mostreig per rang (Ranked Set Sampling, RSS)	Mètode d'ajustament per estrats
Camp	Metodologia d'enquestes	Mostreig	Metodologia d'enquestes
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	Early-to-mid 20th century; canonical treatment 1953/1977	1952	1977
Autor original	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice	Glenn A. McIntyre	William G. Cochran
Tipus	Probability sampling design	Sampling design methodology	Probability-based survey sampling design
Font seminal	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.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Àlies	cluster random sampling, area sampling, one-stage cluster sampling	RSS	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Relacionats	5	4	2
Resum	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.	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.