Porównaj metody

	Wielopoziomowe ważone próbkowanie	Dobór próby skupiskowej
Dziedzina	Metodologia badań sondażowych	Metodologia badań sondażowych
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1960s–1980s (developed alongside large-scale survey programs)	Early-to-mid 20th century; canonical treatment 1953/1977
Twórca	Leslie Kish (probability sampling theory); complex survey methodologists	Formalized by William G. Cochran; roots in early 20th-century U.S. Census Bureau survey practice
Typ	Probability sampling design	Probability sampling design
Źródło pierwotne	Kish, L. (1965). Survey Sampling. John Wiley & Sons. New York. ISBN: 978-0471109495	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0471162407
Inne nazwy	hierarchical weighted sampling, nested weighted sampling, multilevel probability weighting, weighted hierarchical sampling	cluster random sampling, area sampling, one-stage cluster sampling
Pokrewne	6	5
Podsumowanie	Multi-level weighted sampling is a probability-based survey design that draws samples from hierarchically nested populations — such as students within classrooms within schools within districts — and assigns design weights at each level to account for unequal selection probabilities. The resulting weighted data enable unbiased population-level inference despite the complex, non-proportional structure of the sampling frame. It is the backbone of major international assessments such as PISA and TIMSS.	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.