Comparer des méthodes

	Échantillonnage pondéré multi-niveaux	Échantillonnage pondéré
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1960s–1980s (developed alongside large-scale survey programs)	1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)
Auteur d'origine	Leslie Kish (probability sampling theory); complex survey methodologists	Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)
Type	Probability sampling design	Probability sampling design
Source fondatrice	Kish, L. (1965). Survey Sampling. John Wiley & Sons. New York. ISBN: 978-0471109495	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Alias	hierarchical weighted sampling, nested weighted sampling, multilevel probability weighting, weighted hierarchical sampling	probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling
Apparentées	6	6
Résumé	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.	Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient.