Comparer des méthodes

	Échantillonnage pondéré multi-niveaux	Échantillonnage stratifié proportionnel
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1960s–1980s (developed alongside large-scale survey programs)	1953–1965 (formalized in survey sampling literature)
Auteur d'origine	Leslie Kish (probability sampling theory); complex survey methodologists	William G. Cochran; Leslie Kish
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	proportionate stratified sampling, proportional allocation stratified sampling, PSRS, proportionate stratified random 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.	Proportional stratified sampling divides the target population into non-overlapping strata (subgroups defined by a key characteristic such as age band, region, or gender) and then draws a simple random sample from each stratum so that each stratum's share of the total sample matches its share of the total population. Because each subgroup is represented in exact proportion to its population weight, the resulting sample mirrors the population structure closely without requiring post-hoc weighting adjustments.