Comparer des méthodes

	Échantillonnage pondéré de cas typiques	Échantillonnage pondéré
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (as a mixed-methods extension)	1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)
Auteur d'origine	Derived from Patton's typical case sampling (1990) combined with classical survey weighting principles	Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)
Type	Purposive sampling with probability weighting	Probability sampling design
Source fondatrice	Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage. pp. 236–238 (typical case sampling). ISBN: 978-0761919711	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
Alias	weighted purposive typical sampling, probability-weighted typical case selection, typical case sampling with weighting, weighted representative case sampling	probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling
Apparentées	6	6
Résumé	Weighted typical case sampling combines the purposive logic of typical case selection — choosing cases that represent the modal, average, or most common profile of a population — with post-selection probability weighting. The result is a sample that is both substantively representative (cases reflect the norm) and statistically corrected for differential selection probabilities or population structure. It is used in mixed-methods and survey research where depth of typical examples matters alongside inferential accuracy.	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.