Comparer des méthodes

	Analyse Factorielle Confirmatoire	Modélisation Linéaire Hiérarchique (HLM / Modélisation Multiniveaux)
Domaine	Psychométrie	Statistique
Famille	Latent structure	Hypothesis test
Année d'origine	1969	1986
Auteur d'origine	Karl Jöreskog	Raudenbush & Bryk (popularized); Goldstein (parallel development)
Type	Measurement model / latent variable analysis	Parametric nested-data regression
Source fondatrice	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press. ISBN: 978-1462515363	Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049
Alias	Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA), confirmatory factor analysis, measurement model testing	HLM, MLM, multilevel modeling, multilevel analysis
Apparentées	6	4
Résumé	Confirmatory factor analysis is a measurement modelling technique that tests whether a hypothesised factor structure — typically derived from theory or an earlier exploratory analysis — fits observed data from a new sample. Developed by Karl Jöreskog in 1969, it became the dominant tool for validating psychological scales because it requires the researcher to specify in advance which items belong to which latent factor and then assesses the adequacy of that specification against explicit statistical fit criteria.	Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels.