Comparer des méthodes

	Analyse Factorielle Confirmatoire	Analyse en composantes principales
Domaine	Psychométrie	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	1969	2002
Auteur d'origine	Karl Jöreskog	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Measurement model / latent variable analysis	Unsupervised dimensionality reduction
Source fondatrice	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press. ISBN: 978-1462515363	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Doğrulayıcı Faktör Analizi — Ölçek Doğrulama (CFA), confirmatory factor analysis, measurement model testing	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	6	3
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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.