Comparer des méthodes

	Analyse factorielle exploratoire (AFE)	Analyse Factorielle Confirmatoire (AFC)	Analyse en composantes principales
Domaine	Statistique	Psychométrie	Apprentissage automatique
Famille	Latent structure	Latent structure	Machine learning
Année d'origine	—	1969	2002
Auteur d'origine	—	Karl Gustav Jöreskog	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Latent variable / dimension reduction	Hypothesis-testing latent variable model	Unsupervised dimensionality reduction
Source fondatrice	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	common factor analysis, açımlayıcı faktör analizi, factor analysis	CFA, confirmatory FA, measurement model, restricted factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	4	4	3
Résumé	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	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.