Comparer des méthodes

	Analyse canonique des corrélations	Analyse factorielle
Domaine	Statistique	Statistiques de recherche
Famille	Latent structure	Process / pipeline
Année d'origine	1936	1931
Auteur d'origine	Harold Hotelling	Louis Leon Thurstone
Type	Multivariate linear dimension reduction and association	Method
Source fondatrice	Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3–4), 321–377.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.
Alias	CCA, canonical variate analysis, canonical analysis, multiple canonical correlation	EFA, CFA, latent variable modeling
Apparentées	4	3
Résumé	Canonical Correlation Analysis (CCA) is a multivariate statistical method that identifies pairs of linear combinations — one from each of two variable sets — such that the correlation between each pair is maximised. Introduced by Harold Hotelling in his landmark 1936 Biometrika paper, CCA provides the most general linear framework for studying the association between two multivariate batteries of measurements, and many classical procedures (multiple regression, MANOVA, discriminant analysis) are special cases of it.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.