Comparer des méthodes

	Analyse quantitative multivariée de contenu	Analyse factorielle
Domaine	Conception de la recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1969–2000s	1931
Auteur d'origine	Rooted in Holsti (1969) and Neuendorf (2002); multivariate extensions developed in communication and political science research from the 1970s onward	Louis Leon Thurstone
Type	Quantitative research design	Method
Source fondatrice	Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications. ISBN: 978-0761919773	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.
Alias	multivariate QCA, multivariate content analysis, MQCA, multivariate text analysis	EFA, CFA, latent variable modeling
Apparentées	6	3
Résumé	Multivariate quantitative content analysis (MQCA) is a systematic, replicable approach to measuring multiple attributes of communication content simultaneously and examining how those attributes relate to each other or to external variables. It extends standard content analysis by applying multivariate statistical techniques — such as factor analysis, cluster analysis, regression, or MANOVA — to coded content data, enabling researchers to uncover complex patterns across many variables at once.	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.