Comparer des méthodes

	Recherche causale-comparative multivariée	Analyse discriminante
Domaine	Conception de la recherche	Statistique
Famille	Process / pipeline	Latent structure
Année d'origine	Mid-20th century onward; multivariate extension systematized 1970s–1990s	1936
Auteur d'origine	Extension of causal-comparative tradition (cf. Chapin, 1947; Gay, Mills & Airasian)	Ronald A. Fisher
Type	Quantitative non-experimental comparative design	Supervised classification and dimension reduction
Source fondatrice	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260085594	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Alias	multivariate causal-comparative design, MANOVA causal-comparative study, multi-outcome ex post facto research, multivariate ex post facto design	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Apparentées	6	4
Résumé	Multivariate causal-comparative research is a quantitative, non-experimental design that investigates whether pre-existing group differences (defined by a naturally occurring categorical variable) are associated with differences across multiple outcome variables considered simultaneously. By extending the classic causal-comparative framework to several dependent variables at once, it reduces Type I error inflation and captures the correlated structure of outcomes that univariate comparisons would miss.	Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.