Comparer des méthodes

	Recherche sur la validation de modèles multivariés	Analyse de chemin
Domaine	Conception de la recherche	Statistique
Famille	Process / pipeline	Latent structure
Année d'origine	1970s–1980s (multivariate model testing as a distinct approach)	1921
Auteur d'origine	Karl Jöreskog (SEM/LISREL framework); Barbara Tabachnick & Linda Fidell (multivariate methods synthesis)	Sewall Wright
Type	Quantitative confirmatory research design	Causal / mediation model
Source fondatrice	Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Alias	multivariate model testing, multivariate structural testing, multivariate confirmatory modeling, MVMT research	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Apparentées	5	5
Résumé	Multivariate model testing research is a confirmatory quantitative design in which a theoretically derived model involving multiple variables and their interrelationships is formally tested against empirical data. Rather than exploring patterns inductively, the researcher specifies a model a priori — capturing hypothesized directional paths, latent constructs, or covariance structures — and then evaluates how well this model reproduces the observed data using techniques such as structural equation modeling, confirmatory factor analysis, or multivariate path analysis.	Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.