Comparer des méthodes

	Recherche sur les tests de modèles robustes	Analyse de chemin
Domaine	Conception de la recherche	Statistique
Famille	Process / pipeline	Latent structure
Année d'origine	1988–1998	1921
Auteur d'origine	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Sewall Wright
Type	Quantitative model-testing research design with robust estimation	Causal / mediation model
Source fondatrice	Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Alias	robust SEM, robust structural model testing, robust fit evaluation, robust model evaluation research	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Apparentées	6	5
Résumé	Robust model testing research applies structural or path models to data while explicitly accounting for violations of multivariate normality and other distributional assumptions. Rather than discarding non-normal data or forcing transformations, it uses corrected estimators — most notably the Satorra-Bentler scaled chi-square and Yuan-Bentler robust standard errors — to produce trustworthy fit indices and parameter estimates even when classical maximum likelihood assumptions are breached.	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.