Comparer des méthodes

	Analyse robuste des chemins	Modélisation par équations structurelles robuste
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1998	1994
Auteur d'origine	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)	Albert Satorra & Peter M. Bentler
Type	Causal path modeling with robust estimation	Latent variable / path model with robust inference
Source fondatrice	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.	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 (pp. 399–419). Sage.
Alias	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM
Apparentées	6	5
Résumé	Robust path analysis applies robust estimation — such as sandwich standard errors or M-estimation — to path models that specify directed causal relationships among observed variables. It preserves valid inference about path coefficients and indirect effects when data violate normality, contain outliers, or exhibit heteroscedasticity that would distort conventional standard errors.	Robust structural equation modeling (Robust SEM) applies the full SEM framework — simultaneous estimation of measurement and structural relations among latent variables — while using corrected test statistics and sandwich standard errors that remain valid when observed data depart from multivariate normality. The Satorra-Bentler scaled chi-square is the most widely used correction.