Compara mètodes

	Anàlisi de moderació robusta	Modelització d'equacions estructurals robusta
Camp	Estadística	Estadística
Família	Latent structure	Latent structure
Any d'origen	2007	1994
Autor original	Hayes & Cai; Wilcox	Albert Satorra & Peter M. Bentler
Tipus	Robust regression-based interaction test	Latent variable / path model with robust inference
Font seminal	Hayes, A. F. & Cai, L. (2007). Using heteroscedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722.	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.
Àlies	robust interaction analysis, robust moderated regression, HC-corrected moderation, outlier-resistant interaction testing	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM
Relacionats	5	5
Resum	Robust moderation analysis tests whether the effect of a predictor on an outcome depends on the level of a moderator variable, using estimation methods that remain valid under non-normality, heteroscedasticity, or the presence of influential outliers. It is the preferred approach when standard ordinary least squares assumptions cannot be trusted.	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.