Comparar métodos

	Análisis Factorial Exploratorio Robusto	Análisis Factorial Confirmatorio Robusto
Campo	Psicometría	Estadística
Familia	Latent structure	Latent structure
Año de origen	2000–2003	1984–1994
Autor original	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)
Tipo	Latent variable / dimension reduction (robust)	Confirmatory latent variable model with robust estimation
Fuente seminal	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.	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.
Alias	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA
Relacionados	4	6
Resumen	Robust exploratory factor analysis discovers the latent factor structure of a set of items using estimation methods that are resistant to outliers and violations of multivariate normality. It applies the same measurement model as standard EFA but replaces classical covariance estimation with robust counterparts — such as minimum covariance determinant or iteratively reweighted least squares — so that a small fraction of atypical cases cannot distort the recovered factor loadings.	Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.