Bandingkan kaedah

	Analisis Faktor Konfirmatori (CFA) Robust	Analisis Faktor Eksploratori Robust
Bidang	Statistik	Psikometrik
Keluarga	Latent structure	Latent structure
Tahun asal	1984–1994	2000–2003
Pengasas	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)
Jenis	Confirmatory latent variable model with robust estimation	Latent variable / dimension reduction (robust)
Sumber perintis	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.	Yuan, K.-H., & Bentler, P. M. (2000). Robust mean and covariance structure analysis through iteratively reweighted least squares. Psychometrika, 65(1), 43–58.
Alias	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	robust EFA, robust factor analysis, outlier-resistant factor analysis, EFA with robust estimation
Berkaitan	6	4
Ringkasan	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.	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.