เปรียบเทียบวิธี

	การวิเคราะห์องค์ประกอบเชิงสำรวจแบบคงทน	Robust Confirmatory Factor Analysis
สาขาวิชา	การวัดทางจิตวิทยา	สถิติศาสตร์
ตระกูล	Latent structure	Latent structure
ปีกำเนิด	2000–2003	1984–1994
ผู้ริเริ่ม	Pison, Rousseeuw, Filzmoser, and Croux; Yuan and Bentler (parallel streams)	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)
ประเภท	Latent variable / dimension reduction (robust)	Confirmatory latent variable model with robust estimation
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	4	6
สรุป	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.