Сравнение методов

	Робастная конфирматорная факторная модель	Робастное моделирование структурными уравнениями
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	1984–1994	1994
Автор метода	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Albert Satorra & Peter M. Bentler
Тип	Confirmatory latent variable model with robust estimation	Latent variable / path model with robust inference
Основополагающий источник	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.	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.
Другие названия	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM
Связанные	6	5
Сводка	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 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.