Сравнение методов

	Робастная конфирматорная факторная модель	Структурное моделирование (Structural Equation Modeling)
Область	Статистика	Статистика исследований
Семейство	Latent structure	Process / pipeline
Год появления	1984–1994	1921
Автор метода	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)	Sewall Wright
Тип	Confirmatory latent variable model with robust estimation	Method
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.	Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm.
Другие названия	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA	SEM, path analysis, latent variable modeling, causal modeling
Связанные	6	3
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.	Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis.