Salīdzināt metodes

	Robust Structural Equation Modeling	Robust Path Analysis
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1994	1998
Autors	Albert Satorra & Peter M. Bentler	Yuan & Bentler (robust SEM/path framework); Huber (M-estimation foundation)
Tips	Latent variable / path model with robust inference	Causal path modeling with robust estimation
Pirmavots	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.	Yuan, K.-H. & Bentler, P. M. (1998). Robust mean and covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 51(1), 63–88.
Citi nosaukumi	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM	robust PA, path analysis with robust standard errors, robust causal path modeling, robust structural path modeling
Saistītās	5	6
Kopsavilkums	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.	Robust path analysis applies robust estimation — such as sandwich standard errors or M-estimation — to path models that specify directed causal relationships among observed variables. It preserves valid inference about path coefficients and indirect effects when data violate normality, contain outliers, or exhibit heteroscedasticity that would distort conventional standard errors.