Salīdzināt metodes

	Robust Structural Equation Modeling	Ceļu analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1994	1921
Autors	Albert Satorra & Peter M. Bentler	Sewall Wright
Tips	Latent variable / path model with robust inference	Causal / mediation model
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.	Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.
Citi nosaukumi	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM	PA, path coefficient analysis, observed-variable SEM, causal path modeling
Saistītās	5	5
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.	Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.