Salīdzināt metodes

	Robust Structural Equation Modeling	Apstiprinošā faktoru analīze (AFA)
Nozare	Statistika	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1994	1969
Autors	Albert Satorra & Peter M. Bentler	Karl Gustav Jöreskog
Tips	Latent variable / path model with robust inference	Hypothesis-testing latent variable 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.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Citi nosaukumi	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM	CFA, confirmatory FA, measurement model, restricted factor analysis
Saistītās	5	4
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.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.