Sammenlign metoder

	Robust Strukturel Ligningsmodellering	Konstruktiv faktoranalyse (CFA)
Fagområde	Statistik	Psykometri
Familie	Latent structure	Latent structure
Oprindelsesår	1994	1969
Ophavsperson	Albert Satorra & Peter M. Bentler	Karl Gustav Jöreskog
Type	Latent variable / path model with robust inference	Hypothesis-testing latent variable model
Oprindelig kilde	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.
Aliasser	Robust SEM, SEM with robust standard errors, Satorra-Bentler SEM, non-normal SEM	CFA, confirmatory FA, measurement model, restricted factor analysis
Relaterede	5	4
Resumé	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.