Sammenlign metoder

	Robust Model Testing Research	Konstruktiv faktoranalyse (CFA)
Fagområde	Forskningsdesign	Psykometri
Familie	Process / pipeline	Latent structure
Oprindelsesår	1988–1998	1969
Ophavsperson	Albert Satorra & Peter M. Bentler; Ke-Hai Yuan	Karl Gustav Jöreskog
Type	Quantitative model-testing research design with robust estimation	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: Applications for developmental research (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, robust structural model testing, robust fit evaluation, robust model evaluation research	CFA, confirmatory FA, measurement model, restricted factor analysis
Relaterede	6	4
Resumé	Robust model testing research applies structural or path models to data while explicitly accounting for violations of multivariate normality and other distributional assumptions. Rather than discarding non-normal data or forcing transformations, it uses corrected estimators — most notably the Satorra-Bentler scaled chi-square and Yuan-Bentler robust standard errors — to produce trustworthy fit indices and parameter estimates even when classical maximum likelihood assumptions are breached.	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.