The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling

Autor/inn/en	Jobst, Lisa J.; Auerswald, Max; Moshagen, Morten
Titel	The Effect of Latent and Error Non-Normality on Measures of Fit in Structural Equation Modeling
Quelle	In: Educational and Psychological Measurement, 82 (2022) 5, S.911-937 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Jobst, Lisa J.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211046201
Schlagwörter	Goodness of Fit; Structural Equation Models; Error of Measurement; Factor Analysis; Monte Carlo Methods; Maximum Likelihood Statistics; Least Squares Statistics; Sample Size; Evaluation Methods + Suchen Sie Ihr Suchwort? Messfehler; Faktorenanalyse; Monte-Carlo-Methode
Abstract	Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of non-normality is considered. We conducted a Monte Carlo simulation manipulating the underlying multivariate distribution to assess the effect of the source of non-normality (latent, error, and marginal conditions with either multivariate normal or non-normal marginal distributions) on different measures of fit (empirical rejection rates for the likelihood-ratio model test statistic, the root mean square error of approximation, the standardized root mean square residual, and the comparative fit index). We considered different estimation methods (maximum likelihood, generalized least squares, and (un)modified asymptotically distribution-free), sample sizes, and the extent of non-normality in correctly specified and misspecified models to investigate their performance. The results show that all measures of fit were affected by the source of non-normality but with varying patterns for the analyzed estimation methods. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01