The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are due to the Size of the Covariance Matrix

Autor/in	Moshagen, Morten
Titel	The Model Size Effect in SEM: Inflated Goodness-of-Fit Statistics Are due to the Size of the Covariance Matrix
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 19 (2012) 1, S.86-98 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
DOI	10.1080/10705511.2012.634724
Schlagwörter	Goodness of Fit; Structural Equation Models; Statistical Analysis; Monte Carlo Methods; Sample Size; Factor Analysis + Suchen Sie Ihr Suchwort? Statistische Analyse; Monte-Carlo-Methode; Faktorenanalyse
Abstract	The size of a model has been shown to critically affect the goodness of approximation of the model fit statistic "T" to the asymptotic chi-square distribution in finite samples. It is not clear, however, whether this "model size effect" is a function of the number of manifest variables, the number of free parameters, or both. It is demonstrated by means of 2 Monte Carlo computer simulation studies that neither the number of free parameters to be estimated nor the model degrees of freedom systematically affect the "T" statistic when the number of manifest variables is held constant. Increasing the number of manifest variables, however, is associated with a severe bias. These results imply that model fit drastically depends on the size of the covariance matrix and that future studies involving goodness-of-fit statistics should always consider the number of manifest variables, but can safely neglect the influence of particular model specifications. (Contains 2 tables and 1 footnote.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10