Evaluation of an Approximate Method for Synthesizing Covariance Matrices for Use in Meta-Analytic SEM

Autor/inn/en	Beretvas, S. Natasha; Furlow, Carolyn F.
Titel	Evaluation of an Approximate Method for Synthesizing Covariance Matrices for Use in Meta-Analytic SEM
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 13 (2006) 2, S.153-185 (33 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
DOI	10.1207/s15328007sem1302_1
Schlagwörter	Structural Equation Models; Matrices; Statistical Analysis; Synthesis; Simulation; Correlation; Evaluation Methods + Suchen Sie Ihr Suchwort? Matrizenrechnung; Statistische Analyse; Simulation program; Simulationsprogramm; Korrelation
Abstract	Meta-analytic structural equation modeling (MA-SEM) is increasingly being used to assess model-fit for variables' interrelations synthesized across studies. MA-SEM researchers have analyzed synthesized correlation matrices using structural equation modeling (SEM) estimation that is designed for covariance matrices. This can produce incorrect model-fit chi-square statistics, standard error estimates (Cudeck, 1989), or both for parameters that are not scale free or that describe a scale-noninvariant model unless corrected SEM estimation is used to analyze the correlations. This study introduced univariate and multivariate approximate methods for synthesizing covariance matrices for use in MA-SEM. A simulation study assessed the approximate methods by estimating parameters in a scale-noninvariant model using synthesized covariances versus synthesized correlations with and without the appropriate corrections. Standard error bias was noted only for uncorrected analyses of pooled correlations. Chi-square model-fit statistics were overly conservative except when covariance matrices were analyzed. Benefits and limitations of this approximate method are presented and discussed. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10