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Autor/inn/enCheung, Mike W. L.; Chan, Wai
TitelA Two-Stage Approach to Synthesizing Covariance Matrices in Meta-Analytic Structural Equation Modeling
QuelleIn: Structural Equation Modeling: A Multidisciplinary Journal, 16 (2009) 1, S.28-53 (26 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1070-5511
SchlagwörterStructural Equation Models; Simulation; Social Sciences; Correlation; Meta Analysis; Comparative Analysis; Behavioral Sciences; Statistical Analysis
AbstractStructural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation matrices are usually preferred in MASEM, there are cases in which synthesizing covariance matrices is useful, especially when the scales of the measurement are comparable. This study extends the 2-stage SEM (TSSEM) approach proposed by M. W. L. Cheung and Chan (2005b) to synthesizing covariance matrices in MASEM. A simulation study was conducted to compare the TSSEM approach with several approximate methods. An empirical example is used to illustrate the procedures and future directions for MASEM are discussed. (Contains 5 tables and 3 footnotes.) (As Provided).
AnmerkungenPsychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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