Please use this identifier to cite or link to this item: https://doi.org/10.3758/s13428-013-0361-y
Title: Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R
Authors: Cheung, M.W.-L. 
Keywords: Meta-analysis
Meta-analytic structural equation modeling
Random-effects model
Structural equation modeling
Issue Date: Mar-2014
Citation: Cheung, M.W.-L. (2014-03). Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R. Behavior Research Methods 46 (1) : 29-40. ScholarBank@NUS Repository. https://doi.org/10.3758/s13428-013-0361-y
Abstract: Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40-64, 2005b, Structural Equation Modeling 16:28-53, 2009) proposed a two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model by assuming that all studies have the same population correlation or covariance matrices. The main objective of this article is to extend the TSSEM approach to a random-effects model by the inclusion of study-specific random effects. Another objective is to demonstrate the procedures with two examples using the metaSEM package implemented in the R statistical environment. Issues related to and future directions for MASEM are discussed. © 2013 Psychonomic Society, Inc.
Source Title: Behavior Research Methods
URI: http://scholarbank.nus.edu.sg/handle/10635/124502
ISSN: 1554351X
DOI: 10.3758/s13428-013-0361-y
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