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Mark Lipsey
Last active: 4/6/2017

Fitting meta-analytic structural equation models with complex datasets.

Wilson SJ, Polanin JR, Lipsey MW
Res Synth Methods. 2016 7 (2): 121-39

PMID: 27286899 · PMCID: PMC4905597 · DOI:10.1002/jrsm.1199

A modification of the first stage of the standard procedure for two-stage meta-analytic structural equation modeling for use with large complex datasets is presented. This modification addresses two common problems that arise in such meta-analyses: (a) primary studies that provide multiple measures of the same construct and (b) the correlation coefficients that exhibit substantial heterogeneity, some of which obscures the relationships between the constructs of interest or undermines the comparability of the correlations across the cells. One component of this approach is a three-level random effects model capable of synthesizing a pooled correlation matrix with dependent correlation coefficients. Another component is a meta-regression that can be used to generate covariate-adjusted correlation coefficients that reduce the influence of selected unevenly distributed moderator variables. A non-technical presentation of these techniques is given, along with an illustration of the procedures with a meta-analytic dataset. Copyright © 2016 John Wiley & Sons, Ltd.

Copyright © 2016 John Wiley & Sons, Ltd.

MeSH Terms (21)

Algorithms Child, Preschool Computer Simulation Cross-Sectional Studies Databases, Bibliographic Educational Measurement Effect Modifier, Epidemiologic Female Humans Infant Longitudinal Studies Male Meta-Analysis as Topic Models, Statistical Models, Theoretical Parent-Child Relations Parents Programming Languages Regression Analysis Reproducibility of Results Statistics as Topic

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