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Presents a novel approach to conducting metaa€“analysis using structural equation modeling. Structural equation modeling (SEM) and metaa€“analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing metaa€“analytic data within the SEM framework, and illustrates how to conduct metaa€“analysis using the metaSEM package in the R statistical environment. Metaa€“Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and metaa€“analysis in answering research questions. Key ideas in metaa€“analysis and SEM are briefly reviewed, and various metaa€“analytic models are then introduced and linked to the SEM framework. Fixeda€“, randoma€“, and mixeda€“effects models in univariate and multivariate metaa€“analyses, threea€“level metaa€“analysis, and metaa€“analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both metaa€“analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out metaa€“analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or metaa€“analysis will be helpful in understanding the materials in this book.... 160, 210) Ragt; #### Random-effects model with diagonal elements only Ragt; ## First stage analysis Ragt; randoml alt; a€“ tsseml (my. df.4, my . n.4, method=aquot;REMaquot;, RE . type=aquot;Diagaquot;) R= ## rerun to remove error code Ragt; randoml alt; a€“ rerun (randoml, anbsp;...

Author: Mike W.-L. Cheung
Publisher:John Wiley & Sons - 2015-05-06

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