Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-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 meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. MetaAnalysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and metaanalysis in answering research questions. Key ideas in metaanalysis and SEM are briefly reviewed, and various metaanalytic models are then introduced and linked to the SEM framework. Fixed, random, and mixedeffects models in univariate and multivariate metaanalyses, threelevel metaanalysis, and metaanalytic 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 metaanalysis and SEM. Examples in R and some of the analyses in Mplus and LISREL are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

Mike W.-L. Cheung, National University of Singapore, Singapore

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