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.Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-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 meta-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 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.

Preface xiiiAcknowledgments xvList of abbreviations xviiList of figures xixList of tables xxi1 Introduction 11.1 What is meta-analysis? 11.2 What is structural equation modeling? 21.3 Reasons for writing a book on meta-analysis and structural equation modeling 31.4 Outline of the following chapters 61.5 Concluding remarks and further readings 82 Brief review of structural equation modeling 132.1 Introduction 132.2 Model specification 142.3 Common structural equation models 182.4 Estimation methods, test statistics, and goodness-of-fit indices 252.5 Extensions on structural equation modeling 382.6 Concluding remarks and further readings 423 Computing effect sizes for meta-analysis 483.1 Introduction 483.2 Effect sizes for univariate meta-analysis 503.3 Effect sizes for multivariate meta-analysis 573.4 General approach to estimating the sampling variances and covariances 603.5 Illustrations Using R 683.6 Concluding remarks and further readings 784 Univariate meta-analysis 814.1 Introduction 814.2 Fixed-effects model 834.3 Random-effects model 874.4 Comparisons between the fixed- and the random-effects models 934.5 Mixed-effects model 964.6 Structural equation modeling approach 1004.7 Illustrations using R 1054.8 Concluding remarks and further readings 1165 Multivariate meta-analysis 1215.1 Introduction 1215.2 Fixed-effects model 1245.3 Random-effects model 1275.4 Mixed-effects model 1345.5 Structural equation modeling approach 1365.6 Extensions: mediation and moderation models on the effect sizes 1405.7 Illustrations using R 1455.8 Concluding remarks and further readings 1746 Three-level meta-analysis 1796.1 Introduction 1796.2 Three-level model 1836.3 Structural equation modeling approach 1886.4 Relationship between the multivariate and the three-level meta-analyses 1956.5 Illustrations using R 2006.6 Concluding remarks and further readings 2107 Meta-analytic structural equation modeling 2147.1 Introduction 2147.2 Conventional approaches 2187.3 Two-stage structural equation modeling: fixed-effects models 2237.4 Two-stage structural equation modeling: random-effects models 2337.5 Related issues 2357.6 Illustrations using R 2447.7 Concluding remarks and further readings 2738 Advanced topics in SEM-based meta-analysis 2798.1 Restricted (or residual) maximum likelihood estimation 2798.2 Missing values in the moderators 2898.3 Illustrations using R 2948.4 Concluding remarks and further readings 3099 Conducting meta-analysis with Mplus 3139.1 Introduction 3139.2 Univariate meta-analysis 3149.3 Multivariate meta-analysis 3279.4 Three-level meta-analysis 3469.5 Concluding remarks and further readings 353A A brief introduction to R, OpenMx, and metaSEM packages 356A.1 R 357A.2 OpenMx 362A.3 metaSEM 364References 368Index 369