Description
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. Mike W.-L. Cheung, National University of Singapore, Singapore Preface xiii Acknowledgments xv List of abbreviations xvii List of figures xix List of tables xxi 1 Introduction 1 1.1 What is meta-analysis? 1 1.2 What is structural equation modeling? 2 1.3 Reasons for writing a book on meta-analysis and structural equation modeling 3 1.4 Outline of the following chapters 6 1.5 Concluding remarks and further readings 8 2 Brief review of structural equation modeling 13 2.1 Introduction 13 2.2 Model specification 14 2.3 Common structural equation models 18 2.4 Estimation methods, test statistics, and goodness-of-fit indices 25 2.5 Extensions on structural equation modeling 38 2.6 Concluding remarks and further readings 42 3 Computing effect sizes for meta-analysis 48 3.1 Introduction 48 3.2 Effect sizes for univariate meta-analysis 50 3.3 Effect sizes for multivariate meta-analysis 57 3.4 General approach to estimating the sampling variances and covariances 60 3.5 Illustrations Using R 68 3.6 Concluding remarks and further readings 78 4 Univariate meta-analysis 81 4.1 Introduction 81 4.2 Fixed-effects model 83 4.3 Random-effects model 87 4.4 Comparisons between the fixed- and the random-effects models 93 4.5 Mixed-effects model 96 4.6 Structural equation modeling approach 100 4.7 Illustrations using R 105 4.8 Concluding remarks and further readings 116 5 Multivariate meta-analysis 121 5.1 Introduction 121 5.2 Fixed-effects model 124 5.3 Random-effects model 127 5.4 Mixed-effects model 134 5.5 Structural equation modeling approach 136 5.6 Extensions: mediation and moderation models on the effect sizes 140 5.7 Illustrations using R 145 5.8 Concluding remarks and further readings 174 6 Three-level meta-analysis 179 6.1 Introduction 179 6.2 Three-level model 183 6.3 Structural equation modeling approach 188 6.4 Relationship between the multivariate and the three-level meta-analyses 195 6.5 Illustrations using R 200 6.6 Concluding remarks and further readings 210 7 Meta-analytic structural equation modeling 214 7.1 Introduction 214 7.2 Conventional approaches 218 7.3 Two-stage structural equation modeling: fixed-effects models 223 7.4 Two-stage structural equation modeling: random-effects models 233 7.5 Related issues 235 7.6 Illustrations using R 244 7.7 Concluding remarks and further readings 273 8 Advanced topics in SEM-based meta-analysis 279 8.1 Restricted (or residual) maximum likelihood estimation 279 8.2 Missing values in the moderators 289 8.3 Illustrations using R 294 8.4 Concluding remarks and further readings 309 9 Conducting meta-analysis with Mplus 313 9.1 Introduction 313 9.2 Univariate meta-analysis 314 9.3 Multivariate meta-analysis 327 9.4 Three-level meta-analysis 346 9.5 Concluding remarks and further readings 353 A A brief introduction to R, OpenMx, and metaSEM packages 356 A.1 R 357 A.2 OpenMx 362 A.3 metaSEM 364 References 368 Index 369
