Meta-analysis: a structural equation modeling approach

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 impo.

Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; List of abbreviations; List of figures; List of tables; Chapter 1 Introduction; 1.1 What is meta-analysis?; 1.2 What is structural equation modeling?; 1.3 Reasons for writing a book on meta-analysis and structural equation modeling; 1.3.1 Benefits to users of structural equation modeling and meta-analysis; 1.4 Outline of the following chapters; 1.4.1 Computer examples and data sets used in this book; 1.5 Concluding remarks and further readings; References; Chapter 2 Brief review of structural equation modeling; 2.1 Introduction.

2.2 Model specification2.2.1 Equations; 2.2.2 Path diagram; 2.2.3 Matrix representation; 2.3 Common structural equation models; 2.3.1 Path analysis; 2.3.2 Confirmatory factor analysis; 2.3.3 Structural equation model; 2.3.4 Latent growth model; 2.3.5 Multiple-group analysis; 2.4 Estimation methods, test statistics, and goodness-of-fit indices; 2.4.1 Maximum likelihood estimation; 2.4.2 Weighted least squares; 2.4.3 Multiple-group analysis; 2.4.4 Likelihood ratio test and Wald test; 2.4.5 Confidence intervals on parameter estimates; 2.4.6 Test statistics versus goodness-of-fit indices.

2.5 …