## Cheung, Mike W.-L.

# 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 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.

**Keywords:** meta-analysis, structural equation models, statistics, R, educational science, social science, behavioral science, medical science, meta-analysis, structural equation models, statistics, R, educational science, social science, behavioral science, medical science, Applied Probability & Statistics - Models

- Author(s)
- Cheung, Mike W.-L.
- Publisher
- John Wiley and Sons, Inc.
- Publication year
- 2014
- Language
- en
- Edition
- 1
- Page amount
- 400 pages
- Category
- Natural Sciences
- Format
- Ebook
- eISBN (PDF)
- 9781118957837
- Printed ISBN
- 9781119993438