Presents a novel approach to conducting meta-analysis using structural equation modeling. Meta-Analysis: A Structural Equation Modeling Approach, authored by Mike W.-L. Cheung, introduces readers to the integration of these two powerful statistical methods within a unified framework. The book begins by highlighting the importance of SEM and meta-analysis in addressing research questions, providing a brief review of key ideas in both methodologies before delving into various meta-analytic models within the SEM context.

Key topics covered include fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling. Advanced topics such as using restricted maximum likelihood estimation methods and handling missing covariates are also addressed. Readers will learn how to conduct meta-analysis using the metaSEM package in the R statistical environment.

The book is structured to be accessible, making it a valuable resource for academic and statistical researchers and graduate students engaged in meta-analyses. It will also benefit researchers and statisticians working with SEM in biostatistics. Basic knowledge of either SEM or meta-analysis would be helpful for understanding the materials presented.

ISBN-10: 1119993431
ISBN-13: 978-1119993438
Publication Date: May 13, 2015
Print Length: 400 pages

Read more about the book’s content and applications in various scientific fields.