1st Edition
by Mike W.-L. Cheung (Author)
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.
