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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.
- Sales Rank: #306029 in eBooks
- Published on: 2015-04-07
- Released on: 2015-04-07
- Format: Kindle eBook
Review
"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. cover, would sit well on the bookshelves of those interested in this increasingly important field of scientific endeavour." (Zentralblatt MATH, 1 June 2015)
From the Back Cover
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 some of the analyses in Mplus and LISREL 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.
About the Author
Mike W.-L. Cheung, National University of Singapore, Singapore
Most helpful customer reviews
7 of 7 people found the following review helpful.
Not for beginners to SEM or meta-analysis, but an important supplementary text for advanced meta-analysis
By John Sakaluk
I have a lot of ambivalence about this book; it does a few things really well, and some other things not so well.
First, the good: Cheung's method of three-level meta-analysis via SEM is, in my opinion, brilliant, and the chapter (Chapter 6) describing it, characterizing its advantages over other methods, and providing a walkthrough of how to conduct this type of analysis with his metaSEM package for R, is incredibly well-written. So much so, that I can see this particular chapter becoming a staple in graduate level meta-analysis classes. True, much of the information presented in this chapter is available elsewhere (the metaSEM website, and Cheung's 2014a and 2014b articles, for example). But at least with this book/chapter, 99% of what you need to start doing this analysis is right there. If you are trying to meta-analyze dependent effect sizes (e.g., more than one effect size reported per sample), Cheung's approach is cutting-edge, extremely powerful, and deceptively simple to implement via the metaSEM package.
Where the book mainly flounders, for me, is in some of its earlier chapters that attempt to cover SEM and meta-analysis basics needed to get the most out of the SEM approach to meta-analysis that Cheung advocates for. The book assumes, for example, some SEM experience of its readers, but then covers many of the basic SEM concepts anyways. In doing so, however, the book adopts a heavy-handed algebra approach. The end result is a chapter that feels topically suitable, but overly technical for SEM beginners, while topically underwhelming, yet technically appropriate for seasoned SEM users. If you are new to SEM, I would strongly recommend getting up to speed with a book like Latent Variable Modeling Using R: A Step-by-Step Guide, and then revisiting the SEM approach to meta-analysis. Likewise, I don't think the book could stand on its own as a comprehensive start-to-finish meta-analysis text. Little attention, for example, is paid to the matter of how to search and code literature, meta-analytic reporting standards, conventional visualizations of meta-analytic data (e.g., forest and funnel plots), the matter of publication bias, and the like (though solid references for these topics are provided). Thus, those looking for an introductory text for meta-analysis would probably be better off looking at Introduction to Meta-Analysis or Applied Meta-Analysis for Social Science Research (Methodology in the Social Sciences).
Does that mean that "Meta-Analysis: A Structural Equation Modeling Approach" isn't worth a spot on your statistics bookshelf? Certainly not. If you are familiar with the SEM framework, and the basics of carrying out a meta-analysis, you should strongly consider this book. Dependency of effect sizes is, in my opinion, a highly undervalued problem when conducting a meta-analysis. Cheung's approach described in this book (and the accompanying R package) provides a beautiful solution to this issue, that is surprisingly straightforward to implement and interpret. So if you're into meta-analysis, buy this book. And if you want to get into meta-analysis, consider it for your second or third "advanced/specialized" text on meta-analysis.
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