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. Num Pages: 408 pages, illustrations. BIC Classification: PBK. Category: (P) Professional & Vocational. Dimension: 161 x 236 x 24. Weight in Grams: 632. . 2015. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

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Bibliographic Details

Title: Meta-Analysis: A Structural Equation Modeling Approach
Publisher: John Wiley & Sons Inc
Publication Date: 2015
Language: English
ISBN 10: 1119993431
ISBN 13: 9781119993438
Binding: Hardcover
Condition: New

About this title

Synopsis

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.

About the Author

Mike W.-L. Cheung, National University of Singapore, Singapore

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