Presents a novel approach to conducting metaaanalysis using structural equation modeling. Structural equation modeling (SEM) and metaaanalysis 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 metaaanalytic data within the SEM framework, and illustrates how to conduct metaaanalysis using the metaSEM package in the R statistical environment. MetaaAnalysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and metaaanalysis in answering research questions. Key ideas in metaaanalysis and SEM are briefly reviewed, and various metaaanalytic models are then introduced and linked to the SEM framework. Fixeda, randoma, and mixedaeffects models in univariate and multivariate metaaanalyses, threealevel metaaanalysis, and metaaanalytic 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 metaaanalysis 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 metaaanalyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or metaaanalysis will be helpful in understanding the materials in this book.... 160, 210) Ragt; #### Random-effects model with diagonal elements only Ragt; ## First stage analysis Ragt; randoml alt; a tsseml (my. df.4, my . n.4, method=aquot;REMaquot;, RE . type=aquot;Diagaquot;) R= ## rerun to remove error code Ragt; randoml alt; a rerun (randoml, anbsp;...
Title | : | Meta-Analysis |
Author | : | Mike W.-L. Cheung |
Publisher | : | John Wiley & Sons - 2015-05-06 |
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