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Meta-Analysis - A Structural Equation Modeling Approach

Name: Meta-Analysis - A Structural Equation Modeling Approach
Brand: John Wiley and Sons Ltd
SKU: 9781119993438
Price: 31012 HUF
Availability: InStock

Szerző: Mike Cheung

Nyelv:

Kötés: Kemény kötésű

Kiadó: John Wiley and Sons Ltd

Elérhetőség: Beszállítói készleten

Küldés 9-13 napon belül

31 012 Ft

Presents a novel approach to conducting meta-analysis using structural equation modeling.§§Structura...

Információk a könyvről

Szerző

Mike Cheung

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2015

oldal

400

EAN

9781119993438

ISBN

1119993431

Enbook ID

04953467

Kiadó

John Wiley and Sons Ltd

Súly

654

Méretek

157 x 231 x 24

Teljes leírás

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.

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