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Meta-analysis : a structural equation modeling approach /
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 lite...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex :
John Wiley & Sons, Inc.,
2015.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Cheung, Mike W. L. | |
| 245 | 1 | 0 | |a Meta-analysis : |b a structural equation modeling approach / |c Mike W.L. Cheung. |
| 264 | 1 | |a Chichester, West Sussex : |b John Wiley & Sons, Inc., |c 2015. | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; List of abbreviations; List of figures; List of tables; Chapter 1 Introduction; 1.1 What is meta-analysis?; 1.2 What is structural equation modeling?; 1.3 Reasons for writing a book on meta-analysis and structural equation modeling; 1.3.1 Benefits to users of structural equation modeling and meta-analysis; 1.4 Outline of the following chapters; 1.4.1 Computer examples and data sets used in this book; 1.5 Concluding remarks and further readings; References; Chapter 2 Brief review of structural equation modeling; 2.1 Introduction. | |
| 505 | 8 | |a 2.2 Model specification2.2.1 Equations; 2.2.2 Path diagram; 2.2.3 Matrix representation; 2.3 Common structural equation models; 2.3.1 Path analysis; 2.3.2 Confirmatory factor analysis; 2.3.3 Structural equation model; 2.3.4 Latent growth model; 2.3.5 Multiple-group analysis; 2.4 Estimation methods, test statistics, and goodness-of-fit indices; 2.4.1 Maximum likelihood estimation; 2.4.2 Weighted least squares; 2.4.3 Multiple-group analysis; 2.4.4 Likelihood ratio test and Wald test; 2.4.5 Confidence intervals on parameter estimates; 2.4.6 Test statistics versus goodness-of-fit indices. | |
| 505 | 8 | |a 2.5 Extensions on structural equation modeling2.5.1 Phantom variables; 2.5.2 Definition variables; 2.5.3 Full information maximum likelihood estimation; 2.6 Concluding remarks and further readings; References; Chapter 3 Computing effect sizes for meta-analysis; 3.1 Introduction; 3.2 Effect sizes for univariate meta-analysis; 3.2.1 Mean differences; 3.2.2 Correlation coefficient and its Fisher's z transformation; 3.2.3 Binary variables; 3.3 Effect sizes for multivariate meta-analysis; 3.3.1 Mean differences; 3.3.2 Correlation matrix and its Fisher's z transformation; 3.3.3 Odds ratio. | |
| 505 | 8 | |a 3.4 General approach to estimating the sampling variances and covariances3.4.1 Delta method; 3.4.2 Computation with structural equation modeling; 3.5 Illustrations Using R; 3.5.1 Repeated measures; 3.5.2 Multiple treatment studies; 3.5.3 Multiple-endpoint studies; 3.5.4 Multiple treatment with multiple-endpoint studies; 3.5.5 Correlation matrix; 3.6 Concluding remarks and further readings; References; Chapter 4 Univariate meta-analysis; 4.1 Introduction; 4.2 Fixed-effects model; 4.2.1 Estimation and hypotheses testing; 4.2.2 Testing the homogeneity of effect sizes. | |
| 505 | 8 | |a 4.2.3 Treating the sampling variance as known versus as estimated4.3 Random-effects model; 4.3.1 Estimation and hypothesis testing; 4.3.2 Testing the variance component; 4.3.3 Quantifying the degree of the heterogeneity of effect sizes; 4.4 Comparisons between the fixed- and the random-effects models; 4.4.1 Conceptual differences; 4.4.2 Statistical differences; 4.5 Mixed-effects model; 4.5.1 Estimation and hypotheses testing; 4.5.2 Explained variance; 4.5.3 A cautionary note; 4.6 Structural equation modeling approach; 4.6.1 Fixed-effects model; 4.6.2 Random-effects model. | |
| 520 | |a 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 impo. | ||
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