Meta-analytic structural equation modelling /

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Bibliographic Details
Author / Creator:Jak, Suzanne, author.
Imprint:[Cham] : Springer, [2015]
Description:1 online resource.
Language:English
Series:SpringerBriefs in research synthesis and and meta-analysis
SpringerBriefs in research synthesis and and meta-analysis.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11096807
Hidden Bibliographic Details
ISBN:9783319271743
3319271741
9783319271729
3319271725
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (EBSCO, viewed December 10, 2015).
Summary:This book explains how to employ MASEM, the combination of meta-analysis (MA) and structural equation modelling (SEM). It shows how by using MASEM, a single model can be tested to explain the relationships between a set of variables in several studies. This book gives an introduction to MASEM, with a focus on the state of the art approach: the two stage approach of Cheung and Cheung & Chan. Both, the fixed and the random approach to MASEM are illustrated with two applications to real data. All steps that have to be taken to perform the analyses are discussed extensively. All data and syntax files are available online, so that readers can imitate all analyses. By using SEM for meta-analysis, this book shows how to benefit from all available information from all available studies, even if few or none of the studies report about all relationships that feature in the full model of interest.
Other form:Print version: Jak, Suzanne. Meta-Analytic Structural Equation Modelling. Cham : Springer International Publishing, ©2015 9783319271729
Standard no.:10.1007/978-3-319-27174-3

MARC

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588 0 |a Online resource; title from PDF title page (EBSCO, viewed December 10, 2015). 
505 0 |a Preface; Contents; 1 Introduction to Meta-Analysis and Structural Equation Modeling; Abstract ; 1.1 What Is Meta-Analysis?; 1.1.1 Issues in Meta-Analysis; 1.1.2 Statistical Analysis; 1.2 What Is SEM?; 1.2.1 Path Analysis; 1.2.2 Model Fit; 1.2.3 Factor Analysis; 1.3 Why Should You Combine SEM and MA?; References; 2 Methods for Meta-Analytic Structural Equation Modeling; Abstract ; 2.1 Introduction; 2.2 Univariate Methods; 2.3 Multivariate Methods; 2.3.1 The GLS Method; 2.3.2 Two Stage Structural Equation Modeling (TSSEM); References; 3 Heterogeneity; Abstract ; 3.1 Introduction. 
505 8 |a 3.2 Testing the Significance of Heterogeneity3.3 The Size of the Heterogeneity; 3.4 Random Effects Analysis or Explaining Heterogeneity; 3.4.1 Random Effects MASEM; 3.4.2 Subgroup Analysis; References; 4 Issues in Meta-Analytic Structural Equation Modeling; Abstract ; 4.1 Software to Conduct MASEM; 4.2 Fit-Indices in TSSEM; 4.3 Missing Correlations in TSSEM; 4.4 The ML-Approach to MASEM; References; 5 Fitting a Path Model with the Two-Stage Approach; Abstract ; 5.1 Introduction; 5.2 Preparing the Data; 5.3 Fixed Effects Analysis; 5.4 Random Effects Analysis. 
505 8 |a 5.5 Random Effects Subgroup AnalysisReferences; 6 Fitting a Factor Model with the Two-Stage Approach; Abstract ; 6.1 Introduction; 6.2 Preparing the Data; 6.3 Fixed Effects Analysis; 6.4 Random Effects Analysis; References; Appendix A Model Implied Covariance Matrix of the Example Path Model; Appendix B Fitting a Path Model to a Covariance Matrix with OpenMx; Appendix C Model Implied Covariance Matrix of the Example Factor Model; Appendix D Fitting a Factor Model to a Covariance Matrix with OpenMx. 
520 |a This book explains how to employ MASEM, the combination of meta-analysis (MA) and structural equation modelling (SEM). It shows how by using MASEM, a single model can be tested to explain the relationships between a set of variables in several studies. This book gives an introduction to MASEM, with a focus on the state of the art approach: the two stage approach of Cheung and Cheung & Chan. Both, the fixed and the random approach to MASEM are illustrated with two applications to real data. All steps that have to be taken to perform the analyses are discussed extensively. All data and syntax files are available online, so that readers can imitate all analyses. By using SEM for meta-analysis, this book shows how to benefit from all available information from all available studies, even if few or none of the studies report about all relationships that feature in the full model of interest. 
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