Meta-Analytic Structural Equation Modelling
Nonfiction, Social & Cultural Studies, Social Science, Methodology, Science & Nature, Mathematics, Statistics
| Author: | Suzanne Jak | ISBN: | 9783319271743 |
| Publisher: | Springer International Publishing | Publication: | November 28, 2015 |
| Imprint: | Springer | Language: | English |
| Author: | Suzanne Jak |
| ISBN: | 9783319271743 |
| Publisher: | Springer International Publishing |
| Publication: | November 28, 2015 |
| Imprint: | Springer |
| Language: | English |
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.
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.
