Constantin Manuel Bosancianu & Levente Littvay 
Multilevel Structural Equation Modeling [PDF ebook] 

 Multilevel Structural Equation Modeling serves as a minimally technical overview of multilevel structural equation modeling (MSEM) for applied researchers and advanced graduate students in the social sciences. As the first book of its kind, this title is an accessible, hands-on introduction for beginners of the topic. The authors predict a growth in this area, fueled by both data availability and also the availability of new and improved software to run these models. The applied approach, combined with a graphical presentation style and minimal reliance on complex matrix algebra guarantee that this volume will be useful to social science graduate students wanting to utilize such models.

Tabela de Conteúdo

List of Figures

About the Authors

Series Editor′s Introduction

Acknowledgements

Chapter 1: Introduction

About the Book and MSEM

Quick Review of Structural Equation Models

Quick Review of Multilevel Models

Introduction to MSEM and Its Notation

Estimation and Model Fit

Scope of the Book and Online Materials

Chapter 2: Multilevel Path Models

Multilevel Regression Example

Random Intercepts Model

Random Slopes Model

Comparison of Random Intercepts and Random Slopes Models

Mediation and Moderation

Chapter 3: Multilevel Factor Models

Multigroup CFA

Two-Level CFA

Random Latent Variable Intercepts

Random Loadings

Chapter 4: Multilevel Structural Equation Models

Bringing Factor and Path Models Together

Random Intercept of Observed Outcome

Multilevel Latent Covariate Model

Between-Level Latent Variables

Random Slopes MSEM

Chapter 5: Conclusion

References

Sobre o autor

Bruno Castanho Silva is a post-doctoral researcher at the Cologne Centerfor Comparative Politics (CCCP), University of Cologne. Bruno receivedhis Ph D from the Department of Political Science at Central European Universityand teaches introductory and advanced quantitative methods courses, including Multilevel Structural Equation Modeling and Machine Learningat the European Consortium for Political Research Methods Schools. Hismethodological interests are on applications of structural equation modelsfor scale development and causal analysis, and statistical methods of causalinference with observational and experimental data.