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.
表中的内容
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
关于作者
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.
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