Multilevel Structural Equation Modeling: (Quantitative Applications in the Social Sciences)

 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.

Table of Contents:
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

About the Author :
Bruno Castanho Silva is a post-doctoral researcher at the Cologne Center for Comparative Politics (CCCP), University of Cologne. Bruno received his PhD from the Department of Political Science at Central European University and teaches introductory and advanced quantitative methods courses, including Multilevel Structural Equation Modeling and Machine Learning at the European Consortium for Political Research Methods Schools. His methodological interests are on applications of structural equation models for scale development and causal analysis, and statistical methods of causal inference with observational and experimental data. Constantin Manuel Bosancianu is a post-doctoral researcher at the Wissenschaftszentrum Berlin für Sozialforschung (WZB), in the Institutions and Political Inequality research unit. He received his PhD from the Department of Political Science at Central European University in Budapest, Hungary and has been an instructor for multiple statistics courses and workshops at the European Consortium for Political Research Methods Schools, at the Universities of Heidelberg, Gießen and Zagreb, as well as at the Institute of Sociology of the Czech Academy of Sciences. Manuel’s methodological focus is on practical applications of multilevel models, Bayesian analysis, and the analysis of time-series cross-sectional data sets. Levente Littvay is associate professor at Central European University’s Department of Political Science. Recipient of the institution’s Distinguished Teaching Award for graduate courses in research methods and applied statistics with a topical emphasis on political psychology, experiments and American politics. He received an MA and a PhD in Political Science and an MS in Survey Research and Methodology from the University of Nebraska-Lincoln, taught numerous methods workshops and is an academic co-convenor of the European Consortium for Political Research Methods Schools. His research interests include populism, political socialization and biological explanations of social and political attitudes and behaviors. He often works as a methodologist with medical researchers and policy analysts, co-runs the Hungarian Twin Registry, is an associate editor for social sciences of Twin Research and Human Genetics and publishes in both social science and medical journals.

Review :
"This book provides a terrific introduction to the key models and principles of multilevel SEM. The examples speak for themselves, and the authors manage well to help readers understand and evaluate the beauty of MSEM. Datasets are by and large available and ready for course use. This is a book that can easily adopted in an introductory MSEM course."