Multilevel Structural Equation Modeling - Silva, Bruno Castanho; Bosancianu, Constantin Manuel; Littvay, Levente
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Multilevel Structural Equation Modeling by Bruno Castanho Silva, Constantin Manuel Bosancianu, and Levente Littvay 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…mehr

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Produktbeschreibung
Multilevel Structural Equation Modeling by Bruno Castanho Silva, Constantin Manuel Bosancianu, and Levente Littvay 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.
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Autorenporträt
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.