in Regression Kaufman173 An Introduction to Exponential Random Graph Modeling Harris
174 Introduction to Time Series Analysis Pickup
175 Factorial Survey Experiments Auspurg/Hinz
176 Introduction to Power Analysis: Two-Group Studies Hedberg
177 Linear Regression: A Mathematical Introduction Gujarati
178 Propensity Score Methods and Applications Bai/Clark
179 Multilevel Structural Equation Modeling Silva/Bosancianu/Littvay
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MULTILEVEL STRUCTURAL EQUATION MODELING
Bruno Castanho Silva
University of Cologne
Constantin Manuel Bosancianu
WZB Berlin Social Science Center
Levente Littvay
Central European University
Copyright © 2020 by SAGE Publications, Inc.
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Library of Congress Cataloging-in-Publication Data
Names: Silva, Bruno Castanho, author. | Bosancianu, Constantin Manuel, author. | Littvay, Levente, author.
Title: Multilevel structural equation modeling / Bruno Castanho Silva (University of Cologne), Constantin Manuel Bosancianu (Wissenschaftszentrum Berlin für Sozialforschung), Levente Littvay (Central European University).
Description: Thousand Oaks, California : SAGE Publications, Inc., 2019. | Includes bibliographical references and index.
Identifiers: LCCN 2018057333 | ISBN 978-1-5443-2305-3 (pbk. : alk. paper)
Subjects: LCSH: Path analysis (Statistics) | Structural equation modeling. | Multilevel models (Statistics) | Regression analysis.
Classification: LCC QA278.3 .S55 2019 | DDC 519.5/3–dc23 LC record available at https://lccn.loc.gov/2018057333
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Series Editor’s Introduction
I am pleased to introduce Multilevel Structural Equation Modeling, by Bruno Castanho Silva, Constantin Manuel Cosancianu, and Levente Littvay. Multilevel structural equation models (MSEMs) combine the study of relationships between variables measured with error central to structural equation modeling (SEM) with an interest in macro-micro relationships central to multilevel models (MLM). This volume is well-organized with a clear progression of topics, starting with SEMs of observed variables, proceeding to confirmatory factor analysis (CFA), and then the full model, adding a multilevel component to each along the way. In each chapter, the authors proceed systematically from simpler to more complex model specifications, using examples to illustrate each step. Readers can practice by replicating these examples using materials available in the online appendix.
An innovation of the volume is the notation. SEMs and MLMs each have their own conventions; the authors blend them. They maintain notation used in standard multilevel texts but introduce superscripts to keep track of the outcome variable associated with particular coefficients. This approach makes it possible to write MSEM models as a series of equations, making the volume broadly accessible, even to readers not well-versed in matrix algebra. To further reinforce the reader understanding, they show most models both as a set of equations and also as graphical presentations that build on SEM traditions.
In terms of the preparation needed, readers having experience with structural equations models (SEM) or multilevel models (MLM) are in the best position to benefit from this volume. Chapter
