The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field.

Table of Contents

• Notes on Contributors
• Preface
• Multilevel Modeling - Jeffrey, S. Simonoff, New York University, Marc A. Scott, New York University, Brian D. Marx, Louisiana State University
• Part I: Multilevel Model Specification and Inference
• 1 The Multilevel Model Framework - Jeff Gill, Washington University, St. Louis, Andrew J. Womack, University of Florida
• 2 Multilevel Model Notation—Establishing the Commonalities - Marc A. Scott, Patrick E. Shrout, Sharon L. Weinberg, New York University
• 3 Likelihood Estimation in Multilevel Models - Harvey Goldstein, University of Bristol
• 4 Bayesian Multilevel Models - Ludwig Fahrmeir, University of Munich, Thomas Kneib, University of Göttingen, Stefan Lang, University of Innsbruck
• 5 The Choice between Fixed and Random Effects - Zac Townsend, New York University, Jack Buckley, National Center for Education Statistics & New York University, Masataka Harada, Marc A. Scott, New York University
• 6 Centering Predictors and Contextual Effects - Craig K. Enders, Arizona State University
• 7 Model Selection for Multilevel Models - Russell Steele, McGill University
• 8 Generalized Linear Mixed Models—Overview - Geert Verbeke, Katholieke Universiteit Leuven, Geert Molenberghs, Universiteit Hasselt
• 9 Longitudinal Data Modeling - Nan M. Laird, Harvard University, Garrett M. Fitzmaurice, Harvard University & McLean Hospital
• 10 Complexities in Error Structures within Individuals - Vicente Núñez-Antón, University of the Basque Country, Dale L. Zimmerman, University of Iowa
• 11 Design Considerations in Multilevel Studies - Gerard van Breukelen, Maastricht University, Mirjam Moerbeek, Utrecht University
• 12 Multilevel Models and Causal Inference - Jennifer Hill, New York University
• Part II: Variations and Extensions of the Multilevel Model
• 13 Multilevel Functional Data Analysis - Ciprian M. Crainiceanu, Brian S. Caffo, Johns Hopkins University, Jeffrey S. Morris, University of Texas MD Anderson Cancer Center
• 14 Nonlinear Models - Lang Wu, University of British Columbia, Wei Liu, York University
• 15 Generalized Linear Mixed Models: Estimation and Inference - Charles E. McCulloch, John M. Neuhaus, University of California, San Francisco
• 16 Categorical Response Data - Jeroen Vermunt, Tilburg University
• 17 Smoothing and Semiparametric Models - Jin-Ting Zhang, National University of Singapore
• 18 Penalized Splines and Multilevel Models - Göran Kauermann, University of Munich, Torben Kuhlenkasper, Goethe University Frankfurt
• 19 Hierarchical Dynamic Models - Marina Silva Paez, Dani Gamerman, Federal University of Rio de Janeiro
• 20 Mixture and Latent Class Models in Longitudinal and Other Settings - Ryan P. Browne, Paul D. McNicholas, University of Guelph
• 21 Multivariate Response Data - Helena Geys, Janssen Pharmaceutica & Johnson and Johnson, Christel Faes, Universiteit Hasselt
• Part III: Practical Considerations in Model Fit and Specification
• 22 Robust Methods for Multilevel Analysis - Joop Hox, Rens van de Schoot, Utrecht University
• 23 Missing Data - Geert Molenberghs, Universiteit Hasselt, Geert Verbeke, Katholieke Universiteit Leuven
• 24 Lack of Fit, Graphics, and Multilevel Model Diagnostics - Gerda Claeskens, Katholieke Universiteit Leuven
• 25 Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors? - Robert Crouchley, Lancaster University
• 26 Software for Fitting Multilevel Models - Andrzej T. Gałecki, Brady T. West, University of Michigan
• Part IV: Selected Applications
• 27 Meta-Analysis - Larry V. Hedges, Northwestern University, Kimberly S. Maier, Michigan State University
• 28 Modeling Policy Adoption and Impact with Multilevel Methods - James E. Monogan III, University of Georgia
• 29 Multilevel Models in the Social and Behavioral Sciences - David Rindskopf, City University of New York Graduate Center
• 30 Survival Analysis and the Frailty Model - Ardo van den Hout, University College London, Brian D.M. Tom, Medical Research Council Biostatistics Unit
• 31 Point-Referenced Spatial Modeling - Andrew O. Finley, Michigan State University, Sudipto Banerjee, University of Minnesota
• 32 Market Research and Preference Data - Adam Sagan, Cracow University of Economics
• 33 Multilevel Modeling of Social Network and Relational Data - Marijtje A.J. van Duijn, University of Groningen
• Subject Index