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The SAGE Handbook of Multilevel Modeling

The SAGE Handbook of Multilevel Modeling

First Edition
Edited by:

September 2013 | 696 pages | SAGE Publications Ltd
In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling.  

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.  

  • Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference.
  • Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models.
  • Part III includes discussion of missing data and robust methods, assessment of fit and software.
  • Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines.  
Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Notes on Contributors
Multilevel Modeling

Jeffrey S Simonoff, Marc A Scott and Brian D Marx
Jeff Gill and Andrew Womack
The Multilevel Model Framework
Marc A Scott, Patrick E Shrout and Sharon L Weinberg
Multilevel Model Notation - Establishing the Commonalities
Harvey Goldstein
Likelihood Estimation in Multilevel Models
Ludwig Fahrmeir, Thomas Kneib, and Stefan Lang
Bayesian Multilevel Models
Zac Townsend,Jack Buckley, Masataka Harada and Marc A Scott
The Choice between Fixed and Random Effects
Craig K Enders
Centering Predictors and Contextual Effects
Russell Steele
Model Selection for Multilevel Models
Geert Verbeke and Geert Molenberghs
Generalized Linear Mixed Models - Overview
Nan M Laird and Garrett M Fitzmaurice
Longitudinal Data Modeling
Vicente Núnez-Antón and Dale L Zimmerman
Complexities in Error Structures Within Individuals
Gerard van Breukelen and Mirjam Moerbeek
Design Considerations in Multilevel Studies
Jennifer Hill
Multilevel Models and Causal Inference
Ciprian M Crainiceanu, Brian S Caffo and Jeffrey S Morris
Multilevel Functional Data Analysis
Lang Wu and Wei Liu
Nonlinear Models
Charles E McCulloch and John M Neuhaus
Generalized Linear Mixed Models: Estimation and Inference
Jeroen Vermunt
Categorical Response Data
Jin-Ting Zhang
Smoothing and Semiparametric Models
Göran Kauermann and Torben Kuhlenkasper
Penalized Splines and Multilevel Models
Marina Silva Paez and Dani Gamerman
Hierarchical Dynamic Models
Ryan P Browne and Paul D McNicholas
Mixture and Latent Class Models in Longitudinal and Other Settings
Helena Geys and Christel Faes
Multivariate Response Data
Joop Hox and Rens van de Schoot
Robust Methods for Multilevel Analysis
Geert Molenberghs and Geert Verbeke
Missing Data
Gerda Claeskens
Lack of Fit, Graphics, and Multilevel Model Diagnostics
Robert Crouchley
Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?
Andrzej T Galecki and Brady T West
Software for Fitting Multilevel Models
Larry V Hedges and Kimberly S Maier
James E Monogan III
Modeling Policy Adoption and Impact with Multilevel Methods
David Rindskopf
Multilevel Models in the Social and Behavioral Sciences
Ardo van den Hout and Brian D M Tom
Survival Analysis and the Frailty Model: The effect of education on survival and disability for older men in England and Wales
Andrew O Finley and Sudipto Banerjee
Point-Referenced Spatial Modeling
Adam Sagan
Market Research and Preference Data
Marijtje A J Van Duijn
Multilevel Modeling for Scoial Networks and Relational Data
Name Index
Subject Index

...This handbook attempts to cover an intricate and multifaceted topic and delivers what is expected of it very successfully and at good cost considering its all-purpose usefulness. This handbook is a must-have consultation book for researchers interested in multilevel modelling from a wide variety of disciplines, if not all.

Patricio Troncosco, Cathie Marsh Institute for Social Research, The University of Manchester
International Journal of Research and Method in Education

Marc A. Scott

Jeffrey S. Simonoff

Jeffrey S. Simonoff is Professor of Statistics at the NYU Stern School of Business. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He is author or coauthor of roughly 100 articles and five books on the theory and applications of statistics. More About Author

Brian D. Marx

Brian D. Marx is a Professor of Statistics at Louisiana State University. His main research interests include smoothing, ill-conditioned regression problems, high-dimensional chemometric applications; and he has numerous publications on these topics. He is past president of the Statistical Modelling Society, and is currently member of the Executive Committee of this same international professional society. He is coauthor of the book Regression: Models, Methods, and Applications, as well as, the co-editor of the Sage Handbook on Multilevel Modelling. More About Author

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ISBN: 9780857025647

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