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