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.

Notes on ContributorsPreface Multilevel Modeling - Jeffrey S Simonoff, Marc A Scott and Brian D MarxPART ONE: MULTILEVEL MODEL SPECIFICATION AND INFERENCEThe Multilevel Model Framework - Jeff Gill and Andrew WomackMultilevel Model Notation - Establishing the Commonalities - Marc A Scott, Patrick E Shrout and Sharon L WeinbergLikelihood Estimation in Multilevel Models - Harvey GoldsteinBayesian Multilevel Models - Ludwig Fahrmeir, Thomas Kneib, and Stefan LangThe Choice between Fixed and Random Effects - Zac Townsend,Jack Buckley, Masataka Harada and Marc A ScottCentering Predictors and Contextual Effects - Craig K EndersModel Selection for Multilevel Models - Russell SteeleGeneralized Linear Mixed Models - Overview - Geert Verbeke and Geert MolenberghsLongitudinal Data Modeling - Nan M Laird and Garrett M FitzmauriceComplexities in Error Structures Within Individuals - Vicente Núnez-Antón and Dale L ZimmermanDesign Considerations in Multilevel Studies - Gerard van Breukelen and Mirjam MoerbeekMultilevel Models and Causal Inference - Jennifer HillPART TWO: VARIATIONS AND EXTENSIONS OF THE MULTILEVEL MODELMultilevel Functional Data Analysis - Ciprian M Crainiceanu, Brian S Caffo and Jeffrey S MorrisNonlinear Models - Lang Wu and Wei LiuGeneralized Linear Mixed Models: Estimation and Inference - Charles E McCulloch and John M NeuhausCategorical Response Data - Jeroen VermuntSmoothing and Semiparametric Models - Jin-Ting ZhangPenalized Splines and Multilevel Models - Göran Kauermann and Torben KuhlenkasperHierarchical Dynamic Models - Marina Silva Paez and Dani GamermanMixture and Latent Class Models in Longitudinal and Other Settings - Ryan P Browne and Paul D McNicholasMultivariate Response Data - Helena Geys and Christel FaesPART THREE: PRACTICAL CONSIDERATIONS IN MODEL FIT AND SPECIFICATIONRobust Methods for Multilevel Analysis - Joop Hox and Rens van de SchootMissing Data - Geert Molenberghs and Geert VerbekeLack of Fit, Graphics, and Multilevel Model Diagnostics - Gerda ClaeskensMultilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors? - Robert CrouchleySoftware for Fitting Multilevel Models - Andrzej T Galecki and Brady T WestPART FOUR: SELECTED APPLICATIONSMeta-Analysis - Larry V Hedges and Kimberly S MaierModeling Policy Adoption and Impact with Multilevel Methods - James E Monogan IIIMultilevel Models in the Social and Behavioral Sciences - David RindskopfSurvival 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 TomPoint-Referenced Spatial Modeling - Andrew O Finley and Sudipto BanerjeeMarket Research and Preference Data - Adam SaganMultilevel Modeling for Scoial Networks and Relational Data - Marijtje A J Van DuijnName IndexSubject Index