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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.
| 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 |
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