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.
Table of Content
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 Mc Culloch 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 Mc Nicholas
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
About the author
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 ebooks from the same author(s) / Editor
146,564 Ebooks in this category
