Marc A. Scott & Jeffrey S. Simonoff 
The SAGE Handbook of Multilevel Modeling [EPUB ebook] 

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.

Daftar Isi

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

Tentang Penulis

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.