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The SAGE Handbook of Multilevel Modeling
696
by Marc A. Scott (Editor), Jeffrey S. Simonoff (Editor), Brian D. Marx (Editor)
Marc A. Scott
The SAGE Handbook of Multilevel Modeling
696
by Marc A. Scott (Editor), Jeffrey S. Simonoff (Editor), Brian D. Marx (Editor)
Marc A. Scott
Hardcover(First Edition)
$215.00
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Overview
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.
Product Details
ISBN-13: | 9780857025647 |
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Publisher: | SAGE Publications |
Publication date: | 09/19/2013 |
Edition description: | First Edition |
Pages: | 696 |
Product dimensions: | 7.00(w) x 9.80(h) x 1.80(d) |
About the Author
Jeffrey S. Simonoff is Professor of Statistics at the NYU Stern School of Business. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He is author or coauthor of roughly 100 articles and five books on the theory and applications of statistics.
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.
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.
Table of Contents
Notes on ContributorsPrefaceMultilevel 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 Mc Culloch 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 Mc NicholasMultivariate 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 IndexFrom the B&N Reads Blog
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