You are in: Africa
Change location

You are here

Book > Major Work > Quantitative/Statistical Research (General)

Disable VAT on Taiwan

Unfortunately, as of 1 January 2020 SAGE Ltd is no longer able to support sales of electronically supplied services to Taiwan customers that are not Taiwan VAT registered. We apologise for any inconvenience. For more information or to place a print-only order, please contact uk.customerservices@sagepub.co.uk.

The SAGE Handbook of Multilevel Modeling
Share
Share

The SAGE Handbook of Multilevel Modeling

First Edition
Edited by:
September 2013 | 696 pages | SAGE Publications Ltd
Download flyer
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.
 
Notes on Contributors
 
Preface
Multilevel Modeling

Jeffrey S Simonoff, Marc A Scott and Brian D Marx
 
PART ONE: MULTILEVEL MODEL SPECIFICATION AND INFERENCE
Jeff Gill and Andrew Womack
The Multilevel Model Framework
Marc A Scott, Patrick E Shrout and Sharon L Weinberg
Multilevel Model Notation - Establishing the Commonalities
Harvey Goldstein
Likelihood Estimation in Multilevel Models
Ludwig Fahrmeir, Thomas Kneib, and Stefan Lang
Bayesian Multilevel Models
Zac Townsend,Jack Buckley, Masataka Harada and Marc A Scott
The Choice between Fixed and Random Effects
Craig K Enders
Centering Predictors and Contextual Effects
Russell Steele
Model Selection for Multilevel Models
Geert Verbeke and Geert Molenberghs
Generalized Linear Mixed Models - Overview
Nan M Laird and Garrett M Fitzmaurice
Longitudinal Data Modeling
Vicente Núnez-Antón and Dale L Zimmerman
Complexities in Error Structures Within Individuals
Gerard van Breukelen and Mirjam Moerbeek
Design Considerations in Multilevel Studies
Jennifer Hill
Multilevel Models and Causal Inference
 
PART TWO: VARIATIONS AND EXTENSIONS OF THE MULTILEVEL MODEL
Ciprian M Crainiceanu, Brian S Caffo and Jeffrey S Morris
Multilevel Functional Data Analysis
Lang Wu and Wei Liu
Nonlinear Models
Charles E McCulloch and John M Neuhaus
Generalized Linear Mixed Models: Estimation and Inference
Jeroen Vermunt
Categorical Response Data
Jin-Ting Zhang
Smoothing and Semiparametric Models
Göran Kauermann and Torben Kuhlenkasper
Penalized Splines and Multilevel Models
Marina Silva Paez and Dani Gamerman
Hierarchical Dynamic Models
Ryan P Browne and Paul D McNicholas
Mixture and Latent Class Models in Longitudinal and Other Settings
Helena Geys and Christel Faes
Multivariate Response Data
 
PART THREE: PRACTICAL CONSIDERATIONS IN MODEL FIT AND SPECIFICATION
Joop Hox and Rens van de Schoot
Robust Methods for Multilevel Analysis
Geert Molenberghs and Geert Verbeke
Missing Data
Gerda Claeskens
Lack of Fit, Graphics, and Multilevel Model Diagnostics
Robert Crouchley
Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?
Andrzej T Galecki and Brady T West
Software for Fitting Multilevel Models
 
PART FOUR: SELECTED APPLICATIONS
Larry V Hedges and Kimberly S Maier
Meta-Analysis
James E Monogan III
Modeling Policy Adoption and Impact with Multilevel Methods
David Rindskopf
Multilevel Models in the Social and Behavioral Sciences
Ardo van den Hout and Brian D M Tom
Survival Analysis and the Frailty Model: The effect of education on survival and disability for older men in England and Wales
Andrew O Finley and Sudipto Banerjee
Point-Referenced Spatial Modeling
Adam Sagan
Market Research and Preference Data
Marijtje A J Van Duijn
Multilevel Modeling for Scoial Networks and Relational Data
 
Name Index
 
Subject Index

...This handbook attempts to cover an intricate and multifaceted topic and delivers what is expected of it very successfully and at good cost considering its all-purpose usefulness. This handbook is a must-have consultation book for researchers interested in multilevel modelling from a wide variety of disciplines, if not all.

Patricio Troncosco, Cathie Marsh Institute for Social Research, The University of Manchester
International Journal of Research and Method in Education

Select a Purchasing Option

Order from:

ISBN: 9780857025647
£150.00

Related Products

Multilevel Analysis
Multilevel Analysis
Applied Regression Analysis and Generalized Linear Models
Applied Regression Analysis and Generalized Linear Models
Show More Show Less
SAGE Knowledge Promotion

SAGE Knowledge is the premier social sciences platform for SAGE and CQ Press book, reference and video content.

The platform allows researchers to cross-search and seamlessly access a wide breadth of must-have SAGE book and reference content from one source.

Find out more
SAGE Research Methods Promotion

SAGE Research Methods is a research methods tool created to help researchers, faculty and students with their research projects. SAGE Research Methods links over 175,000 pages of SAGE’s renowned book, journal and reference content with truly advanced search and discovery tools. Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and more.

With SAGE Research Methods, researchers can explore their chosen method across the depth and breadth of content, expanding or refining their search as needed; read online, print, or email full-text content; utilize suggested related methods and links to related authors from SAGE Research Methods' robust library and unique features; and even share their own collections of content through Methods Lists. SAGE Research Methods contains content from over 720 books, dictionaries, encyclopedias, and handbooks, the entire “Little Green Book,” and "Little Blue Book” series, two Major Works collating a selection of journal articles, and specially commissioned videos.

Find out more