eBook The SAGE Handbook of Multilevel Modeling, 1st Edition

  • Marc A. Scott
  • Published By: SAGE
  • ISBN-10: 1446265978
  • ISBN-13: 9781446265970
  • DDC: 519.535
  • Grade Level Range: College Freshman - College Senior
  • 696 Pages | eBook
  • Original Copyright 2013 | Published/Released December 2014
  • This publication's content originally published in print form: 2013
  • Price:  Sign in for price



In this important 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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Notes on Contributors.
Multilevel Modeling.
1: Multilevel Model Specification and Inference.
2: The Multilevel Model Framework.
3: Multilevel Model Notation—Establishing the Commonalities.
4: Likelihood Estimation in Multilevel Models.
5: Bayesian Multilevel Models.
6: The Choice between Fixed and Random Effects.
7: Centering Predictors and Contextual Effects.
8: Model Selection for Multilevel Models.
9: Generalized Linear Mixed Models–Overview.
10: Longitudinal Data Modeling.
11: Complexities in Error Structures Within Individuals.
12: Design Considerations in Multilevel Studies.
13: Multilevel Models and Causal Inference.
14: Variations and Extensions of the Multilevel Model.
15: Multilevel Functional Data Analysis.
16: Nonlinear Models.
17: Generalized Linear Mixed Models: Estimation and Inference.
18: Categorical Response Data.
19: Smoothing and Semiparametric Models.
20: Penalized Splines and Multilevel Models.
21: Hierarchical Dynamic Models.
22: Mixture and Latent Class Models in Longitudinal and Other Settings.
23: Multivariate Response Data.
24: Practical Considerations in Model Fit and Specification.
25: Robust Methods for Multilevel Analysis.
26: Missing Data.
27: Lack of Fit, Graphics, and Multilevel Model Diagnostics.
28: Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?.
29: Software for Fitting Multilevel Models.
30: Selected Applications.
31: Meta-Analysis.
32: Modeling Policy Adoption and Impact with Multilevel Methods.
33: Multilevel Models in the Social and Behavioral Sciences.
34: Survival Analysis and the Frailty Model.
35: Point-Referenced Spatial Modeling.
36: Market Research and Preference Data.
37: Multilevel Modeling of Social Network and Relational Data.
Name Index.
Subject Index.