The SAGE Handbook of Multilevel Modeling 1st Edition by Marc Scott, Jeffrey Simonoff, Brian Marx 0857025643 9780857025647

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Product details:

ISBN 10: 0857025643

ISBN 13: 9780857025647

Author: Marc A. Scott, Jeffrey S. Simonoff, Brian D. Marx

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.

The SAGE Handbook of Multilevel Modeling 1st Table of contents:

PART I: MULTILEVEL MODEL SPECIFICATION AND INFERENCE

1 The Multilevel Model Framework

2 Multilevel Model Notation—Establishing the Commonalities

3 Likelihood Estimation in Multilevel Models

4 Bayesian Multilevel Models

5 The Choice Between Fixed and Random Effects

6 Centering Predictors and Contextual Effects

7 Model Selection for Multilevel Models

8 Generalized Linear Mixed Models—Overview

9 Longitudinal Data Modeling

10 Complexities in Error Structures Within Individuals

11 Design Considerations in Multilevel Studies

12 Multilevel Models and Causal Inference

PART II: VARIATIONS AND EXTENSIONS OF THE MULTILEVEL MODEL

13 Multilevel Functional Data Analysis

14 Nonlinear Models

15 Generalized Linear Mixed Models: Estimation and Inference

16 Categorical Response Data

17 Smoothing and Semiparametric Models

18 Penalized Splines and Multilevel Models

19 Hierarchical Dynamic Models

20 Mixture and Latent Class Models in Longitudinal and Other Settings

21 Multivariate Response Data

PART III: PRACTICAL CONSIDERATIONS IN MODEL FIT AND SPECIFICATION

22 Robust Methods for Multilevel Analysis

23 Missing Data

24 Lack of Fit, Graphics, and Multilevel Model Diagnostics

25 Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors?

26 Software for Fitting Multilevel Models

PART IV: SELECTED APPLICATIONS

27 Meta-Analysis

28 Modeling Policy Adoption and Impact with Multilevel Methods

29 Multilevel Models in the Social and Behavioral Sciences

30 Survival Analysis and the Frailty Model

31 Point-Referenced Spatial Modeling

32 Market Research and Preference Data

33 Multilevel Modeling of Social Network and Relational Data

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Marc Scott,Jeffrey Simonoff,Brian Marx,Multilevel