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Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.


 
Series Editor's Introduction
 
About the Author
 
Preface
 
1. The Need for Multilevel Modeling
Background and Rationale

 
Theoretical Reasons for Multilevel Models

 
Statistical Reasons for Multilevel Models

 
Scope of Book

 
Online Book Resources

 
 
2. Planning a Multilevel Model
The Basic Two-Level Multilevel Model

 
The Importance of Random Effects

 
Classifying Multilevel Models

 
 
3. Building a Multilevel Model
Introduction to Tobacco Voting Data Set

 
Assessing the Need for a Multilevel Model

 
Model-building Strategies

 
Estimation

 
Level-2 Predictors and Cross-Level Interactions

 
Hypothesis Testing

 
 
4. Assessing a Multilevel Model
Assessing Model Fit and Performance

 
Estimating Posterior Means

 
Centering

 
Power Analysis

 
 
5. Extending the Basic Model
The Flexibility of the Mixed-Effects Model

 
Generalized Models

 
Three-level Models

 
Cross-classified Models

 
 
6. Longitudinal Models
Longitudinal Data as Hierarchical: Time Nested Within Person

 
Intra-individual Change

 
Inter-individual Change

 
Alternative Covariance Structures

 
 
7. Guidance
Recommendations for Presenting Results

 
Useful Resources

 
 
References

Sample Materials & Chapters

Chapter 1. The Need for Multilevel Modeling


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