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Multilevel Modeling
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Multilevel Modeling

Second Edition


April 2020 | 128 pages | SAGE Publications, Inc
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

With growing statistical software package costs, more researchers are using R than ever before. This book allows researchers to do more when using R.

Gina R. Gullo
Lehigh University
Review

The book offers insights and explanations from which both newcomers and seasoned experts can find benefit.

Timothy Ford
Ohio University
Review

Because of the author’s pedagogically masterful presentation of multi-level modeling, the otherwise challenging journey to this topic now becomes not only smooth but also enjoyable.

Lin Ding
Ohio State Univesity
Reviewer

This is a very well-written and organized book. The author uses practical examples to help the readers understand the reasoning and steps of a complex statistical approach. I have used the first edition of this book in my class, and definitely plan on using the second edition too. This is a book that I would highly recommend to clinical researchers who are interested in learning multilevel modeling.

Dorina Kallogjeri
Washington University in Saint Louis
Review

Multilevel Modeling provides a thorough and accessible introduction to multilevel models. Through extensive examples, the author expertly guides the reader through the material addressing interpretation, graphical presentation, and diagnostics along the way.

Jennifer Hayes Clark
University of Houston
review

The new second edition is even better than the first. The models presented are closely linked to an extended example that students can readily identify with. 

Richard R. Sudweeks
Brigham Young University
Review

Sample Materials & Chapters

Chapter 1. The Need for Multilevel Modeling


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