Multilevel Modeling
Second Edition
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Go to College Publishing WebsiteDescription
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.
Contents
Series Editor's Introduction
Series Editor's Introduction
About the Author
About the Author
Preface
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
References
Additional materials
Description
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.
Contents
Series Editor's Introduction
Series Editor's Introduction
About the Author
About the Author
Preface
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
References
Additional materials
Reviews
December 2019 | 128 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
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.
Table Of Contents:
- 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
Recent Product Reviews:
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
The book offers insights and explanations from which both newcomers and seasoned experts can find benefit.
Timothy Ford, Ohio University
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
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
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