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Multilevel Modeling
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Multilevel Modeling
Applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™

August 2019 | 552 pages | SAGE Publications, Inc
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Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLM™ provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.

 
Preface
 
Acknowledgments
 
About the Author
 
Chapter 1 • Introduction to Multilevel Modeling
Overview

 
What Multilevel Modeling Does

 
The Importance of Multilevel Theory

 
Types of Multilevel Data

 
Common Types of Multilevel Model

 
Mediation and Moderation Models in Multilevel Analysis

 
Alternative Statistical Packages

 
Multilevel Modeling Versus GEE

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 2 • Assumptions of Multilevel Modeling
About This Chapter

 
Overview

 
Model Specification

 
Construct Operationalization and Validation

 
Random Sampling

 
Sample Size

 
Balanced and Unbalanced Designs

 
Data Level

 
Linearity and Nonlinearity

 
Independence

 
Recursivity

 
Missing Data

 
Outliers

 
Centered and Standardized Data

 
Longitudinal Time Values

 
Multicollinearity

 
Homogeneity of Error Variance

 
Normally Distributed Residuals

 
Normal Distribution of Variables

 
Normal Distribution of Random Effects

 
Convergence

 
Covariance Structure Assumptions

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 3 • The Null Model
Overview

 
Testing the Need for Multilevel Modeling

 
Likelihood Ratio Tests

 
Partition of Variance Components

 
Examples

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 4 • Estimating Multilevel Models
Fixed and Random Effects

 
Why Not Just Use OLS Regression?

 
Why Not Just Use GLM (ANOVA)?

 
Types of Estimation

 
Robust and Cluster-Robust Standard Errors

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 5 • Goodness of Fit and Effect Size in Multilevel Models
Overview

 
Goodness of Fit Measures and Tests

 
Effect Size Measures

 
Effect Size and Endogeneity

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 6 • The Two-Level Random Intercept Model
Overview

 
SPSS

 
Stata

 
SAS

 
HLM 7

 
R

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 7 • The Two-Level Random Coefficients Model
Overview

 
SPSS

 
Stata

 
SAS

 
HLM 7

 
R

 
Significance (p) Values for Variance Components

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 8 • The Three-Level Unconditional Random Intercept Model with Longitudinal Data
Overview

 
SPSS

 
Stata

 
SAS

 
HLM 7

 
R

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 9 • Repeated Measures and Heterogeneous Variance Models
Overview

 
SPSS

 
SAS

 
Stata

 
R

 
HLM 7

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 10 • Residual and Influence Analysis for a Three-Level RC Model
About This Chapter

 
Overview

 
Why Residual Analysis?

 
Data

 
Model

 
Model Diagnostics

 
SAS

 
Stata

 
SPSS

 
HLM 7

 
R

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 11 • Cross-Classified Linear Mixed Models
Overview

 
Data

 
Model

 
Research Purpose

 
Stata

 
SPSS

 
SAS

 
HLM 7

 
R

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Chapter 12 • Generalized Linear Mixed Models
Overview

 
Estimation Methods

 
Data

 
Model

 
Stata

 
SAS

 
SPSS

 
HLM 7

 
R

 
Summary

 
Glossary

 
Challenge Questions With Answers

 
 
Appendix 1: Data Used in Examples. Refers to Student Companion Website
 
Appendix 2: Reporting Multilevel Results
 
References
 
Index

Supplements

Student Website
  • Downloadable data for all exercises
  • Downloadable figures and tables from the book
  • “Getting Started with R and RStudio” quick guide
  • FAQs on multilevel modeling

“The practical and hands-on approach in addition to using several software make this book appealing to a wide range of readers.”

Amin Mousavi
University of Saskatchewan

“This is a solid treatment of MLMs which illustrates implementation across all major MLM software.”

J.M. Pogodzinski
Department of Economics, San Jose State University

“This text effectively balances depth, complexity, and readability of a number of challenging topics related to multilevel modeling. The wealth of examples in many different software environments are fantastic.”

Michael Broda
Virginia Commonwealth University

A fantastic resource that reflects a modern approach to modeling. Provides an introduction that touches on the many nuances of mixed models without overwhelming the reader with technical details and theory. Some content on model notation and specification (non-software) would be a good addition.

Dr Kirsten Eilertson
Statistics Dept, Colorado State University-Ft Collins
October 13, 2021
Report this review
Key features
  • Downloadable Data provided within the text offers a hands-on-approach to all discussed examples, reinforcing the students learning through practice.
  • End-of-chapter summaries provide an extended and valuable recap of what is important in each chapter helping students to understand the key takeaways.
  • Step-by-step instructions for the software allow students to walk through each package, even if they are unfamiliar with them.
  • Instructions for different platforms allow students to translate their knowledge to their employer's software of choice.
  • Chapter overviews, exercises, and key terms included throughout provide guideposts for students along the way to enhance their learning experience with ability to truly understand the material.
  • Challenge Questions force critical thinking about what has been read in each chapter. 
  • Extended coverage in Appendices and PowerPoint describes each dataset used, provides a unique guide to writing up the results, and answers frequently asked questions, providing students with additional tutorial material. 

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