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

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September 2019 | 552 pages | SAGE Publications, Inc

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

Sample Materials & Chapters

Chapter 1: Introduction to Multilevel Modeling

Chapter 3: The Null Model


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ISBN: 9781544319292
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