Multilevel Modeling

Applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™
George David Garson
Multilevel Modeling
August 2019 | 552 pages | Sage US
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Description

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.



Contents

Preface

Preface

Acknowledgments

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 1: Data Used in Examples. Refers to Student Companion Website

Appendix 2: Reporting Multilevel Results

Appendix 2: Reporting Multilevel Results

References

References

Index

Index

Description

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.



Contents

Preface

Preface

Acknowledgments

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 1: Data Used in Examples. Refers to Student Companion Website

Appendix 2: Reporting Multilevel Results

Appendix 2: Reporting Multilevel Results

References

References

Index

Index

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Multilevel Modeling

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

George David Garson
Cancel Print

August 2019 | 552 pages | Sage US

Format Published Date ISBN Price

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.




Table Of Contents:

  • 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

Recent Product Reviews:

“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

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