
Multilevel Modeling
Applications in STATA®, IBM® SPSS®, SAS®, R, & HLM™
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover
- Title Page
- Title Page
- Copyright Page
- Brief Contents
- Detailed Contents
- Preface
- Acknowledgments
- About the Author
- Publisher Note
- 1 Introduction to Multilevel Modeling
- 2 Assumptions of Multilevel Modeling
- 3 The Null Model
- 4 Estimating Multilevel Models
- 5 Goodness of Fit and Effect Size in Multilevel Models
- 6 The Two-Level Random Intercept Model
- 7 The Two-Level Random Coefficients Model
- 8 The Three-Level Unconditional Random Intercept Model With Longitudinal Data
- 9 Repeated Measures and Heterogeneous Variance Models
- 10 Residual and Influence Analysis for a Three-Level RC Model
- 11 Cross-Classified Linear Mixed Models
- 12 Generalized Linear Mixed Models
- Appendix 1 Data Used in Examples
- Appendix 2 How to Report Multilevel Results
- References
- Index