Multilevel Modeling Quantitative Applications in the Social Sciences 2nd Edition by Douglas Luke – Ebook PDF Instant Download/Delivery: 1452274761 ,9781452274767
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ISBN 10: 1452274761
ISBN 13: 9781452274767
Author: Douglas Luke
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.
Multilevel Modeling Quantitative Applications in the Social Sciences 2nd Edition Table of contents:
Chapter 1: Introduction to Multilevel Modeling
- The Need for Multilevel Modeling
- Key Concepts in MLM
- Hierarchical Data Structures
- The Assumptions of Multilevel Models
- A Brief Overview of MLM Techniques
Chapter 2: Basics of Multilevel Modeling
- The Basic Multilevel Model
- Level 1 and Level 2 Models
- The Random Intercept Model
- Estimating Parameters in MLM
- Interpreting the Output from MLM
Chapter 3: Variance Partitioning and Intraclass Correlation
- Understanding Variance Partitioning
- Intraclass Correlation (ICC) and its Meaning
- Application of ICC in Multilevel Models
- Decomposing Variance in Multilevel Data
Chapter 4: Random Slopes and the Cross-Level Interaction
- Random Slopes Models
- Cross-Level Interactions
- The Interpretation of Cross-Level Interactions
- Applications of Random Slopes Models
Chapter 5: Estimation Methods for Multilevel Models
- Maximum Likelihood Estimation (MLE)
- Restricted Maximum Likelihood Estimation (REML)
- Bayesian Estimation Methods
- Comparing Estimation Techniques
Chapter 6: Model Assumptions and Diagnostics
- Assumptions in Multilevel Models
- Model Fit and Evaluation
- Residual Diagnostics
- Identifying and Dealing with Model Violations
- Checking for Homoscedasticity and Normality
Chapter 7: The Random Coefficient Model
- Understanding Random Coefficients
- Applications of Random Coefficients in MLM
- Estimation of Random Coefficients
- The Interpretation of Random Coefficients
Chapter 8: Multilevel Models with Categorical Outcomes
- Modeling Categorical Dependent Variables
- Multilevel Logistic Regression
- Multilevel Poisson Regression
- Interpreting Categorical Outcomes in MLM
Chapter 9: Multilevel Structural Equation Models
- Introduction to Structural Equation Modeling (SEM)
- Multilevel SEM Models
- Combining MLM and SEM Techniques
- Estimation and Interpretation of Multilevel SEMs
Chapter 10: Model Building and Testing
- The Process of Building a Multilevel Model
- Model Comparison and Selection
- Model Testing in MLM
- The Role of Likelihood Ratio Tests
- AIC and BIC for Model Comparison
Chapter 11: Applications of Multilevel Modeling
- Educational Research: Teacher and Student-Level Variables
- Organizational Research: Employees and Organizational Levels
- Health and Medical Research: Patient and Provider-Level Variables
- Social and Psychological Research: Individual and Group-Level Effects
Chapter 12: Advanced Topics in Multilevel Modeling
- Multilevel Growth Models
- Longitudinal Data and Time-Series Models
- Multilevel Time Series Models
- Multilevel Models with Complex Sampling Designs
Chapter 13: Software for Multilevel Modeling
- Introduction to MLM Software
- Using SPSS for Multilevel Modeling
- R and the
lme4Package - HLM and MLwiN for Advanced MLM Analyses
- Choosing the Right Software for Your Analysis
Chapter 14: Conclusion
- Summary of Key Takeaways
- Best Practices in Multilevel Modeling
- The Future of Multilevel Modeling in Social Science Research
Appendices
- Appendix A: Multilevel Modeling Formulas
- Appendix B: Glossary of Terms
- Appendix C: Further Reading and Resources
References
Index
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