This focuses on models and data that arise from repeated observations of a cross-section of individuals, households or companies. These models have found important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also describes substantive applications from across the social sciences, showing the breadth and scope that these models enjoy. The applications are enhanced by real-world data sets and software programs in SAS and Stata.
• A flexible and easy teaching text - includes many real world data sets and exercises and technical appendix to allow instructor to choose the topics to cover • Contains many interesting historical references • Software examples in SAS and Stata
1. Introduction; Part I. Linear Models: 2. Fixed effects models; 3. Models with random effects; 4. Prediction and Bayesian Inference; 5. Multilevel models; 6. Random regressors; 7. Modeling issues; 8. Dynamic models; Part II. Nonlinear Models: 9. Binary dependent variables; 10. Generalized linear models; 11. Categorical dependent variables and survival models; Appendix A. Elements of Matrix Algebra; Appendix B. Normal distribution; Appendix C. Likelihood-based inference; Appendix D. Kalman Filter; Appendix E. Symbols and notation; Appendix F. Selected longitudinal and panel data sets; Appendix G. References.