Winter School in Empirical Research Methods
LONGITUDINAL DATA ANALYSIS
PREREQUISITES (KNOWLEDGE OF TOPIC)
Comfortable familiarity with univariate differential and integral calculus, basic probability theory, and linear algebra is required. Students should have completed Ph.D.-level courses in introductory statistics and linear regression models, up to the level of Regression III. Familiarity with discrete and continuous univariate probability distributions will be helpful.
Students will be required to provide their own laptop computers.
All analyses will be conducted using the R statistical software. R is free, open-source, and runs on all contemporary operating systems. The instructor will also offer limited support for Stata and SAS.
The subject matter of the course is regression models for data that vary both over cross-sectional units and across time. The course will begin with a discussion of the relevant dimensions of variation in such data, and discuss some of the challenges and opportunities that such data provide. It will move on to models for one-way unit effects (fixed, between, and random), models for complex panel error structures, dynamic panel models, and nonlinear models for discrete dependent variables. The second part of the course will focus on models for time-to-event (“survival,” or “event history”) data. In every case, students will learn the statistical theory behind the various models, details about estimation and inference, and techniques for the substantive interpretation of statistical results. Students will also develop statistical software skills for fitting and interpreting the models in question, and will use the models in both simulated and real data applications. Students will leave the course with a thorough understanding of both the theoretical and practical aspects of conducting analyses of longitudinal data.
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