- Understanding and applying Bayesian methods with important applications; using these methods to produce innovative research outputs.
- Students will be able to design and interpret Bayesian statistical analyses, using prominent software such as JAGS and STAN.
- Students will learn how to design and implement Bayesian multilevel models and measurement models.
- COURSE PLANThe course will consist of 14 classes, each consisting of a lecture and seminar. Most classes will be divided into 1) a lecture in which students will be introduced to the theoretical and practical issues involved in a method, and 2) a seminar in which students will work with the instructor and small groups to implement the techniques to which they were exposed. There will also be two days of in-class presentations and one day for collaborative work on projects. The class will be divided into four thematic groups, each with two or more lectures: 1) an introduction to Bayesian statistics, 2) Bayesian regression analysis, 3) hierarchical models, and 4) latent variable models.
- Class 1-2: Introduction to Bayesian statistics
- Class 3-6: Bayesian regression analysis
- Class 7: Model choice / Simulation analyses
- Class 8: In-class presentations of replication analyses
- Class 9-10: Hierarchical models
- Class 11-13: Latent variable models
- Class 14: Final presentations
- Interim assessment (2 module)0.35 * Final paper + 0.1 * Homework + 0.2 * Participation + 0.35 * Replication project
- Daniel Pemstein, Stephen Meserve, & James Melton. (2010). Democratic Compromise: A Latent Variable Analysis of Ten Measures of Regime Type.” Political Analysis 18(4):426.
- Simon Jackman. (2009). Bayesian Analysis for the Social Sciences. Wiley.
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian Data Analysis (Vol. Third edition). Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1763244
- Shawn Treier, & Simon Jackman. (2008). Democracy as a latent variable.