- First session. No readings.
- Jeff Gill. "Generalized Linear Models."
- Nathaniel Beck. "Estimating Grouped Data Models with a Binary-Dependent Variable and Fixed Effects via a Logit versus a Linear Probability Model: The Impact of Dropped Units." (2019)
Jeff Gill. "Monte Carlo Methods."
Jonathon Homola and Jeff Gill. “A Flexible Class of Bayesian Frailty Models For Political Science Data.” (2019)
Jeff Gill. “Monte Carlo and Related Iterative Methods.” Chapter from Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition (2014)
Graeme Blair and Kosuke Imai. “Statistical Analysis of List Experiments.” (2012)
Gary King and Richard Nielson. “Why Propensity Scores Should Not Be Used for Matching.” (2019)
Kosuke Imai, In Song Kim, and Erik Wang. “Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.” (2020)
Trevor Park and George Casella. “The Bayesian Lasso.” (2008)
Regularization: Ridge, Lasso and Elastic Net (Tutorial)
Jennifer L. Hill and Hanspeter Kriesi. “Classification by Opinion-Changing Behavior: A Mixture Model Approach.” (2001)
- 1/15: Principles of Inference and Testing
- 1/22: Generalized Linear Models
- 2/19: Green, K.M. and Stuart, E.A., 2014. Examining Moderation Analyses in Propensity Score Methods: Application to Depression and Substance Use. Journal of Consulting and Clinical Psychology, 82(5), p.773.
- 2/26: Missing data
- 4/2: Survival models
- 4/9: Survival models (cont'd)
- 4/16: King, G. and Roberts, M.E., 2015. How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About it. Political Analysis, 23(2), pp.159-179.
- 4/30: Cranmer, S.J. and Desmarais, B.A., 2017. What Can We Learn from Predictive Modeling?. Political Analysis, 25(2), pp.145-166.