These are slides I display in class while teaching. My strategy is to go through as much material as possible at each lecture, subject to the constraint that everyone follows what I'm doing. The speed at which I go is therefore dependent on the composition of each year's class and the questions that arise. As such, the slides below are not broken up into distinct weeks (I seem to go through around 15-20 pages in a weekly session lasting almost 2 hours, but issues and topics not represented here are covered most weeks). Separate PDF versions appear for teaching (i.e,. with pauses, etc.) and for (color) printing. I update these slides almost continuously while I teach. I'd appreciate if you would contact me with any comments, corrections, or suggestions. This course is available for Harvard students and for others via distance learning; see the syllabus.

1. The basics. [to print]: Introduction, outline, What is statistics?, What is the field of political methodology?, notation, probability, probability densities, statistical simulation
2. Theories of Inference. [to print]: alternative theories of inference (Bayes, likelihood, Neyman-Pearson hypothesis testing, etc.)
3. Single Equation Models. [to print]: binary variable models, interpretation and presentation via simulation, ordinal dependent variables, how do you know which model is better?, grouped binary variables, event counts, simple duration models and censoring.
4. Causal Inference
• Detecting Model Dependence [to print]: Sensitivity to parametric assumptions, revealing inferences too far from the data to have empirical answers, the curse of dimensionality, extrapolation, measures of distance from the data.
• Matching to Ameliorate Model Dependence [to print]: Matching methods as nonparametric preprocessing to reduce model dependence in parametric causal inference.
• Coarsened Exact Matching [to print]: A simple, powerful, and easy-to-use method of matching that avoids problems with existing methods.
• Research Designs [to print]: an overview of how key features of various observational and experimental research designs, and the designs themselves, reduce components of error in estimating causal effects.
5. Multiple Equation Models
• Analysis Models. [to print]: identification, how multiple and single equation models differ, seemingly unrelated regression models, reciprocal causation, multiple equation reparametrization, multinomial choice models (multinomial probit, multinomial logit), independence of irrelevant alternatives, conditional logit.
• Missing Data Modeling. [to print] problems with listwise deletion, assumptions, general purpose methods, application specific methods, multiple imputation, computational algorithms, a detailed example, Amelia software
6. Time Series Fundamentals. [to print]: the basics of time series models.
7. Rare Events
• Time Invariant Models. [to print] Rare events, relationship to logistic regression, classic case-control research designs (how and why to select on the dependent variable), robust bayesian analysis, reporting standards in case-control studies, ReLogit software.
• Time Varying Models [to print] time varying quantities of interest; understanding hazard rates; exponential, Weibull, and Cox Proportional hazard duration models, density case control models.
8. Cross-Cultural and Interpersonal Comparability in Survey Research. Part I [to print]: Differential item functioning, measurement in survey research; modeling issues: random effects, conditional estimation, parametric methods, introduction to nonparametric methods. Part II [to print]: Nonparametric methods.
9. Ecological Inference [to print]: Making inferences about individual behavior from aggregate, group-level data. History of the ecological inference problem, Goodman's model, the Davis-Duncan bounds, King's EI model, what can go wrong and what to do about it, and model extensions.
10. Text Analysis: Statistical analysis when the observation is a text document of some kind. Part I: Estimating the average in a category vs. individual classification, dealing with measurement error, extensions to an apparently unrelated application in epidemiology and public health Part I [to print]. Part II: Unsupervised learning via cluster analysis; why humans are incapable of learning from text as well as computers Part II [to print].
11. More to come...
12. See also public presentations