POL 572: Quantitative Analysis II

 

  Course Description

This course is the first course in applied statistical methods for social scientists. Students will learn a variety of basic cross-section regression models (as time permits!) including linear regression model, discrete choice models, duration (or hazard) models, event count models, structural equation models, and others. Unlike traditional courses on applied regression modeling, I will emphasize the connections between these methods and causal inference, which is the primary goal of social science research. Prerequisites, POL 502 and POL 571.

  Lecture Slides

Quantitative Social Science at Princeton
Basic Principles of Statistical Inference : Modes of Statistical Inference, Sample Surveys, Randomized Experiments, Estimation, Confidence Intervals, Identification
Linear Regression : Linear Regression with a Single Variable, Linear Regression with Multiple Variables, Residual Diagnostics, Robust Standard Error, Regression Discontinuity Design, Violations of Exogeneity
Structural Equation Modeling : Linear Structural Equation Modeling, Causal Mediation Analysis, Instrumental Variables, Encouragement Design, Fuzzy Regression Discontinuity Design
Likelihood Inference : Maximum Likelihood Estimation, Bootstrap, Likelihood Ratio Test, Normal Regression, Logit/Probit Models
Discrete Choice Models : Ordered/Multinomial Logit/Probit Models, Sample Selection Model

© Kosuke Imai
 Last modified: Tue Sep 9 11:45:37 EDT 2014