POL 573: Quantitative Analysis III


  Course Description

This course is the second course in applied statistical methods for social scientists. Building on the materials we covered in POL 572 or its equivalent (i.e., linear regression, structural equation modeling, instrumental variables, maximum likelihood estimation, discrete choice models), students will learn a variety of statistical methods including models for longitudinal data and survival data. 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. Prerequisite: POL 572 or equivalent.

  Lecture Slides

Quantitative Social Science at Princeton
Discrete Choice Models : Ordered/Multinomial Logit/Probit Models, Sample Selection Model
Applied Regression Models for Cross-Section Data: Event Count Models, Generalized Linear Models
Causal Inference: Fixed Effects Regression, Difference-in-Differences, Matching, Propensity Score, Weighting, Doubly-robust Estimator, Missing Data
Applied Regression Models for Longitudinal Data: Varying Intercept Models, Linear Mixed Effects Models, Generalized Linear Mixed Effects Models, Generalized Estimating Equations, Incidental Parameter Problem and Conditional Likelihood
Survival Data Analysis: Basic Concepts, Nonparametric Estimation of Survival Function, Parametric Regression Models, Cox Proportional-Hazard Model, Competing Risks Models


How to Make a Poster for Social Science Research
How to Write an Empirical Social Science Paper

© Kosuke Imai
 Last modified: Mon Dec 8 09:29:25 EST 2014