

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.

Discrete Choice Models
: Ordered/Multinomial Logit/Probit Models, Sample Selection Model

Applied
Regression Models for CrossSection Data: Event
Count Models, Generalized Linear Models

Causal Inference:
Fixed Effects Regression, DifferenceinDifferences, Matching,
Propensity Score, Weighting, Doublyrobust 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 ProportionalHazard
Model, Competing Risks Models
