

This course is the first course
in applied statistical methods for social scientists. Students
will
learn a variety of basic crosssection 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.

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
