[POLS 4150] Quantitative Methods in Political Science

Office Hours: Tuesdays, 2:30-3:30, Baldwin 414 (or by appointment)

Syllabus

Introduction to R: Code and Examples - Load and manipulate 2016 precinct-level presidential election data.

Significance Tests in R (Hypothesis Testing)

Linear Regression in R with code.

PROBLEM SETS

Problem set 1

Questions - ps1.pdf
Data - 2016 Presidential Election Returns for Georgia Counties - gadata.csv

Problem set 2

Questions - ps2.pdf
Data - 2016 and 2008 Election Data and U.S. County Demographics - votes-trunc.csv
Solutions: Solutions, Solutions with R Code and Explanations for "Election Forensics" and "Hillary's 2020 Vision"

LECTURE NOTES

01-05-Course Overview

01-10-Introduction to Quantitative Methods and R - What is data? Parameters and statistics. What are variables? How do we go about measuring variables?

2016 Presidential Election Data by Precinct and State (thanks to Stephen Pettigrew!) - president-long.csv
R code - election-data.r

01-12-R, Randomization and Sampling - Continuation of discussion about variables. Manipulating variables in R. Randomization and sampling. Simple random sampling.

01-19 Observational studies, experiments and bias.

01-24 Describing and summarizing data

01-26 Measures of spread, bivariate and descriptive statistics

01-31 Introduction to Probability, Discrete and Continuous Variable Distributions I

02-02 Introduction to Probability, Discrete and Continuous Variable Distributions II

02-07 The Normal Distribution

02-09 The Normal Distribution and Sampling Distributions

02-14 Point Estimation, Confidence Intervals and Significance Tests (Hypothesis Testing)

02-16 Significance Tests I

02-21 Significance Tests II

02-28 Type I and Type II Errors, Two Group Comparisons

03-02 Type I and Type II Errors, Two Group Comparisons