Introduction to QUANTITATIVE METHODS

9590A - Graduate Class

The objective of this course is to provide graduate students with the necessary statistical tools to make inferences about politics. We will cover fundamentals of probability theory, estimation, hypothesis testing and data visualization. These topics will be discussed with an eye on applications to research questions in all subfields of political science. Leaving this course, students will also be able to acquire, format, analyze, and visualize various types of data using the statistical programming language R.

All slides for the theory portion of the class are available on OWL.

The following laboratory material is provided to facilitate your learning in this class. 

You can make an office hours appointment here: https://calendly.com/e_brie.

lab material

  • Introducing basic R functions

  • Creating objects of different classes

  • Creating vectors and data frames

Introduction to R

Pre-semester

INTRODUCTION TO causal inference

Week 1

  • Importing datasets

  • Exploring properties of datasets and variables

  • Creating variables

  • Renaming variables

  • Merging datasets

  • Creating subsets of datasets

  • Creating sample spaces and subsetting event spaces

  • Combining and permuting observations with and without replacement

  • Computing the birthday paradox

Probability theory 1

Week 2

  • Generating random samples from distribution functions (with replacement)

  • Introducing loops

  • Calculating the standard deviation of the distribution of our bootstrap samples

Probability theory 2

Week 3

Estimation & Inference

Week 4

  • Estimating basic statistics on univariate data (mean, median, variance, s.d.)

  • Estimating basic statistics on bivariate data (covariance, correlation) and displaying trends using two-way tables

  • Graphing data in base R with density plots, histograms and scatterplots

Data visualization

Week 5

See scripts on OWL.

  • Generating a random binomial distribution

  • Testing the null hypothesis

  • Calculating critical values

  • Performing a t-test

Hypothesis Testing

Week 6

Midterm Exam & Fall Break

  • Loading a dataset from the car package

  • Graphing the relationship between two variables

  • Perform a simple linear regression analysis between two variables using lm()

  • Running a test on the residuals of our model

Linear models 1

Week 9

  • Loading a dataset from the datasets package

  • Graphing the relationship between two variables or more

  • Perform a simple and multivariate linear regression analysis using lm()

  • Introducing the concept of polynomial models

  • Running different OLS diagnostic tests.

Linear models 2

Week 10

  • Creating contingency tables

  • Calculate a chi-square and a Cramer’s V

  • Perform ANOVA and a Tukey’s test

Nominal & Ordinal data

Week 11

  • Identifying missing values across columns

  • Looking for patterns of missing data

  • Performing row-wise deletion

Missing data & generalization

Week 12

Final Exam