Introduction to Statistics for Political and Social Scientists Workshop

How can we detect voting irregularities?
What are the conditions for the onset (or cessation) of civil war?
How do democracies choose electoral systems?
In what sense (if any) does democracy (or trade) facilitate international cooperation?

Quantitative political methodology addresses these questions, and many others, by developing statistical methods that combine data analysis with political science theory.

This course is an introduction to the tools used in basic quantitative political methodology. The first half covers introductory (univariate) statistics; the second half focuses on regression models.

Our days are split into a three-hour teaching block in the morning, and a two-hour lab session in the afternoon. During the lab sessions, we will cover the same topics as in the morning lectures, but you will get hands-on experience in working with the methods learned in the morning.

Each day, we work on one problem set, using R. First, you'll get some time to solve the questions alone or in teams. Then, to keep everyone on the same page, we will go over the problems together and solve them, based on participants’ answers.

Day 1
After clarifying basic terminology, we define what variables are, and then discuss sampling techniques (simple random sample, cluster sampling, stratified sampling) and randomisation. Then we discuss descriptive statistics, for example how to use tables and graphs to summarise (and better understand) the data, and also the centre and the variability of variables. I will also present bivariate descriptive statistics (in table from and as graphs).

Day 2
We start by discussing probability distributions for discrete and continuous variables, with particular focus on normal distribution. Then we talk about sampling distributions, and how to use sample data to estimate population parameters (such as point and interval estimates). We also cover the choice of the sample size.

Day 3
We focus mostly on hypothesis testing. I describe the logic of significance tests, distinguish type I and type II errors, and teach you how to employ statistical tests to compare two groups.

Day 4
We start by covering associations between categorical variables, how to detect patterns of association, and how to measure association in contingency tables. Then I introduce simple linear regression. We cover ordinary least squares, interpreting linear models, model assumptions and violations, and graphical representation.

Day 5
We discuss multiple regression analysis, the concept of control variables, and the difference between correlation and causation. Then we will try to improve the model by revisiting the model assumptions, and establish how to detect (and correct) potential model violations. We will also cover interaction effects.

The course is taught in R, a powerful and versatile computing environment. R has the huge advantage that is is free, open software, and you can work on your own computer if you want to during the classroom sessions. Computers are of course provided in the lab sessions.

In the first lab session, I will introduce some basics of R, but if you are not yet familiar with R, please take Thorsten Schnapp's short Introduction to R course before signing up for this one.

The course is for beginners and no prior statistical (or computing) knowledge is required. However, we cover many topics in a relatively short period of time. Based on past years' experience, participants find this course very challenging. You should, therefore, be prepared to work intensively throughout each day.

Students on this course usually make great progress, and come to understand the most important principles of statistics. They learn to work with data and implement basic analyses, like running linear regression models, and acquire the knowlege to sign up for courses covering more complex statistical techniques.

By the end of the course, you should be familiar enough with R to use it independently for your own projects.

No statistical knowledge is required, though we will rely strongly on R during lab sessions. If you are not familiar with this software, take Thorsten Schnapp's short Introduction to R course beforehand.