Foundational Courses

Research Design and Causal Inference in Political Science 

Causal inference is at the heart of many of the key questions in political science: - why do some nations democratize but not others? What are the causes of war and peace? How do we improve the representation of marginalized groups in political life? But causal inference is hard. In this course, we will examine the principles of good research design, with a particular focus on causal inference. We begin by examining what we mean by ‘causation’, ‘causal inference’ and ‘causal explanation’. We then move to examining a suite of techniques for answering causal questions: - experiments in their various forms, natural or quasi experimental methods such as instrumental variables, difference in differences and regression discontinuity designs, and methods for estimating causal quantities from observational data such as matching, sensitivity analysis and directed acyclic graphs (DAGs). [Instructor: Dr. Charles Miller]

Foundations of Statistical Analysis in Political Science 

The aim of this course is to provide students with an accessible introduction to the fundamentals of quantitative political science research. The course will be focused on understanding types of data; data visualization; bivariate statistical methods (t tests, chi-squared tests, correlation); and multivariate statistical methods (simple and multiple regression). [Instructor: Dr. Patrick Leslie]

Advanced Courses (prerequisites: both foundational courses or permission of instructor)

Political Survey Design and Analysis 

The aim of this course is to provide students with an introduction to designing and administering high-quality surveys in political science, as well as the basics of survey analysis using R. The course will be focused on the following content areas: measuring concepts and ideas; questionnaire design and question wording; sampling and response maximisation; modes of data collection; and basic survey data analysis. [Instructor: Dr. Jill Sheppard]

Introduction to Political Text Analysis 

This course surveys methods for systematically extracting quantitative information from political text for social scientific purposes, starting with classical content analysis and dictionary-based methods, to classification methods, and state-of-the-art scaling methods and topic models for estimating quantities from text using statistical techniques. The course lays a theoretical foundation for text analysis but mainly takes a very practical and applied approach, so that students learn how to apply these methods in actual research. [Instructor: Prof. Ken Benoit]

Bayesian Statistics for Politics 

The aim of this course is to provide an introduction to the Bayesian approach to statistical inference and estimation for applications in political science using the statistical modelling package R. Emphasis will be placed on the understanding the basic framework of Bayesian statistics, comparison with traditional frequentist approaches, the implementation of these models, the interpretation of results, and the evaluation of model fit.  Models considered include. [Instructor: Dr. Shawn Treier]

Introduction to Critical Discourse Analysis 

The aim of this course is to introduce students to the foundations of Critical Discourse Analysis. The course will provide an overview of the foundations of CDA in critical and social theory, hermeneutics, social constructivism, and socio-linguistics before introducing students to the conceptual framework and analytical tools of CDA.  [Instructor: Dr. April Biccum]

Introduction to Social Network Analysis 

The “network perspective” puts emphasis on social ties between actors, rather than individual characteristics, in understanding behaviour and outcomes. This course provides a practical introduction to the network perspective and social network analysis (SNA), with a focus on applications in social science, in particular political analysis. Students will learn about collecting and storing social network data, and will gain practical experience in visualising networks and constructing basic SNA metrics. There will also be an introduction to exponential random graph models (ERGM), which provide a statistical inferential framework for modelling interdependencies in social tie formation such homophily, reciprocity and transitivity. The course involves the use of R, with a focus on igraph for network visualisation and basic SNA, and statnet for ERGM. [Instructor: Professor Robert Ackland]

Game Thoery in Political Science 

This course will introduce the basics of game theory and show how they can be used to help explain political phenomena. We will cover normal form, extensive form, and repeated games and discuss what they suggest about the nature of coordination problems and political institutions. The course will touch on a range of topics familiar to political scientists, including voting, disarmament, and the regulation of public goods. The goal is to equip participants with the knowledge they need to use game-theoretic models in future research. [Instructuor: Dr. Will Bosworth]

Advanced Political Text Analysis 

The course covers advanced methods for systematically extracting quantitative information from political text for social scientific purposes, building on the foundations from Political Text Analysis. The topics covered include: statistical scaling of texts to measure latent variables; topic models for estimating quantities from text using statistical techniques; classification methods for predicting document classes; clustering methods for text and word features; and ways to model and use word embedding to further improve model accuracy. [Instructor: Prof. Ken Benoit]