2024-2025

Theory of International Relations

What drives state behavior in the international system? What are the major causes of international and civil conflict? Why do states cooperate with one another on issues such as trade, migration, human rights, and peace? How does power, hierarchy, gender, and race shape global politics? What does the future look like for international relations?

Theory is essential to answering these questions and explaining the complex dynamics of global politics. This PhD-level course is designed to prepare students to conduct research and coursework in international relations. The course introduces students to core theories and empirical analysis of international relations in a scientific and rigorous way. 

We begin the course with the fundamental concepts and theories of international relations. We then transition to an overview of what causes countries to engage in international conflict and cooperation. From there, we explore key themes and approaches within the subfields of international organizations, international political economy, and conflict and political violence. We conclude with a discussion of where international relations theory should head next.

The course centers on active participation, discussion, and student collaboration. Each week, students will complete the assigned readings, provide insights on the topics, and work together on interactive activities. Students will learn how to evaluate theoretical arguments with empirical evidence, identify new research questions, and use theoretical frameworks to analyze current and historical events.

Graduate syllabus

Text as Data

Automated text analysis has become widely used in the social sciences following recent innovations in machine learning and the increased digitalization of political texts. This PhD-level course introduces students to the theoretical underpinnings of text analysis and the different techniques for systematically extracting and analyzing text with applications to political science topics. The focus of the course is on practical applications that allow students to apply cutting edge statistical and computational methods to their own research. 

We start the course with the conceptual foundations of quantitative text analysis. We then proceed to consider how we can extract, pre-process, and describe social text data. After that, we explore supervised and unsupervised machine learning methods that can be used to measure and analyze textual content. Students will become familiar with dictionary methods, supervised classification models, sentiment analysis, clustering, structural topic models, word embeddings, and replication and validation.

Active participation in class discussions and computer labs is central to the course. Each week, students will complete the assigned readings, provide insights on the topics, and complete in-class programming activities. Students will develop a general understanding of the text as data literature and will focus in-depth on one method from the course by developing an independent research data paper.

Graduate syllabus

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Human Rights
Graduate syllabus
Undergraduate syllabus

Civil Conflict
Graduate syllabus 
Undergraduate syllabus
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Terrorism and Political Violence
​Graduate syllabus
Undergraduate syllabus