Course options

Tutor: Vicente Dinis Valentim

From the effect of trust in science to the impact of gender quotas on political efficacy, most questions social scientists want to tackle are causal. Showing a causal effect, however, is far from easy.

In our Causal Inference course, students will learn a number of state-of-the-art tools to tackle difficulties in isolating a single factor whose effect one wants to study.

We will:

• Discuss three popular methods of causal inference in the social sciences: instrumental variables, difference-in-differences, and regression discontinuity.
• Devote one session to thinking about how these designs can be applied to the research topics each of the students is interested in.
• Achieve a solid grasp of the intuition and logic behind each of these methods.
• Enable students to understand more recent developments in these methods and be ready to follow the methodological literature on causal inference.

Tutor: Tom O' Grady

Texts such as political speeches, social media posts, newspaper articles and international treaties convey a wide range of important information for social scientists. They are now available in digitised form on an unprecedented scale, allowing us to turn texts into data and study them systematically.

This largely practical and hands-on course–relevant for students from any field of social science–will teach students to collect and analyse texts using statistical and computational methods.

We will:

• Show students how to conduct original research with text as data.
• Cover the major approaches to processing and describing documents prior to analysis.
• Explore statistical methods that describe, classify and scale documents in order to infer characteristics or traits of their authors, such as partisanship.
• Finish with the basics of web-scraping for automated text collection online.

Tutor: Spyros Kosmidis

This is an introductory course on data analysis for social scientists and it is designed for students with little or no knowledge of quantitative methods. 

We will:

• Cover a wide range of research designs (e.g: survey, cross- national, experimental etc)
• Teach the basics of data management/recoding, data visualization, statistical modelling and  interpretation.
• Divide the course into theory lectures and hands-on lab sessions using RStudio.
• By the end of the course students will be able to perform quantitative analysis for their own projects.

Tutor: Marnie Howlett

This course introduces students to the concepts, techniques, and data used with and collected using qualitative research methods. It covers themes like process tracing, research ethics and reflexivity, and the presentation and evaluation of qualitative research projects.

We will:

• Address and consider various aspects of qualitative research design, including research questions, case selection and small-N comparisons
• Introduce several data collection techniques for qualitative research: interviews, focus groups, ethnography/participant observation, textual/document analysis, and archival research.
• Equip students with the knowledge, tools, practical skills, and confidence to independently conduct qualitative research.

Tutor: Andreas Murr

Predicting social phenomena can be hard, but it does not always have to be. Recent advances in artificial intelligence and machine learning (ML) have given social scientists a toolkit to better predict and measure social phenomena. 

We will:

• Introduce students to the fundamental theories and concepts used across ML (e.g: maximum likelihood estimation, classification, overfitting, and cross-validation),
• Show students how to apply various popular ML techniques (e.g: LASSO, random forests, bayesian additive regression trees and others). 
• Empower students by the end of the course to apply the basic ML algorithms using R.