Course options

Tutor: Spyros Kosmidis (Oxford)

This is an introductory course on data analysis for social scientists and is designed for students with little or no knowledge of quantitative methods. The course will cover a wide range of research designs (e.g. survey, cross- national, experimental etc) and students will learn the basics of data management/recoding, data visualization, statistical modeling and interpretation. The course will be divided into theory lectures and hands-on lab sessions using RStudio.

The course is well-suited for students seeking an introduction to quantitative methods or those looking to refresh their knowledge in this field. Upon completion, students will be in a position to analyse their own data and confidently engage with academic work on quantitative social science.

Tutor: Tom Robinson (LSE)

Computational techniques broadly referred to as "machine learning” (ML) are attracting increasing attention in the social sciences. This course will introduce students to the fundamental theories and concepts used across ML techniques (e.g maximum likelihood estimation, classification, overfitting, and cross-validation) and how to apply various popular ML techniques (e.g. LASSO, random forests, bayesian additive regression trees, and neural networks).

This course will also pay particular attention to inferential problems regarding how best to use ML methods in social science contexts and discuss practical issues (e.g. computational power and parallelisation). Students on this course are required to be familiar with probability and statistics (OLS, hypothesis testing, logistic regression) and to have basic knowledge of the R programming language.

Tutor: Vicente Dinis Valentim (Oxford)

From the effect of trust in science to the impact of gender quotas on political efficacy, most questions that social scientists want to tackle are causal. Showing a causal effect, however, is far from easy. In the social sciences, unlike in the natural sciences, it is often impossible to isolate a single factor whose effect one wants to study. Because social phenomena are often highly correlated, it is very hard to pinpoint the effect of a single one.

In this course, students will learn a number of state-of-the-art tools to tackle these difficulties. In this course, we will discuss three popular methods of causal inference in the social sciences: instrumental variables, difference-in-differences, and regression discontinuity. We will also devote one session to thinking about how these designs can be applied to the research topics each of the students is interested in.

This is an introductory course and our focus throughout will be on achieving a solid grasp of the intuition and logic behind each of these methods. After this course, students will be capable of understanding more recent developments in these methods and will be ready to follow the methodological literature on causal inference.

Tutor: Rachel Bernhard (Oxford)

Social science experiments have become a necessary tool for researchers. This course is designed for social scientists who seek a comprehensive understanding of experimental methods. Participants will be exposed to a number of research designs and will be taught the key principles and practices of experimentation.

The course covers a range of topics including randomization and experimental manipulation, with an additional focus on internal and external validity. The lectures will be accompanied with lab sessions using R and participants will be taught statistical methods to analyse experiments.

During the sessions students will learn to formulate hypotheses, design experiments, collect and analyse data, and draw insights from the results.

Tutor: Marnie Howlett (Oxford)

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.

We will introduce several data collection techniques for qualitative research: interviews, focus groups, ethnography/participant observation, textual/document analysis, and archival research.

The aim of the course is to equip students with the knowledge, tools, practical skills, and confidence to independently conduct qualitative research.

Tutor: Jody LaPorte (Oxford)

The aim of this course is to discuss both the theory and practice of process tracing, which is perhaps the primary method of making casual inferences in qualitative and case study research. Process tracing entails the exploration of causal relationships with reference to the mechanisms and causal chains that link independent to dependent variables in specific cases. We will explore assumptions about the nature of causal processes, examine the sources of inferential leverage in small-N research, and clarify how these principles apply in practice. Each day will include an applied component, where students will have opportunity to develop process tracing techniques in the framework of their own research projects.

Tutor: Tom O' Grady (UCL)

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 course will teach students to collect and analyse texts using statistical and computational methods. By the end of the course they will be able to conduct original research with text as data. The course begins by covering the major approaches to processing and describing documents prior to analysis.

It then covers statistical methods that describe, classify and scale documents in order to infer characteristics or traits of their authors, such as partisanship, sentiment and political opinions. Methods covered include dictionary methods, machine-learning classifiers, scaling methods such as Wordscores and WordFish, and topic models. It finishes with an introduction to word embeddings and the use of large language models for text analysis.

The course will be largely practical and hands-on, with an emphasis on applying the methods that we learn in R. Examples will mostly be drawn from political science, but the course is relevant for students from any field of social science.