Spring School 2023 consists of five course options. Please note, you can only attend one of the five core course options, as they will run alongside each other from 2pm-5pm BST, Monday 27 to Friday 31 March 2023.
We regret to inform you that the Formal Theory and Models course will no longer go ahead and we apologise for any inconvenience caused. If you are interested in being notified of future Formal Theory and Models courses, please sign up to our mailing list.
Participants who choose to attend one of these courses will also be invited to attend additional research methodology classes along with the full Spring School cohort in the mornings.
Please note all courses will be taught both in-person and virtually and all times are BST.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
Students accepted to the Oxford Spring School’s core courses, will be also attending additional courses in methodology that will run in the morning.
Our expert instructors will be offering pre-fresher courses on mathematics for social scientists, a crash course in R/RStudio (for those with no experience using the software), introductory courses in Python (from downloading the software through to running basic analyses), seminars on the empirical implications of theoretical models, and students of Qualitative methods will be given personalised feedback on their research projects.