This course introduces academics and professional data analysts to Bayesian inference, using the Stan interface in R. The atmosphere of the workshop will be friendly and supportive, with the goal of teaching the basics of Bayesian inference in Stan for academics and professionals alike from diverse backgrounds ranging from industry to research fields such as population health, social sciences, disaster risk reduction, and many more. We will show participants how one can develop and compile Stan scripts for Bayesian inference through RStudio to perform basic parameter estimation, as well as a wide range of regression-based techniques from the simplest univariate linear models to more advanced multivariate spatial risk models. Participants will leave the course with a clear understanding of the Bayesian approach to data analysis and inference, and its applications in a range of fields.

This course provides a practical introduction to computational text analysis in the social sciences. Participants will gain hands-on experience with quantitative text analysis, machine learning, and the use of large language models, working with applied examples in R and learning how to conduct scalable text analysis through programmatic use of language model APIs.

This three-day course provides a comprehensive introduction to survey methods and analysis. It will focus on familiarising participants with the fundamentals of survey design and analysis, combined with practical applications using the statistical programme R. This course is designed for anyone interested in learning how to design, conduct, and analyse surveys effectively, regardless of their prior experience with survey research or statistical software.

Specifically, we will cover: sampling and weighting and how these relate to questions of representativeness; survey question and response scale design and to what extent a given question and its associated scale may (or may not) be measuring what was intended; different types of response bias, such as social desirability or non-response; effective ways to describe survey data and simple multivariate analysis; and a quick tour of the increasingly popular field of survey experiments.

On each day, the sessions will conclude with some time to implement the concepts discussed in the lecture. Specifically, participants will do a series of practical exercises, whose solutions will be discussed step-by-step together as a class. Participants will be encouraged to work together and feel free to ask any questions to allow them to learn in a collaborative environment.