This four-day course introduces Bayesian inference and modelling. It is suitable 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. The focus will be on teaching the Stan interface, which works with statistical software packages to perform state-of-the-art statistical modelling within a Bayesian framework. The focus of this workshop will be on using Stan with R, although Stan can also be used with Python, Julia, Stata, and MATLAB. We will show you to 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. This will begin with the simplest univariable linear models and its different families, moving up to Bayesian generalised linear models, hierarchical models and spatial intrinsic conditional autoregression models.

By the end of the workshop, the participants will

• Understand the key principles of statistical modelling within a Bayesian framework;

• Be able to calculate inferential statistics on spatial and non-spatial data for hypothesis testing using the diverse types of regression-based models in a Bayesian framework
• Be able to perform spatial risk prediction for areal data as well as quantify levels of uncertainty using exceedance probabilities
• Acquire new programming language skills in Stan (interfaced with RStudio)

Each day will include a lecture, a live walkthrough demonstration of applying the methods, and a computer seminar class where participants will get hands-on experience of Bayesian estimation. For those that have not used R before, introductory materials to learn the basics of R programming will be provided before the course, making it suitable for users of all software packages.  

Texts such as speeches, social media posts and newspaper articles convey a wide range of important information for researchers whether in business, academia or the public sector. They are now available in digitised form on an unprecedented scale, allowing users to turn texts into data and study them systematically. This course will teach participants to collect and analyse texts using statistical and computational methods. By the end of the course, students will be confident users of text analysis methods, able to embark on original research and analysis with large collections of text.

The course begins by covering the major approaches to processing documents prior to analysis and describing their linguistic features. It then covers statistical methods that describe and classify documents in order to infer characteristics or traits of their authors, including dictionary methods and supervised classification. It finishes with systematic practical advice on how to conduct research with texts, including how to use text analysis in research projects and how to collect texts using web-scraping and automated online data portals.

The course is largely practical and hands-on, with an emphasis on applying the methods that we learn. Each day will include lectures as well as a computer seminar class where participants will get direct experience with text analysis using the popular R software package. For those that have not used R before, introductory materials to learn the basics of R programming will be provided before the course, making it suitable for users of all software packages or those new to statistical programming. 

This three-day course will focus on familiarising participants with the fundamentals of survey design and analysis, combined with practical applications using statistical software. The course will be of interest to anyone tasked with analysing, understanding, presenting or designing public opinion surveys in a professional setting.

Specifically, the course will cover: sampling and weighting methods and how these relate to questions of representativeness; the design of survey questions and response-scale, and how these relate to the concepts that we intend to measure in surveys; different types of response bias, such as social desirability or non-response; effective ways to describe survey data and simple multivariate analysis; and an introduction to the increasingly popular field of survey experiments.

On each day, the sessions will include implementing the concepts discussed in class. Participants will complete a series of practical exercises, with solutions discussed step-by-step together as a class. Participants will be encouraged to work together and to learn in a collaborative environment.

At the end of the three days, participants will have reached a good level of survey literacy, with an ability to critique existing surveys and survey questions. They will be able to identify well-designed surveys and will be confident in interpreting survey results. The guiding theme of the course will be learning to think critically about what information can (and cannot) be learned from survey data.