Richard Traunmüller is a visiting professor of quantitative methods in the social and behavioural sciences the University of Mannheim and an assistant professor of empirical democracy research at Goethe University Frankfurt. He has previously held positions at the University of Konstanz, the University of Berne, and the University of Essex. Traunmüller has taught semester long courses on data visualisation at these universities and has been invited to teach statistical visualisation at the European University Institute (EUI) in Florence, the German Institute of Global and Area Studies (GIGA) as well as the Essex Summer School in Social Science Data Analysis. His work has been published in British Journal of Political Science, Comparative Political Studies, European Journal of Political Research, Political Analysis, and Sociological Methods & Research, amongst others.
Data visualisation is one of the most powerful tools to explore, understand and communicate patterns in quantitative information. At the same time, good data visualisation is a surprisingly difficult task and demands three quite different skills: substantive knowledge, statistical skill, and artistic sense. The course is intended to introduce participants to a) key principles of graphical perception and analytic design, b) useful visualisation techniques for the exploration and presentation of univariate, multivariate, time series and geographic data and c) new developments of data visualisation for the social sciences, such as interactive data visualization, visual inference, and visualising statistical models.
This course is highly applied in nature and emphasizes the practical aspects of data visualisation in the social sciences. Students will learn how to evaluate data visualizations based on principles of analytic design, how to construct compelling visualisations using the free statistics software R, and how to explore and present their data and models with visual methods. In short, students will get hands-on experience producing modern visualisations for their practical problems. This will be especially helpful to those students with own datasets related to their research.
Course prerequisites are a basic understanding of statistics and bivariate linear regression. Some experience in the use of a statistical software package would help but no prior exposure to R is required. I will provide detailed code examples and spend much time in the lab to get students up to pace.
Few, Stephen (2009). Now you see it: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
Tufte, Edward (2001). The Visual Display of Quantitative Information. (Second Edition). Graphics Press.
Murrell, Paul (2011). R Graphics. (Second Edition). Chapman & Hall/CRC.
Unwin, Anthony (2015). Graphical Data Analysis with R. CRC Press.
Background knowledge required
OLS = e
Maximum Likelihood = e
e = elementary, m = moderate, s = strong