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Panel data - i.e. the observation of multiple variables over multiple time intervals for the same units of analysis - often suffer from various violations of the Gauss-Markov assumptions. For example, in many settings it can hardly be assumed that repeated measurements within units are independent of each other. In contrast to seminal pooled time-series models, multilevel approaches can directly model such dependencies by allowing for varying intercepts and slopes. This enables us to disentangle temporal from cross-sectional variation and to study their cross-level interactions.

R is a powerful, free and open source programming language for data manipulation, analysis and graphing. The course provides a basic understanding of the guiding principles of data analysis with R. Participants will learn the basics of R’s syntax and grammar. By the end of the course, they will be able to perform their own data analysis including data exploration, visualization and simulation.

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