The primary goal of the workshop is to teach transparent and reproducible workflows for research data management and statistical analysis using the free R programming language for statistical computing and graphics and the RStudio environment. The basic idea is that transparent data management anticipates the data representation needed for statistical analyses and modeling. A transparent representation of data greatly facilitates the specification of statistical models that are appropriate for the data; in other words, it effectively prevents the specification of incorrect statistical models. The secondary goal of the workshop is to introduce some multivariate statistical analyses. However, the extent and amount of time spent on the secondary goal depends on how fast the primary goal is reached, that is it depends on participants’ background and success in achieving the primary goal.
Implement a complete workflow for own empirical social science project
- Participants know and implement the steps of a data science project: import, clean, transform, visualize, and model data as well as communicate results
- Participants are able to formulate goals and research questions about observational and (quasi-)experimental studies
- Participants know principles of good scientific practice and learn to document their research in a reproducible format