GESIS Training Courses

Scientific Coordination

André Ernst

Administrative Coordination

Claudia ODonovan-Bellante

Tools and Workflows for Reproducible Research in the Quantitative Social Sciences

Online via Zoom
General Topics:
Course Level:
Software used:
Students: 160 €
Academics: 240 €
Commercial: 480 €
Additional links
Lecturer(s): Dr. Arnim Bleier, Dr. Johannes Breuer, Dr. Bernd Weiß

About the lecturer - Dr. Arnim Bleier

About the lecturer - Dr. Johannes Breuer

About the lecturer - Dr. Bernd Weiß

Course description

The focus of the course is on reproducible research in the quantitative social and behavioral sciences. Reproducibility here means that other researchers can fully understand and (re-)use your statistical analyses. The workflows and tools covered in this course will, ultimately, also facilitate your own work as they allow you to automate analysis and reporting tasks. The goal of this course is to introduce participants to tools and processes for reproducible research and enable them to make use of those for their own work.
In addition to a conceptual introduction to the processes and key terms around reproducible research, the focus in this course will be on procedures for making a data analysis with R fully reproducible. We will cover questions of organization (e.g., folder structures, naming schemes, documentation, version control), “clean” code (e.g., documentation and modularization), as well as choosing and working with the required tools (besides R: RStudio, Git & GitHub, LaTeX, RMarkdown, Jupyter Notebooks, and Binder).

Target group

The workshop is targeted at participants who have (at least some) experience with R and want to learn (more) about workflows and tools for making the results of their research reproducible.

Learning objectives

By the end of the course participants should:
-  have gained important insights into key concepts of reproducible research and recommended best practices
-  be able to work with state-of- the art frameworks and tools, such as RMarkdown, Jupyter, Git, and Binder
-  be able to publish reproducible computational analysis pipelines with R


Participants should have some basic knowledge of R. While this is not required, participants who have experience with doing statistical analysis in R will benefit most from this course.


Recommended readings

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