Empirical Accounting Research Project

Field(s) of Study
upon request
Letztes Update


Special Note

This course is not credited, i.e., it is not graded and does not count towards any degree.
However, we strongly encourage you to enroll if you are interested in empirical work for practice, your studies (e.g., seminar paper or thesis project) and/or (management) accounting research.

Course Description

In this course, students conduct empirical research projects replicating and extending published (management) accounting research or white papers with a focus on empirical data analysis with STATA. In general, students work on assigned research topics, selected in accordance with their prior experience, however, students are also welcomed to work on their own research ideas. While there is no formal submission, students are expected to present their code, main findings and key learnings in a get-together of all currently enrolled students and their supervisor.


We encourage students to conduct a (supervised) empirical research project, in this course, because of the increasing importance of data processing skills (data cleaning, consolidating and interpreting) for practice, academia (e.g., seminar papers) and research. Working intensely with data, students gain first and/or additional experiences in empirical work empowered through individual guidance and feedback. As there is no grading, students have the opportunity to learn and/or practice (empirical) project work free from academic and occupational pressure.

Students are expected to work independently. However, in informal meetings, students discuss their results, challenges and/or next steps with their supervisor and if suitable, peers. As the course is offered to practice empirical work, 1) there is no grading and 2) the completed work cannot be used or submitted for other modules, e.g., for seminar papers or Master theses.


Deadlines are determined on an individual basis in coordination with supervisors. However, a research project should not exceed four weeks. Especially if students have little experience in empirical work, we suggest planning with a higher workload – details can be discussed with a supervisor, i.e., lecturer, beforehand. 

If you are interested in enrolling, we kindly ask you to reach out via mac@wiwi.uni-muenster.de to determine a suitable topic and time schedule for your research project.

Contact Person(s)

Prof. Dr. Martin Artz (responsible)

Florian Droese (accompanying)