EVIDS (Experimentelle Verhaltensforschung in Data Science)

Contents

In the course of digitalisation, data is becoming increasingly available that makes it possible to observe, analyse and understand human behaviour in various areas. Examples include transactions on websites, movement data recorded by smartphones or data from smart home devices. Accordingly, experimental behavioural research is becoming increasingly important in business practice and economics. In this module, students are taught basic knowledge of experimental research and practical application possibilities, which can be used to be able to answer behaviour-related questions in scientific and entrepreneurial practice in a well-founded manner.

The lecture focuses in particular on the planning, organisation and implementation of behavioural experiments. The students learn theoretical scientific basics and understand the relevance of experimental research in companies and the economic sciences. In addition, students are taught how to develop and conduct their own experiments. The focus is on the generation of testable hypotheses, the selection of a suitable experimental design, the ethical conduct of experiments and the clear and comprehensible reporting of the results.

The contents of the lecture are deepened in an accompanying exercise with training tasks and practice-oriented questions. The students will analyse and evaluate experimental designs in the literature as well as develop and present their own designs.

 

Learning objectives and skills

In this course, students will
  • acquire a basic understanding of the importance of experimental research in the context of gaining scientific knowledge
  • learn to discuss how experimental methodology differs from other scientific research methods and what contribution experiments can make to business informatics research projects
  • learn to explain the basic principles and designs of experiments
Recommended prerequisites Successful participation in the lectures „Data Science: Datenauswertung“ and „Data Science: Statistik“
Time and room Lecture: Thursdays, 9:45 – 11:15, room LG 0.423; exercise session: Tuesdays, 13:00 – 15:00, LG 5.152
Method of examination Written examination (90 minutes)
Grading procedure Written examination (100 %)
Module frequency Each summer term
Workload Lecture and exercise sessions: 50h
Self-study: 100h
Module duration 1 Semester
Teaching and examination language German
(Recommended) reading Will be announced in class