Registration ends 07.4.2026 at 23:59 h

This seminar introduces students to the practical steps of conducting a systematic literature review as a transparent and reproducible research method. Together we will work on formulating clear research questions and translating them into structured search strings. Students will learn how to identify and select relevant academic databases and apply systematic screening procedures to titles, abstracts, and full texts. We will practice extracting and organizing metadata from articles while ensuring consistency and traceability of the review process. The seminar emphasizes transparency and reproducibility at each stage, from documenting search decisions to reporting screening outcomes. Students will engage with methods for synthesizing findings and critically reflecting on inclusion and exclusion choices. An innovative concept of textual abundance analysis will also be introduced, offering students a fresh perspective on identifying thematic patterns in large bodies of text. The course highlights common pitfalls and biases in literature reviews and how to address them. Finally, students interested in advanced approaches will be introduced to programmatic tools for automating parts of the workflow. Overall, the seminar combines hands-on exercises with critical reflection to prepare students for applying systematic review methods in their own fields.

Preliminar seminar structure:

week 1 - Why systematic reviews matter?
week 2 - Types of reviews, Topic selection, research problem and review methodology
week 3 - Creating search strings and databases
week 4 - Metadata retrieval from databased
week 5 - Abstract screening
week 6 - Full-text retrieval and full-depth search
week 7 - Meta-analysis and network analysis
week 8 - Content analysis
week 9 - Textual abundance analysis
week 10 - Reporting findings and creating visualizations
week 11 - Writing the methodolgy
week 12 - Putting everything together
week 13 - Round of questions/self-work
week 14 - Final presentations

Bonus (optional) - Workflow automation with Python

Students will learn how to develop and document search strategies, practice systematic screening and data extraction with real examples, and organize the data accordingly. They will gain experience in visualizing and summarizing patterns in the literature while critically reflecting on biases, limitations, and good practices in review work.

Further information on teaching evaluation: https://www.leuphana.de/en/teaching/quality-management/feedback-instruments.html