Final Thesis: Erweiterung der Datentypen in der Cloudanwendung QDAcity

Abstract: Data triangulation is a well-established method in qualitative research projects fostering the synthesis of theories by comparing different perspectives from a variety of data sources. This widely used approach poses a
challenge for Qualitative Data Analysis tools like QDAcity because different data types need to be supported. Usually, each of these data types requires its own UI and data model while access to the same code book and collaboration within the project team must be sustained. This master thesis addresses the challenge by integrating spreadsheet support while maintaining all collaborative workflows using Conflict-free Replicated Data Types (CRDTs). The new XLSX- and DOCX-file import enables a seamless transition from office tools to QDAcity generating a user-friendly
experience. With these adjustments, QDAcity is becoming better aligned with the standards users are familiar with from popular text editing and spreadsheet software tools and the support of projects with a diverse set of data types is extended.

Keywords: Cloud, React, QDA, QDAcity

PDF: Master Thesis

Reference: Lara Sulzbach. Erweiterung der Datentypen in der Cloudanwendung QDAcity. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2026.