Final Thesis: Recommendation System for Qualitative Data Analysis

Abstract: QDAcity is a cloud-based application for performing qualitative data analysis. The analysis takes place within the coding editor enabling users to assign codes to various text segments across multiple documents. These text segments are called codings. Besides its task-solving capabilities, collaboration plays a central role in QDAcity. Teams can be organized through different project types, with role-based permissions. It does offer technical support for data synchronization via its real-time service and a service for concurrent text editing is currently in development. These services, however, primarily target the technical aspects of collaborating on shared data. Currently, there is little support for communicating change proposals regarding the content of the data. The goal of this thesis is to design and implement a recommendation system for QDAcity. As an integral part of QDAcity, this system enables users to create and review code system recommendations. The system focuses on codes while placing an emphasis on extensibility to include other entities such as codings or documents in the future. As a result, users are now able to communicate potential changes within QDAcity without leaving the platform. This improves the collaborative working capabilities of QDAcity and, consequently, improves the overall user experience.

Keywords: recommendation system, web, collaboration, coding, research, QDAcity

PDF: Master Thesis

Reference: Dominik Schöpf. Recommendation System for Qualitative Data Analysis. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2023.

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