Final Thesis: PDF Support for Qualitative Research in the Cloud

Abstract: Effective Qualitative Data Analysis (QDA) using software tools relies on the range of supported document types to work with. The Portable Document Format (PDF) standard is widely known and used because of its versatility. Therefore, the support of PDF documents in QDA software is essential.

The cloud-based QDA tool `QDAcity’ only supports Rich Text Format (RTF) documents. In this thesis, we design and implement PDF support for QDAcity. Since the current state of QDAcity does not allow to easily extend the range
of supported document types, our implementation is required to allow this in the future. The main challenge however is to design a coding mechanism, that can handle different document types. Using the coding mechanism, researchers
annotate segments of the qualitative data to extract a theory. To implement such a coding mechanism, we analyzed the implementation of different QDA tools and evaluated various implementation strategies for the cloud.

Different types of documents require different types of codings, such as area codings that can be used for image data. Our implemented coding mechanism therefore can be extended by future coding types and already includes support
for area codings.

Keywords: PDF, pdf.js, Cloud, Qualitative Research, QDAcity

PDF: Master Thesis

Reference: Julian Lehrhuber. PDF Support for Qualitative Research in the Cloud. Master Thesis. Friedrich-Alexander-Universität Erlangen-Nürnberg: 2021.