Research Paper: Supporting Interview Analysis with Autocoding
Abstract: Interview analysis is a technique employed in qualitative research. Researchers annotate (code) interview transcriptions, often with the help of Computer-Assisted Qualitative Data Analysis Software (CAQDAS). The tools available today largely replicate the manual process of annotation. In this article, we demonstrate how to use natural language processing (NLP) to increase the reproducibility and traceability of the process of applying codes to text data. We integrated an existing commercial machine–learning (ML) based concept extraction service into an NLP pipeline independent of domain specific rules. We applied our prototype in three qualitative studies to evaluate its capabilities of supporting researchers by providing recommendations consistent with their initial work. Unlike rule based approaches, our process can be applied to interviews from any domain, without additional burden to the researcher for creating a new ruleset. Our work using three example data sets shows that this approach shows promise for a real–life application, but further research is needed.
Keywords: QDA, NLP, interview analysis, CAQDAS, machine learning, autocoding
Reference: Andreas Kaufmann, Ann Barcomb and Dirk Riehle. 2020 (January). Supporting Interview Analysis with Autocoding. 53rd Hawaii International Conference on System Sciences (HICSS 2020). Honolulu, USA.
A preprint of the paper is available as a PDF.