Information Systems (IS) researchers undertaking qualitative research are increasingly proficient with programming techniques. They are also increasingly endeavouring towards openness and transparency, in light of the Open Science movement; and they are increasingly receptive towards alternative and emerging qualitative techniques. In light of these considerations, this paper introduces a “YAML Workflow for Qualitative Data Analysis”. In this workflow, qualitative data analysis is seen as a form of data modelling, thus leveraging techniques from the domain of data modelling such as boundary objects like class diagrams and tools such as integrated development environments. Further, this workflow entails the use of programming languages like Python, by which data can be manipulated, queried, and summarised (e.g., in table-like overviews). Importantly, this workflow is entirely driven by plain-text files that can be tracked with a version control system like Git. Overall, this workflow supports the innovative directions towards which qualitative IS research is evolving.
Wang, Blair, "Programming for Qualitative Data Analysis: Towards a YAML Workflow" (2022). ACIS 2022 Proceedings. 17.