Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data
This work targets researchers in qualitative studies by offering a method to reuse existing data, though it is incremental as it builds on existing secondary analysis concepts.
The paper addresses the underutilization of rich qualitative data by advocating for computationally intensive secondary analysis, proposing a scheme to enable cross-contextual and longitudinal research.
This paper draws attention to the potential of computational methods in reworking data generated in past qualitative studies. While qualitative inquiries often produce rich data through rigorous and resource-intensive processes, much of this data usually remains unused. In this paper, we first make a general case for secondary analysis of qualitative data by discussing its benefits, distinctions, and epistemological aspects. We then argue for opportunities with computationally intensive secondary analysis, highlighting the possibility of drawing on data assemblages spanning multiple contexts and timeframes to address cross-contextual and longitudinal research phenomena and questions. We propose a scheme to perform computationally intensive secondary analysis and advance ideas on how this approach can help facilitate the development of innovative research designs. Finally, we enumerate some key challenges and ongoing concerns associated with qualitative data sharing and reuse.