Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data

TL;DR

This paper advocates for the use of computational methods in secondary analysis of qualitative data to unlock its potential for cross-contextual and longitudinal research, proposing a new scheme and discussing challenges.

Contribution

It introduces a novel scheme for computationally intensive secondary analysis of qualitative data and explores its potential to enhance research design and data reuse.

Findings

Highlights benefits of secondary analysis of qualitative data

Proposes a new computational analysis scheme

Discusses challenges in data sharing and reuse

Abstract

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.