用计算方法重用旧质性数据，挖掘跨时空研究新发现
Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data
- 通过计算密集型分析重用过往质性研究数据
- 可整合多情境、长时段数据解决跨时跨域问题
- 适合想突破传统质性研究局限的学者
本文关注计算方法在重新利用过往质性研究生成数据方面的潜力。尽管质性研究通常通过严谨且资源密集的过程产生丰富数据，但这些数据大多未被再利用。本文首先从收益、区别和认识论角度论证质性数据二次分析的合理性，进而提出计算密集型二次分析的可行性，强调利用跨越多个情境和时间跨度的数据集合，以应对跨情境与纵向研究问题。我们提出一种实施该分析的方法框架，并探讨其如何推动创新研究设计的发展。最后，列举了质性数据共享与再利用面临的关键挑战与持续关切。
原文摘要 · Abstract (English)
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.
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