Big data, qualitative style: a breadth-and-depth method for working with large amounts of secondary qualitative data.
Big data, qualitative style: a breadth-and-depth method for working with large amounts of secondary qualitative data.
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DOI：
10.1007/s11135-018-0757-y
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Weller, Susie
中科院分区：
文献类型：
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作者：
Davidson, Emma;Edwards, Rosalind;Weller, Susie
来自定性研究的数据集的档案存储为合并小规模数据集以供重复使用/二次分析提供了机会。在这篇文章中，我们概述了我们组合多个定性数据集的方法，并解释了为什么使用“大质量”数据语料库是一项值得努力的工作。我们提出了一种迭代结合递归表面专题制图和深度解释工作的新方法。我们的广度和深度方法包括一系列步骤：(1)调查存档数据集，以创建新的数据集合；(2)在对话中进行递归地表专题测绘；(3)初步“试验坑”分析、重新测绘和初步分析的重复；(4)对大多数定性研究人员熟悉的类型进行深入分析。在这样做的过程中，我们展示了定性研究人员如何在保持关于社会过程的独特知识顺序的同时进行“大质量”分析，这是严格定性研究的标志，其完整性关注于细微差别的背景和细节。
Archival storage of data sets from qualitative studies presents opportunities for combining small-scale data sets for reuse/secondary analysis. In this paper, we outline our approach to combining multiple qualitative data sets and explain why working with a corpus of 'big qual' data is a worthwhile endeavour. We present a new approach that iteratively combines recursive surface thematic mapping and in-depth interpretive work. Our breadth-and-depth method involves a series of steps: (1) surveying archived data sets to create a new assemblage of data; (2) recursive surface thematic mapping in dialogue with (3) preliminary 'test pit' analysis, remapping and repetition of preliminary analysis; and (4) in-depth analysis of the type that is familiar to most qualitative researchers. In so doing, we show how qualitative researchers can conduct 'big qual' analysis while retaining the distinctive order of knowledge about social processes that is the hallmark of rigorous qualitative research, with its integrity of attention to nuanced context and detail.