Beyond the Interpretive: Finding Meaning in Qualitative Data
Beyond the Interpretive: Finding Meaning in Qualitative Data
复制标题
超越解释：在定性数据中寻找意义
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
E. Douglas
中科院分区：
文献类型：
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作者：
E. Douglas
本文比较了两种不同的定性分析技术：解释性主题分析和解构性分析。最近，定性方法学家呼吁从编码转向他们所谓的“后定性”分析和“用理论思考”。他们认为，由于解释性编码是在不考虑理论的情况下进行的，并将数据分解为小块，因此它本质上是还原的，导致了一组肤浅和不言自明的主题。相比之下，后定性分析从理论解释数据开始，导致深入的解释。本文中的分析使用了一个更大的工程多样性和包容性研究的单一访谈。这两种分析最显著的区别是语气。主题分析的结果有点遥远的语气，对待与会者的意见作为公认的知识。解构主义分析在解释上更丰富。通过后质性分析所获得的更深层次的意义将有益于工程教育研究。
This theory paper compares two different qualitative analysis techniques for the same data: interpretive thematic analysis and deconstructive analysis. Recently qualitative methodologists have called for a move away from coding to what they call “postqualitative” analysis and “thinking with theory”. They argue that because interpretive coding is conducted without regard to theory and breaks the data into small pieces, it is inherently reductive, leading to a superficial and self-evident set of themes. In contrast, post-qualitative analysis begins by interpreting data in light of theory, resulting in deep interpretation. The analysis in this paper uses a single interview from a larger study on diversity and inclusion in engineering. The most striking difference in the two analyses is the tone. Thematic analysis results in a somewhat distant tone, treating the participant’s views as received knowledge. The deconstructive analysis is richer in interpretation. Engineering education research would benefit from the deeper meanings obtained through post-qualitative analysis.