分类
定性研究
自反性
严厉
定性性质
认识论
奖学金
过程(计算)
定性分析
数据科学
计算机科学
管理科学
社会学
社会科学
人工智能
政治学
工程类
机器学习
哲学
法学
操作系统
作者
Stine Grodal,Michel Anteby,Audrey Holm
标识
DOI:10.5465/amr.2018.0482
摘要
Scholars have long debated how rigor can be achieved in qualitative analysis. To answer this question, we need to better understand how theory is generated from data. Qualitative analysis is, at its core, a categorization process. Nevertheless, despite a surge of interest in categorization within the social sciences, insights from categorization theory have not yet been applied to our understanding of qualitative analysis. Drawing from categorization theory, we argue that the movement from data to theory is an active process in which researchers choose between multiple moves that help them to make sense of their data. In addition, we develop a framework of the main moves that people use when they categorize data and demonstrate that evidence of these moves can also be found in past qualitative scholarship. Our framework emphasizes that, if we are not sufficiently reflexive and explicit about the active analytical processes that generate theoretical insights, we cannot be transparent and, thus, rigorous about how we analyze data. We discuss the implications of our framework for increasing rigor in qualitative analysis, for actively constructing categories from data, and for spurring more methodological plurality within qualitative theory building.
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