Why the Data Revolution Needs Qualitative Methods
Why the Data Revolution Needs Qualitative Methods
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为什么数据革命需要定性方法
DOI：
10.1162/99608f92.eee0b0da
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Dreier, Sarah K.
中科院分区：
文献类型：
--
作者：
Tanweer, Anissa;Gade, Emily Kalah;Krafft, P.M.;Dreier, Sarah K.
本文借鉴了质的社会科学，为社会现象的数据科学提出了一个关键的智力基础。定性敏感性--尤其是解释主义、溯因推理和反身性--可以解决数据科学中出现的方法论问题，并有助于扩展社会知识的边界。首先，解释主义的镜头--它关注的是在给定的语境中构建意义--可以实现从数字痕迹数据中理解高级行为模式所必需的更深层次的洞察。没有这样的背景洞察力，研究人员经常曲解他们在大规模分析中发现的东西。其次，溯因推理在数据科学中很常见，但它的应用往往并不系统化。溯因推理是一种利用观察结果生成新解释的过程，其基础是对世界的先前假设。纳入执行、描述和评估绑架适用的质量传统的规范和做法将有助于提高透明度和问责制。最后，数据科学家将受益于增加的反身性--这是评估研究人员自己的假设、经验和关系如何影响他们的研究的过程。研究表明，研究人员的经验中通常没有在量化传统中提及的这些方面可以影响研究结果。定性研究人员长期以来一直面临着同样的担忧，他们在如何解构和记录个人和智力起点方面的培训可能会被证明对数据科学家有指导意义。我们相信，这些和其他定性的敏感性具有巨大的潜力，可以促进更有意义、更可靠和更道德的数据科学研究的产生。
This essay draws on qualitative social science to propose a critical intellectual infrastructure for data science of social phenomena. Qualitative sensibilities—interpretivism, abductive reasoning, and reflexivity in particular—could address methodological problems that have emerged in data science and help extend the frontiers of social knowledge. First, an interpretivist lens—which is concerned with the construction of meaning in a given context—can enable the deeper insights that are requisite to understanding high-level behavioral patterns from digital trace data. Without such contextual insights, researchers often misinterpret what they find in large-scale analysis. Second, abductive reasoning—which is the process of using observations to generate a new explanation, grounded in prior assumptions about the world—is common in data science, but its application often is not systematized. Incorporating norms and practices from qualitative traditions for executing, describing, and evaluating the application of abduction would allow for greater transparency and accountability. Finally, data scientists would benefit from increased reflexivity—which is the process of evaluating how researchers’ own assumptions, experiences, and relationships influence their research. Studies demonstrate such aspects of a researcher’s experience that typically are unmentioned in quantitative traditions can influence research findings. Qualitative researchers have long faced these same concerns, and their training in how to deconstruct and document personal and intellectual starting points could prove instructive for data scientists. We believe these and other qualitative sensibilities have tremendous potential to facilitate the production of data science research that is more meaningful, reliable, and ethical.