Semiautomated text analytics for qualitative data synthesis
Semiautomated text analytics for qualitative data synthesis
复制标题
DOI：
10.1002/jrsm.1361
复制
发表时间：
2019-07-09
影响因子：
9.8
通讯作者：
Guell, Cornelia
中科院分区：
文献类型：
--
作者：
Haynes, Emily;Garside, Ruth;Guell, Cornelia
迄今为止，综合定性数据的方法主要集中于综合已发表报告的结果。然而，文本挖掘软件的发展提供了对大型汇集的主要定性数据集进行有效分析的潜力。本案例研究旨在(a)提供使用一个软件应用程序Leximancer的逐步指南，以及(b)询问定性数据合成软件的机会和局限性。我们将Leximancer v4.5应用于五个定性的、基于英国的交通研究，如步行、骑自行车和开车，并将自动内容分析的结果显示为主题间距离图。lexximancer使我们能够“缩小”，以熟悉我们自己，并获得一个广泛的视角，汇集的数据。它指出了哪些研究集中在诸如“人”之类的主要主题上。该软件还使我们能够“放大”，将视角缩小到特定的子群体和查询线。例如，“人”在男性和女性的叙述中都有出现，但谈论的方式不同，男性会提到“孩子”和“老人”，而女性会提到“东西”和“东西”。该方法为分析过程中的初始归纳步骤提供了一个新的视角，并可以指导进一步的探索。使用lexximancer的限制是涉及大量的数据准备时间，以及研究人员将调查线转化为有意义的见解所需的上下文知识。总之，lexximancer是一个有用的工具，有助于定性数据合成，促进全面和透明的数据编码，但只能告知，不能取代，研究人员主导的解释工作。
Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. This case study aimed to (a) provide a step-by-step guide to using one software application, Leximancer, and (b) interrogate opportunities and limitations of the software for qualitative data synthesis. We applied Leximancer v4.5 to a pool of five qualitative, UK-based studies on transportation such as walking, cycling, and driving, and displayed the findings of the automated content analysis as intertopic distance maps. Leximancer enabled us to "zoom out" to familiarize ourselves with, and gain a broad perspective of, the pooled data. It indicated which studies clustered around dominant topics such as "people." The software also enabled us to "zoom in" to narrow the perspective to specific subgroups and lines of enquiry. For example, "people" featured in men's and women's narratives but were talked about differently, with men mentioning "kids" and "old," whereas women mentioned "things" and "stuff." The approach provided us with a fresh lens for the initial inductive step in the analysis process and could guide further exploration. The limitations of using Leximancer were the substantial data preparation time involved and the contextual knowledge required from the researcher to turn lines of inquiry into meaningful insights. In summary, Leximancer is a useful tool for contributing to qualitative data synthesis, facilitating comprehensive and transparent data coding but can only inform, not replace, researcher-led interpretive work.