论文标题
一个实验，探讨了开发一种半自动化方法来分析小N定性数据的理论和方法论挑战
An experiment exploring the theoretical and methodological challenges in developing a semi-automated approach to analysis of small-N qualitative data
论文作者
论文摘要
本文通过设计半自动化的定性数据分析（QDA）算法进行了实验，以使用免费软件分析20个成绩单。文本挖掘（TM）和QDA以频率和关联度量为指导，因为当样本量较小时，这些统计数据仍然很强。精致的TM算法根据手动修订的词典将文本分为各种尺寸。这种诱饵方法可以更好地反映文本的上下文，而不是将文本统一地将文本统计到一个单一尺寸。 TM结果用于初始编码。代码重新包装是通过关联措施和外部数据来指导的，以实现一般的归纳QDA方法。 TM和QDA检索的信息在子图中描述了以进行比较。分析在6-7天内完成。两种算法都在上下文一致和相关信息中检索。但是，与单独使用TM相比，QDA算法检索了更多的特定信息。 QDA算法并不严格遵守TM或QDA的惯例，而是比常规QDA方法更有效，系统性和透明的文本分析方法。扩展QDA以可靠地从文本中发现知识正是研究目的。本文还阐明了了解信息技术，理论和方法论之间的关系。
This paper experiments with designing a semi-automated qualitative data analysis (QDA) algorithm to analyse 20 transcripts by using freeware. Text-mining (TM) and QDA were guided by frequency and association measures, because these statistics remain robust when the sample size is small. The refined TM algorithm split the text into various sizes based on a manually revised dictionary. This lemmatisation approach may reflect the context of the text better than uniformly tokenising the text into one single size. TM results were used for initial coding. Code repacking was guided by association measures and external data to implement a general inductive QDA approach. The information retrieved by TM and QDA was depicted in subgraphs for comparisons. The analyses were completed in 6-7 days. Both algorithms retrieved contextually consistent and relevant information. However, the QDA algorithm retrieved more specific information than TM alone. The QDA algorithm does not strictly comply with the convention of TM or of QDA, but becomes a more efficient, systematic and transparent text analysis approach than a conventional QDA approach. Scaling up QDA to reliably discover knowledge from text was exactly the research purpose. This paper also sheds light on understanding the relations between information technologies, theory and methodologies.
