In this paper, we propose a methodology for the analysis of questionnaire
data along with its application on discovering insights from investor data
motivated by a day trading competition. The questionnaire includes categorical
questions, which are reduced to binary questions, 'yes' or 'no'. The
methodology reduces dimensionality by grouping questions and participants with
similar responses using clustering analysis. Rule discovery was performed by
using a conversion rate metric. Innovative visual representations were proposed
to validate the cluster analysis and the relation discovery between questions.
When crossing with financial data, additional insights were revealed related to
the recognized clusters.

本文提出了一种基于问卷数据的分析方法，并在投资者数据中应用该方法以发现洞察。该方法通过聚类分析将类别问题归约为二元问题，并使用转化率指标进行规则发现。为验证聚类分析和问题之间的关系发现，提出了创新的可视化表达方式。与金融数据结合后，发现了与已识别的聚类相关的额外洞察。