  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.
