A Methodology for Questionnaire Analysis: Insights through Cluster Analysis of an Investor Competition Data
This is an incremental method for analyzing questionnaire data in specific domains like finance.
The authors tackled the problem of extracting insights from questionnaire data by proposing a methodology that uses clustering analysis to group questions and participants, revealing patterns and relationships, such as connections to financial data in an investor competition context.
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.