A Methodology for Questionnaire Analysis: Insights through Cluster Analysis of an Investor Competition Data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910325817737216 |
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| author | Forster, Carlos Henrique Q. de Castro, Paulo André Lima Ramalho, Andrei |
| author_facet | Forster, Carlos Henrique Q. de Castro, Paulo André Lima Ramalho, Andrei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Methodology for Questionnaire Analysis: Insights through Cluster Analysis of an Investor Competition Data Forster, Carlos Henrique Q. de Castro, Paulo André Lima Ramalho, Andrei Human-Computer Interaction Artificial Intelligence 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. |
| title | A Methodology for Questionnaire Analysis: Insights through Cluster Analysis of an Investor Competition Data |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2402.06759 |