Information Architecture: Using Best Merge Method, Category Validity, and Multidimensional Scaling for Open Card Sort Data Analysis.
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| Title: | Information Architecture: Using Best Merge Method, Category Validity, and Multidimensional Scaling for Open Card Sort Data Analysis. |
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| Authors: | Paea, Sione (AUTHOR), Katsanos, Christos (AUTHOR), Bulivou, Gabiriele (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Jan2024, Vol. 40 Issue 2, p203-223. 21p. |
| Subjects: | Multidimensional scaling, Information architecture, Multidimensional databases, K-means clustering, Data analysis, Hierarchical clustering (Cluster analysis), Quantitative research |
| Abstract: | Open card sorting is a widely used method in HCI for the design of user-centered Information Architectures (IAs). This article proposes a new algorithm that combines the best merge method (BMM), category validity technique (CVT), and multidimensional scaling (MDS) to explore, analyze and visualize open card sort data. A study involving 20 participants and 41 cards explored the IA redesign of a university's website. The collected data were analyzed using two popular methods employed in the quantitative analysis of open card sort data (i.e., hierarchical clustering, K-means) and the proposed algorithm. It was found that the latter provides increased IA insights compared to the existing methods. Specifically, the proposed algorithm can expose hidden patterns and relationships amongst cards and identify complexities. We also found that the proposed algorithm produces better initial clusters, which have a direct effect on the final clustering quality. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Open card sorting is a widely used method in HCI for the design of user-centered Information Architectures (IAs). This article proposes a new algorithm that combines the best merge method (BMM), category validity technique (CVT), and multidimensional scaling (MDS) to explore, analyze and visualize open card sort data. A study involving 20 participants and 41 cards explored the IA redesign of a university's website. The collected data were analyzed using two popular methods employed in the quantitative analysis of open card sort data (i.e., hierarchical clustering, K-means) and the proposed algorithm. It was found that the latter provides increased IA insights compared to the existing methods. Specifically, the proposed algorithm can expose hidden patterns and relationships amongst cards and identify complexities. We also found that the proposed algorithm produces better initial clusters, which have a direct effect on the final clustering quality. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2022.2112077 |
