A Comparison of Card-sorting Analysis Methods

Ather Nawaz

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    Abstract

    This study investigates how the choice of analysis method for card sorting studies affects the suggested information structure for websites. In the card sorting technique, a variety of methods are used to analyse the resulting data. The analysis of card sorting data helps user experience (UX) designers to discover the patterns in how users make classifications and thus to develop an optimal, user-centred website structure. During analysis, the recurrence of patterns of classification between users influences the resulting website structure. However, the algorithm used in the analysis influences the recurrent patterns found and thus has consequences for the resulting website design. This paper draws an attention to the choice of card sorting analysis and techniques and shows how it impacts the results. The research focuses on how the same data for card sorting can lead to different website structures by generating different set of classifications. It further explains how the agreement level between the users can change for similar data due to the choice of analysis.
    Original languageEnglish
    Title of host publicationAPCHI '12. Proceedings of the 10th Asia Pacific Conference on Computer-Human Interaction
    Volume2
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery
    Publication date2012
    Pages583-592
    ISBN (Print)9784990656201
    Publication statusPublished - 2012
    EventThe 10th Asia Pacific Conference on Computer Human Interaction. 2012 - Matsue, Japan
    Duration: 28 Aug 201231 Aug 2012
    Conference number: 10
    http://apchi2012.org/

    Conference

    ConferenceThe 10th Asia Pacific Conference on Computer Human Interaction. 2012
    Number10
    CountryJapan
    CityMatsue
    Period28/08/201231/08/2012
    Internet address

    Cite this

    Nawaz, A. (2012). A Comparison of Card-sorting Analysis Methods. In APCHI '12. Proceedings of the 10th Asia Pacific Conference on Computer-Human Interaction (Vol. 2, pp. 583-592). Association for Computing Machinery.