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    Assessing Type Agreeability in the Unified Model of Personality and Play Styles

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    Authors
    Brooke, Alexander
    Crossley, Matthew
    Lloyd, Huw
    Cunningham, Stuart
    Affiliation
    Manchester Metropolitan University; University of Chester
    Publication Date
    2024-08-08
    
    Metadata
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    Abstract
    Classifying players into well defined groups can be useful when designing games and gamified systems, with many models relating to player or personality ‘type’. The Unified Model of Personality and Play Styles groups together many player and personality taxonomies, but whilst similarities have been noted in previous work, the overlap between models has not been analysed ahead of its use. This study provides evidence both for and against aspects of the Unified Model, with model agreeability assessed through comparison of participant classifications. Results show that representations of types related by the Unified Model do correlate significantly greater than types unrelated by the model, but do so with only weak-to-moderate correlation coefficients. Ranking classifications leads to results better mapping to the Unified Model, but also reduces the overall strength of correla tions between types. The Unified Model is therefore considered fit for purpose as an explanatory tool, but without additional study should be used with caution in further use cases.
    Citation
    Brooke, A., Crossley, M., Lloyd, H., & Cunningham, S. (2024, 5-8 Aug). Assessing Type Agreeability in the Unified Model of Personality and Play Styles. 2024 IEEE Conference on Games (CoG), Milan, Italy. https://doi.org/10.1109/CoG60054.2024.10645648
    Publisher
    IEEE
    URI
    http://hdl.handle.net/10034/628992
    DOI
    10.1109/cog60054.2024.10645648
    Additional Links
    http://dx.doi.org/10.1109/cog60054.2024.10645648
    Type
    Conference Contribution
    Description
    © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    ISSN
    2325-4270
    EISSN
    2325-4289
    ISBN
    9798350350685
    Sponsors
    N/A
    ae974a485f413a2113503eed53cd6c53
    10.1109/cog60054.2024.10645648
    Scopus Count
    Collections
    Computer Science

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