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dc.date.accessioned2018-02-06T13:42:47Z-
dc.date.available2018-02-06T13:42:47Z-
dc.date.issued2017-
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/26430-
dc.descriptionB.FINE ARTS DIG.ARTSen_GB
dc.description.abstractThis study explores the role of data visualisation as applied to the abstract psychological subject of human personality. It offers visual representation in a discipline best known for its theoretical texts. This dissertation enquires into the origins of the human self and how it has been discerned through personality theories and assessment methods. In conjunction, the discipline of data visualisation is also explored, where specific interest is given to practical guidelines and key theories that pioneered such a vast discipline. The research methodology subjects a family of four individuals to a personality inventory questionnaire and a daily behavioural log. Such methods were selected so as to gather both quantitative and qualitative data regarding their personalities. These data sets were then visually represented in the practical segment of this study. The project concludes with seven different data visuals for each individual tested. This study provides a clear and visual alternative to the understanding of personality without the need of any previous theoretical psychological background.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectInformation visualisationen_GB
dc.subjectSelf-perceptionen_GB
dc.subjectBig Five modelen_GB
dc.titleThe Infographic self : the visualisation of human personalities through gathered quantitative and qualitative dataen_GB
dc.typebachelorThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Media and Knowledge Sciences. Department of Digital Artsen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorSciberras, Stephanie-
Appears in Collections:Dissertations - FacMKS - 2017
Dissertations - FacMKSDA - 2017

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