Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/26430
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2018-02-06T13:42:47Z | - |
dc.date.available | 2018-02-06T13:42:47Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/26430 | - |
dc.description | B.FINE ARTS DIG.ARTS | en_GB |
dc.description.abstract | This 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.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Information visualisation | en_GB |
dc.subject | Self-perception | en_GB |
dc.subject | Big Five model | en_GB |
dc.title | The Infographic self : the visualisation of human personalities through gathered quantitative and qualitative data | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The 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.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Media and Knowledge Sciences. Department of Digital Arts | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Sciberras, Stephanie | - |
Appears in Collections: | Dissertations - FacMKS - 2017 Dissertations - FacMKSDA - 2017 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
17BFADA019.pdf Restricted Access | 207.51 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.