University of Lincoln
Browse

3D visualisation of psychometric estimates for the ideal male body

Version 2 2024-03-12, 19:28
Version 1 2024-03-01, 12:00
journal contribution
posted on 2024-03-01, 12:00 authored by Piers. L Cornelissen, Thomas V. Pollet, Robin KramerRobin Kramer, Sophie Mohamed, Tracey Thornborrow, Martin Tovee

Psychological concerns are frequently indexed by psychometric questionnaires but the mental representations that they seek to quantify are difficult to visualise. We used a set of questionnaires designed to measure men’s concept of their bodies including: the Drive for Muscularity Scale (DMS; McCreary & Sasse, 2000), the Perceived Sociocultural Pressures Scale (PSPS; Stice, Nemeroff, & Shaw, 1996a), the Body Appreciation Scale (BAS-2; Tylka & Wood-Barcalow, 2015), and the Sociocultural Attitudes Towards Appearance Questionnaire-3 (SATAQ-3; Thompson, van den Berg, Roehrig, Guarda, & Heinberg, 2004). We combined their use with an interactive 3D modelling programme to allow men to create computer-generated representations of their ideal bodies. We used a principal component analysis to extract those shape components of our participants’ CGI ideal bodies that were predicted by the questionnaires and reconstructed the body shapes that these questionnaires were capturing. Moving from the lowest to the highest score on both the DMS and SATAQ corresponded with changes in muscularity, particularly muscle mass and definition. This approach allows us to demonstrate the actual body features that are being captured by a particular questionnaire.

History

School affiliated with

  • School of Psychology (Research Outputs)

Publication Title

Body Image

Volume

38

Pages/Article Number

295-305

Publisher

Elsevier

ISSN

1740-1445

eISSN

1740-1445

Date Submitted

2022-07-11

Date Accepted

2021-05-03

Date Document First Uploaded

2022-07-01

ePrints ID

45037

Usage metrics

    University of Lincoln (Research Outputs)

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC