Open AccessRank-Order of Body Shapes Reveals Internal Hierarchy of Body Image
Author(s) -
Revital NaorZiv,
Rose King,
Joseph Glicksohn
Publication year - 2020
Publication title -
journal for person-oriented research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.23
H-Index - 3
eISSN - 2003-0177
pISSN - 2002-0244
DOI - 10.17505/jpor.2020.22044
Subject(s) - torso , artificial intelligence , image (mathematics) , hierarchy , computer science , computer vision , mathematics , pattern recognition (psychology) , anatomy , medicine , economics , market economy
How do individuals rank body shapes? Does this relate to the body part one is most dissatisfied with? Our study investigates whether one can generalize regarding how women represent the body. Three BMI-calibrated images from the Photographic Figure Rating Scale, representative of thin (BMI = 14.72), medium (BMI = 20.33), and large (BMI = 29.26) shapes, were divided into torso, legs and arms, and saved as individual images on a black background. Of 27 possible composite images, 8 were chosen based on a Torso (thin vs. large) × Leg (thin/large vs. medium) × Arm (thin vs. large) design. Our 44 female participants ordered these from thinnest to largest. This was first according to torso, then leg, and finally arm: 41 individuals agreed on the thinnest image (thin torso, thin legs, thin arms), followed by a second image (thin torso, thin legs, large arms, n = 26; or thin torso, medium legs, thin arms, n = 10). One participant differed markedly in her choice of the first image (large torso, medium legs, thin arms). Interestingly, she scored 10 on the EDI-2 scale of Bulimic Tendencies, revealing high risk for bulimia, suggesting that our task might be useful for studying eating disorders. Our juxtaposition of two analytic procedures—partial order scalogram analysis (POSAC) and cluster analysis—enables one to uncover such outliers in a data set. Importantly, the 2D POSAC space clearly reveals the hierarchical structure of the body image.