Naor-Ziv Revital, King Rose, Glicksohn Joseph
Department of Criminology, Bar-Ilan University, Israel.
The Leslie and Susan Gonda (Goldschmied) Multidisciplinary Brain Research Center, Bar-Ilan University, Israel.
J Pers Oriented Res. 2020 Sep 10;6(1):28-38. doi: 10.17505/jpor.2020.22044. eCollection 2020.
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.
人们如何对体型进行排序？这与人们最不满意的身体部位有关吗？我们的研究调查了是否可以归纳出女性对身体的呈现方式。从摄影体型评定量表中选取了三张经身体质量指数（BMI）校准的图像，分别代表瘦（BMI = 14.72）、中等（BMI = 20.33）和胖（BMI = 29.26）的体型，将其分为躯干、腿部和手臂部分，并以黑色为背景保存为单独的图像。在27种可能的合成图像中，根据躯干（瘦与胖）×腿部（瘦/胖与中等）×手臂（瘦与胖）的设计选择了8张。我们的44名女性参与者将这些图像从最瘦到最胖进行排序。首先是根据躯干，然后是腿部，最后是手臂：41人对最瘦的图像（瘦躯干、瘦腿部、瘦手臂）达成一致，其次是第二张图像（瘦躯干、瘦腿部、胖手臂，n = 26；或瘦躯干、中等腿部、瘦手臂，n = 10）。一名参与者对第一张图像的选择明显不同（胖躯干、中等腿部、瘦手臂）。有趣的是，她在饮食失调倾向量表（EDI - 2）上的得分是10分，显示出患贪食症的高风险，这表明我们的任务可能对研究饮食失调有用。我们将两种分析程序——偏序量表分析（POSAC）和聚类分析——并列使用，能够在数据集中发现此类异常值。重要的是，二维POSAC空间清楚地揭示了身体形象的层次结构。
