Background: To investigate what makes a female figure attractive, an extensive experiment was conducted using high-quality photographic stimulus material and several systematically varied figure parameters. The objective was to predict female bodily attractiveness by using figure measurements.
Methods: For generating stimulus material, a frontal-view photograph of a woman with normal body proportions was taken. Using morphing software, 243 variations of this photograph were produced by systematically manipulating the following features: weight, hip width, waist width, bust size, and leg length. More than 34,000 people participated in the web-based experiment and judged the attractiveness of the figures. All of the altered figures were measured (e.g., bust width, underbust width, waist width, hip width, and so on). Based on these measurements, ratios were calculated (e.g., waist-to-hip ratio). A multiple regression analysis was designed to predict the attractiveness rank of a figure by using figure measurements.
Results: The results show that the attractiveness of a woman’s figure may be predicted by using her body measurements. The regression analysis explains a variance of 80 percent. Important predictors are bust-to-underbust ratio, bust-to-waist ratio, waist-to-hip ratio, and an androgyny index (an indicator of a typical female body).
Conclusions: The study shows that the attractiveness of a female figure is the result of complex interactions of numerous factors. It affirms the importance of viewing the appearance of a bodily feature in the context of other bodily features when performing preoperative analysis. Based on the standardized beta-weights of the regression model, the relative importance of figure parameters in context of preoperative analysis is discussed.
From the Departments of Experimental and Applied Psychology and Plastic Surgery, University of Regensburg, and Department of Plastic and Aesthetic Hand and Reconstructive Surgery, Caritas-Krankenhaus St. Josef.
Received for publication November 19, 2007; accepted July 24, 2008.
Disclosure: None of the authors has a financial interest to declare in relation to the content of this article.
Martin Gründl, Ph.D., Department of Experimental and Applied Psychology, University of Regensburg, Universitätsstr. 31, 93053 Regensburg, Germany, firstname.lastname@example.org