What characteristics matter most when evaluating a person's attractiveness?

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Ended on 1/18/19
Campaign Ended
  • $1,147
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  • Finished
    on 1/18/19



Within the evolutionary psychological literature, many characteristics have been associated with attractiveness. These include facial symmetry, waist-to-hip ratio, intelligence, creativity, status, strength etc. 

However, these attributes are often considered in isolation. Thus, the relative importance of each characteristic is unclear. This research aims to compare these attributes side-by-side to see which contributes the most to an individual’s attractiveness and mating success.

We predict that when a large number of attractiveness criteria are considered together, only a small number will be related to an individual’s overall attractiveness. The others will become “overshadowed” and no longer account for unique variance in attractiveness. 

We also expect to find that attribute importance will vary by mating context - attributes such as physical attractiveness and social status will be more important within short-term rather than long-term mating contexts.

A full list of the measures to be included in the study can be found on the right-hand side under "List of measures."

In part 1 of the study, we will use these measures to predict the participant's self-reported attractiveness and sexual history as measured using questionnaires.

In part 2 of the study, we will see how these measures relate to other-reported attractiveness. To do so, we will collate each participant's information into a "profile" which displays their characteristics on a single page. For example, the profile will show the participant's picture and their percentile on other characteristics (e.g. strength and creativity etc.) relative to the others in the sample. Then, a large number of separate volunteers(n > 200) from a different cohort (universities and colleges elsewhere in the UK) will view and rate the profiles for attractiveness. 

Pre Analysis Plan

After cleaning and transforming the data. We will begin the analysis by correlating the measures of interest with the measure of attractiveness to establish. Attributes that significantly correlate with attractiveness will be entered into a step-wise regression analysis. This analysis will pit these variables against each other as they all try to uniquely predict the self- and other-reported attractiveness ratings.

Doing so will allow us to see which attributes are the largest contributors to attractiveness and which become “overshadowed”.


Browse the protocols that are part of the experimental methods.