Presentation on theme: "How valuable is beauty? By: Rachael Bradford, Sean Christiansen, Karla Diaz, Veronica Hayes, & James Mitchell."— Presentation transcript:
How valuable is beauty? By: Rachael Bradford, Sean Christiansen, Karla Diaz, Veronica Hayes, & James Mitchell
The definition of Physical Attractiveness is the degree to which a person's physical traits are considered aesthetically pleasing or beautiful. (Wikipedia)
How much does the average person value such a concept? We decided to put our question of beauty to the test by designing, deploying and analyzing a short online survey.
We wondered; does age, gender and education affect a persons perception of attractiveness?
By using the free survey option offered by Survey Monkey, we sent our survey link into the world via facebook and email and patiently waited…
The survey was open to collect responses from Monday, April 8 th through Friday, April 12 th.
The survey asked… On a scale of 1 to 5 where 1 means not at all and 5 means very much, how much value do you place on physical beauty in another person? 3 images were then shown. After each image, we asked the individual to rate the previous image. Please rate the person in the previous image on a scale from 1 to 10, where 1 means Not Attractive/Beautiful At All and 10 means Very Attractive/Beautiful.
The first image was of a young Audrey Hepburn, demonstrating what some may describe as classic beauty.
The third image was of a middle aged Afghani man.
We then asked a few last questions to better analyze the responses. What is the highest level of education you have completed? What is your age? What is your gender?
The overall responses did not surprise us, knowing our peer demographics (for the most part) were younger, more educated and most likely an even split between males and females. In total, we gathered responses from 57 individuals. Are you as curious to see the results as we were?
Overall, the results were close to what we had expected. We wanted to find out if different groups of people rated the images differently. This is where things get…
Though we collected data on multiple questions, we decided for the sake of this presentation and data analytics to focus on the first question: On a scale of 1 to 5 where 1 means not at all and 5 means very much, how much value do you place on physical beauty in another person?.
Our hypotheses were as follows: -Education: Those with more education will place a lower value on physical beauty in another person. -Age: Younger respondents will place a higher value on physical beauty in another person. -Gender: Female respondents will place a higher value on physical beauty in another person.
Our results were contradictory to our subconsciously biased ideas… Those with more education placed a higher value on physical beauty. Younger respondents placed a lower value on physical beauty. Female respondents placed a lower value on physical beauty.
Education Ho: mu = 7.61t0 = -2.67 Hi: mu < 7.61t(0.05) =-1.67 N = 63 a= 0.050.0025 < P- Value < 0.005 df = 60Because the p-value is less than the level of significance a=0.05, we reject the null hypothesis. P-value is telling us that we obtained unusual results based on our assumption mu = 7.61. Therefore, our assumption may be incorrect. There is sufficient evidence that individuals that are less educated tend to view beauty lower than those with a higher education.
Age Ho: mu = 8.24to =-2.171 Hi: mu < 8.24t(0.05) = -1.699 a = 0.05 N = 300.01 < P-value < 0.02 df = 29 Because the p-value is less than the level of significance a=0.05, we reject the null hypothesis. The P-value is telling us that we obtained unusual results based on our assumption mu = 8.24. Therefore, our assumption may be incorrect. There is sufficient evidence that younger individuals tend to view beauty lower than older individuals.
Gender Ho: mu = 7.80to = -2.611 Hi: mu < 7.80t(0.05) = 1.706 a = 0.05 N =270.01 < P-value < 0.005 df = 26Because the p-value is less than the level of significance a=0.05, we reject the null hypothesis. The P-value is telling us that we obtained unusual results based on our assumption mu = 7.80. Therefore, our assumption may be incorrect. There is sufficient evidence that males tend to view beauty lower than females.
In conducting our study, we did observe that there were areas for improvement if another survey was run. First we noticed that using the free survey site Survey Monkey came with a few limitations in itself. Survey Monkeys free option has a 10 question limit (including graphics), a 100 survey limit, no question rotations, and no conditional logic.
We deployed the survey via facebook and email, so data was collected from those who had access to the computer and internet. The survey was an observational study, which used a self- selected convenience sample to collect results. Due to the survey being deployed via facebook, the average age of data collected was 25-35 years of age; therefore this is not representative of the population. A sampling bias may be present (ie: those who chose to opt in versus opt out as well as those we were unable to reach due to no computer or internet access). Also, there may be under coverage (the survey data skews towards a younger age group). Maybe if we had more time to conduct other survey methods besides facebook and email, we may have had a bigger sample group than the 57 individuals who chose to take the survey.
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