Female V. Male Happiness
Group B: Andrew Nelson, Steven Jung, Colin Suehiro, Matus Koronthaly
Summary of Findings:
To start with the simplest and most basic overview, the graphs show a slight but noticeable difference in satisfaction levels between our chosen categories. On average, female happiness levels were lower than that of the male respondents. At the same time, the female responses had far less variability in terms of outliers then the male results and didn’t hit quite the same extremes. While on average male respondents claimed higher satisfaction levels, on an individual level male respondents claimed lower satisfaction levels then the worst female response.
For the elderly population, it becomes harder to draw any conclusions. Having read the Economist article detailing the “U-bend of life” theory, we had initially expected our “50-54” respondents to have a higher average happiness then the student respondents, regardless of gender. The data gathered, however, is difficult to extrapolate any useful results from. Instead of the standard bell curve one expects when mapping a population, with easily identifiable standard distributions of results, the elderly population showed a for want of a better phrase “flat” curve, with both male and female respondents showing more outliers and a lower average then expected. This proved problematic to interpret, as it seems to directly contradict the theories put forth in the class and in the Economist article (which would have predicted or assumed a higher average satisfaction level for the elderly then for the student population rather than the reverse). According to the U-curve theory, the elderly start being less self-conscious, less stressed about the future, and more willing to “live in the moment” - all of which boosts happiness according to most of the materials we studied in class as well as most of the readings we were assigned. I propose an opposite effect where for certain individuals, youthful hopes that have gone unfulfilled and a personally observed negative or stagnant trend leads to a net decrease of happiness. This doesn’t necessarily oppose the U-curve, as it is individualized, and both effects taken in concert would explain the data stream and resulting graph where instead of a neat clump we have an almost even distribution with a considerable group of outliers both far below and far above what the student graphs showed. In other words, I think part of our problem can be laid at the feet of too small a sample size (a negligible portion of our respondents actually fit the category of 50-54 when compared to the study as a whole) skewing our results, and would postulate that with a larger sample size to account for these individual effects the sample distribution should return to the results expected by the U-curve theory (e.g. a higher average then any of the student graphs and male/female effects comparable to those visible in our student population). According to several articles and University studies we were able to find, female happiness used to be higher than male happiness until about the 60s, when we see what various articles call the “shift” or the “reversal.” This is in line with both the in-class work and the Economist article, which also noted this shift as well as pointing out higher rates of depression in female participants.
As previously mentioned in the discussion of our results, the female graph ended out more constant and less variable then the male graph across most of our student populations (Students – Class, Students – UW and Students – SU). Part of the female consistency in the graphs might be residual effects from the ongoing change in averages, where they don’t hit as great a low (because their outliers used to be higher than male outliers) but the decreasing happiness rates lower the upper outliers from the final quartile and force a sort of consistency of results. That being said, more study would be needed to make any generalized statements and the statistical significance of our results is not sufficient to draw away any conclusive findings.
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|Last updated||December 24, 2018|
|Created||December 24, 2018|
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