Added: Emalee Edmond - Date: 21.12.2021 19:51 - Views: 30462 - Clicks: 7604
Today, finding a date is not a challenge — finding a match is probably the issue. InColumbia University ran a speed-dating experiment where they tracked 21 speed dating sessions for mostly young adults meeting people of the opposite sex. I was interested in finding out what it was about someone during that short interaction that determined whether or not someone viewed them as a match.
The dataset at the link above is quite substantial — over 8, observations with almost datapoints for each. However, I was only interested in the speed dates themselves, and Speed dating odds I simplified the data and ed a smaller version of the dataset to my Github here. We can leave the first four columns out of any analysis we do. Our outcome variable here is dec. Before I Speed dating odds to do any analysis, I want to check if any of these variables are highly collinear — ie, have very high correlations. If two variables are measuring pretty much the same thing, I should probably remove one of them.
But none of these get up really high eg past 0. I might want to spend a bit more time on this issue if my analysis had serious consequences here. The outcome of this process Speed dating odds binary. The respondent decides yes or no. This could be a factor of the population being studied, whom I believe were all undergraduates at Columbia and so would all have a high average SAT I suspect — Speed dating odds intelligence might be less of a differentiator. Everything else seems to play a ificant role. More interesting is how much of a role each factor plays.
The Coefficients Estimates in the model output above tell us the effect of each variable, assuming other variables are held still. We find a couple Speed dating odds interesting differences. True to stereotype, physical attractiveness seems to matter a lot more to men. And as per long-held beliefs, intelligence does matter more to women.
Men seemingly prefer new interactions, versus women who like to see a familiar face. As I mentioned above, the entire dataset is quite large, so there is a lot of exploration you can do here — this is just a small part of what can be gleaned. Interesting analysis! The speed dating dataset The dataset at the link above is quite substantial — over 8, observations with almost datapoints for each. The next seven columns are important. Then we have scores out of ten on six characteristics: attractiveness, sincerity, intelligence, fun, ambitiousness and shared interests.
The like column is an overall rating. The prob column is a rating on whether the rater believed that the other person would like them, and the final column is a binary on whether the two had met prior to the speed date, with the lower value indicating that they had met before. Running a logistic regression on the data The outcome of this process is binary.
Attractiveness seems substantially the primary positive indicator of a match. Interestingly, sincerity and ambitiousness decreased the likelihood of a match — they were seemingly turn-offs for potential dates. Other factors played a minor positive role, including whether or not the respondent believed the interest to be reciprocated. Like this: Like Loading Next Post Next post: An unfathomable act of selfishness? Leave a Reply Cancel reply.
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