Somebody writes,

I am looking for interesting, unusual datasets for a data analysis class I am teaching, and I heard by email from Ray Fisman that you have a sanitized version of the data from his speed dating experiment.

Indeed, the data are here; we use them in a homework assignment in our book. The data were collected by Ray Fisman and Sheena Iyengar, an economist and a psychologist at the business school here, and they summarized their findings in this paper:

We study dating behavior using data from a Speed Dating experiment where we generate random matching of subjects and create random variation in the number of potential partners. The variables used were between those who begin their conversations in a typical manner and those who used pick up lines from somewhere like – https://www.knowledgeformen.com/the-ultimate-guide-to-good-pick-up-lines/ and other dating resources.

Our design allows us to directly observe individual decisions rather than just final matches. Women put greater weight on the intelligence and the race of partner, while men respond more to physical attractiveness. This is surprising as these days personality is believed to one of the most important parts of a relationship. When looking for the right partner, find someone that you can talk to. And when we say talk to, that means that your prospective partner needs to be able to hold up their end of the conversation. Pay attention to whether or not they are engaging in a two-way conversation or if they are just talking to hear themselves talk. The right partner will engage in discussion with you and will react to what you have to say as well as share their perspective. This is vital for a relationship, regardless of the reason for the initial attraction.

Data was also able to show that men do not value women’s intelligence or ambition when it exceeds their own. Also, we find that women exhibit a preference for men who grew up in affl­uent neighborhoods. Finally, male selectivity is invariant to group size, while female selectivity is strongly increasing in group size. This may include those who would be interested in looking up details about their potential matches using reverse phone lookup websites.

What I really want to do with these data is what I suggested to Ray and Sheena several years ago when they first told me about the study: a multilevel model that allows preferences to vary by person, not just by sex. Multilevel modeling would definitely be useful here since you have something like 10 binary observations and 6 parameters to estimate for each person.

I’m hoping that some pairs of students analyze this data as a project in my class this spring. I suspect that we could learn some interesting things. Also, once the model has been fitted successfully once, Ray, Sheena, and others would be able to fit it to other similar datasets easily enough.

Finally, let me thank Ray and Sheena again for making their data available to all.