Don’t worry, I didn’t make my changes in response to your comment specifically – I just realized that the section that I changed was weak because I was unnecessarily vague in my comments along the lines of “here is the data, but the effect size is smaller than it looks because of regression to the mean” – I could instead just show the degree to which past behavior predicted future behavior.
I fully intend to change it
Anything that I might be able to help with? I have a background is in math education. You might find some of the pointers in the Cognito Mentoring math and statistics learning recommendations to be useful.
I have to just hope that the equation-using overlords know what they are doing.
My experience has been that while statistics sometimes requires substantive mathematical knowledge, intuition built from exposure to data and experience with thinking about it gets you ~80% of the way to a full understanding. On the flip side, mathematical knowledge without exposure to real world data doesn’t get one very far...
Especialy the trade-off thing...it fits together with a lot of the other literature on prioritizing in long term and short term mating
It’s certainly natural to hypothesize that the differences came from some people looking for short term mates and others looking for long term mates, but I’m not sure that that’s what was going on. You’ll see what I mean when I show the demographic correlates. I think that your reference to assortative mating is highly relevant.
Kinda wish we had data on menstrual cycle phase to go with this :P
There was in fact more variation with respect to preference on the tradeoff dimension amongst women than amongst men. The comparison isn’t an apples to apples one, because e.g. men found women more attractive than women found men. The difference isn’t very large: 0.42 standard deviations vs. 0.36 standard deviations.
If you’re planning on publishing you might want to cite stuff within that whole body of work.
I need to talk with psychology researchers to get a better sense for how what I’ve done fits in with the literature. In the past, I read some papers on the subject out of casual interest, but I haven’t done a deep dive.
I would guess that some of the phenomena in the data are in fact previously unknown to academic psychology. The dataset is unusual in a number of ways: the sample size is large (~500 people), the data was collected in a real world context where people were actually looking to find partners as opposed to a lab setting, and many features were collected. Moreover, even to the extent that there exist equally rich datasets, they’re not in the public domain (sometimes for privacy reasons, sometimes because the researchers aren’t motivated), so nobody but the researchers would be able to analyze them. Fisman and Iyengar were unusually shrewd in their choice of experimental design, and unusually generous in going out of their way to anonymize and publish their data.
Of course, many people are familiar with the phenomena at an informal level, through observations of peers and personal experience, but the effect sizes may not have been quantified.
I found it all quite understandable, clear, and interesting =)
Thanks :-)
Although, an abstract up-front would probably help reach more readers..
Yes, several people have said this. What do you see as the key points of the article? It may seem as though I should know better than you :P, but I’m not sure how best to summarize the situation, which seems to have irreducible complexity, although impressions of this type often turn out to be mistaken.
Don’t worry, I didn’t make my changes in response to your comment specifically – I just realized that the section that I changed was weak because I was unnecessarily vague in my comments along the lines of “here is the data, but the effect size is smaller than it looks because of regression to the mean” – I could instead just show the degree to which past behavior predicted future behavior.
Anything that I might be able to help with? I have a background is in math education. You might find some of the pointers in the Cognito Mentoring math and statistics learning recommendations to be useful.
My experience has been that while statistics sometimes requires substantive mathematical knowledge, intuition built from exposure to data and experience with thinking about it gets you ~80% of the way to a full understanding. On the flip side, mathematical knowledge without exposure to real world data doesn’t get one very far...
It’s certainly natural to hypothesize that the differences came from some people looking for short term mates and others looking for long term mates, but I’m not sure that that’s what was going on. You’ll see what I mean when I show the demographic correlates. I think that your reference to assortative mating is highly relevant.
There was in fact more variation with respect to preference on the tradeoff dimension amongst women than amongst men. The comparison isn’t an apples to apples one, because e.g. men found women more attractive than women found men. The difference isn’t very large: 0.42 standard deviations vs. 0.36 standard deviations.
I need to talk with psychology researchers to get a better sense for how what I’ve done fits in with the literature. In the past, I read some papers on the subject out of casual interest, but I haven’t done a deep dive.
I would guess that some of the phenomena in the data are in fact previously unknown to academic psychology. The dataset is unusual in a number of ways: the sample size is large (~500 people), the data was collected in a real world context where people were actually looking to find partners as opposed to a lab setting, and many features were collected. Moreover, even to the extent that there exist equally rich datasets, they’re not in the public domain (sometimes for privacy reasons, sometimes because the researchers aren’t motivated), so nobody but the researchers would be able to analyze them. Fisman and Iyengar were unusually shrewd in their choice of experimental design, and unusually generous in going out of their way to anonymize and publish their data.
Of course, many people are familiar with the phenomena at an informal level, through observations of peers and personal experience, but the effect sizes may not have been quantified.
Thanks :-)
Yes, several people have said this. What do you see as the key points of the article? It may seem as though I should know better than you :P, but I’m not sure how best to summarize the situation, which seems to have irreducible complexity, although impressions of this type often turn out to be mistaken.