Also this week: Deborah Mayo vs. Andrew Gelman on statistical power, a new analysis of the leaky pipeline, the verjus theory of blogging, Excel=C, and more.
This is a few months old but I missed it (and embarrassingly, can’t recall if we’ve already linked to it): a new preprint analyzing the biennial NSF Survey of Doctoral Recipients, a longitudinal study that follows the career paths of thousands of STEM Ph.D. receipients from the year they received their Ph.D. until age 76 (ht the Chronicle, which has a summary). Using data for the 31,000+ people surveyed who got their doctorates from 1993-2010, the authors find that:
- 20% of STEM Ph.D.s get a tenure track position within 3 years of their Ph.D. (Though I bet that number would be lower if you restricted attention to the most recent Ph.D.s)
- The conditional probability of getting a tenure-track job, given that you haven’t gotten one yet, is highest 2 years post-Ph.D., and declines to less than 1% 10 years post-Ph.D. (Note: this is broadly consistent with survey data from ASLO that Meg’s linked to in the past, indicating IIRC that ecologists who get faculty positions mostly get them 4 years post Ph.D. or less.)
- Post-Ph.D., women are hired into tenure-track positions about 6 months before men on average. Blacks and Hispanics are hired about a year before whites on average, while Asians are hired about 2 years later than whites on average.
- Overall, women are about 10% more likely than men to obtain a tenure-track position. Blacks and Hispanic are 51 and 30% more likely than whites to obtain a tenure-track position, while Asians are 33% less likely.
- Women and Blacks who obtain a tenure-track position are less likely to get tenure than men and whites, respectively, although the sex effect disappears if you control for heterogeneity among disciplines.
- Controlling for marital status, parenthood, and their interactions with one another and with sex has complex effects that I find a little difficult to interpret, but that might make more sense to people who study this stuff. But it looks like there’s a “baby penalty”: women with children under 6 are about 15-22% less likely to get a tenure track position, and to get tenure once they’ve gotten a tenure-track position, than men or other women.
Note that the analysis doesn’t consider lots of other covariates you might want to consider, like career intentions, achievements like publications and awards, etc. So it’s not a complete analysis of the leaky pipeline (that’s probably impossible). As the authors note, it doesn’t show that there’s now “reverse discrimination”. And this sort of analysis obviously doesn’t show that incidents of sexism and racism are a thing of the past. But together with previous studies of pre-1993 cohorts, the results do suggest that, on average, academic job prospects for women, Hispanics, and Blacks have improved a lot, particularly at the hiring and pre-hiring career stages. The authors suggest that the strength of the “baby penalty” indicates a need to focus on child care, family leave, and tenure clock policies to make further progress.
Stephen Heard gave a talk on the history of scientific writing to an English department. He was nervous going in, but it turned out well.
The NSF DEB is great about using its blog to disseminate information and dispel myths about the grant evaluation process (e.g., this). I missed it at the time, but earlier this spring the NSF IOS did the same, presenting a bunch of data on the effects of the new preproposal system. See here, here, and here. Bottom line: the preproposal system is working as intended, and widespread fears about potential bad effects have not come to pass. The only big thing the posts don’t comment on directly (unless I missed it in my quick skim) is whether the advent of the preproposal system had the unintended side effect of leading to a big jump in the number of submissions and an associated drop in success rates, as occurred at DEB. Relatedly, see here and here for some commentary on the difficulty funding agencies have of managing submission volume and success rates.
Economist Mark Thoma taught some online courses and now thinks more highly of them. Here’s his list of their good and bad points. Note that many of the good points depend on students having sufficient self-motivation and study skills to figure things out for themselves. (ht Brad DeLong)
Terry McGlynn is going to stop ignoring ResearchGate. I’m still ignoring it, but with less certainty.
Andrew Gelman links to the latest developments in the ongoing Tim Hunt fight (in his p.s.). I don’t have any opinion on the Hunt incident, at least not one I’m sufficiently confident in to share publicly. I haven’t followed it closely. In general, I personally find it very difficult to sort truth from falsehood and wisdom from its opposite in these sorts of social media-driven fights. As with Andrew, this is an illustration of why I stick to blogging rather than Twitter, and why I personally prefer not to use these sorts of incidents as an occasion to comment on larger issues of unquestioned importance. But I’m sure your mileage may vary on all this. I’m just noting my own personal attitudes, which I wouldn’t necessarily expect others to share.
Oikos Blog, where I got my blogging start, hasn’t posted anything since February. RIP? I’d be sad to see it go, even though I haven’t looked at it much since I left and they switched to posting summaries of forthcoming Oikos papers (which is totally fine, it’s just not what I personally look for in a blog). I still think the original idea for Oikos Blog–all the editors would post interesting, provocative thoughts, somehow related to (but not just summarizing) the journal’s content–is well worth a go for any ambitious journal that wants to try it. But I doubt it’ll happen. Most people don’t want to write that sort of blog post even occasionally, so I doubt you’d be able to get an entire editorial board to start blogging. Much less get the board to keep it up for long enough (months at least; more likely years) to build enough of an audience to make a material difference to the journal.
This is a month old but I missed it at the time: a historian argues that no, Watson and Crick didn’t “steal” Rosalind Franklin’s data, or “forget” to give her credit. And while they certainly treated her data cavalierly, there’s no evidence that they’d have treated data collected by a man less cavalierly. Which isn’t to excuse Watson’s appalling sexist attitude toward Franklin or downplay the importance or quality of her scientific work, of course. Interesting deep dive into the details of a famous moment in the history of science.
Dan Davies’ verjus theory of blogging. Or, why you should worry about audience quality rather than quantity (or really, worry about writing what seems worth writing, and let the audience look after itself). I agree with this. I would only add that, if you’re writing for a niche audience of professionals (as we are), the only way to get traffic is to not try to get it. So somewhat contra Davies, there’s no necessary trade-off between audience quality and quantity for professional niche blogging.
Deborah Mayo with a good post on the interpretation of statistical power. This is a tricky and controversial subject. I need to sit down at some point and figure out how her argument relates to the apparently-opposing argument of Andrew Gelman. I think that they’re asking subtly but importantly different questions, and that the disagreement comes down to which question is the best one to ask. Or maybe they’re just saying the same thing in very different ways, so that their apparent opposition is merely apparent. I’m not sure yet. (And if you care to enlighten me in the comments, please do!)
Here’s why R functions like read.table and read.csv default to reading character strings as factors. I mostly use R for traditional statistical tasks, so I like this default and had no idea anyone found it annoying. I liked this line at the end:
I fully expect that this blog post will now make all R users happy.
And finally, sticking with programming links that may be good or bad news, depending on your point of view: you can convert Excel spreadsheets into C!