mathbabe

This is crossposted from Naked Capitalism.

A study recently came out which was entitled “Can stereotype threat explain the gender gap in mathematics performance and achievement?”. One of the authors created and posted a video describing the paper, which you can view here.

As a preview, there seem to be four main points of the paper and the video:

1. The papers on stereotype threat normalize with respect to SAT scores which is bad.
2. Evidence for stereotype threat is therefore weak.
3. We should therefore stop putting all of our resources into combating stereotype threat.
4. We should instead do something easy like combating stereotypes themselves.

Before we go into the details of the paper, we need a bit of context. For that reason, this post is split into three parts. The first addresses a meta-issue, namely that of the “null hypothesis” in this discussion. A frustration that I have, and that I think is shared by many of the women I know in math, is that the (often unspoken) working hypothesis is that in fact women are just not as talented, and it is somehow up to us women to prove this otherwise, presumably by convincing men that we’re geniuses.

The authors of the above paper fall prey to this disingenuous line of thought, by proclaiming stereotype threat is an insufficient explanation but not offering any alternative explanations. This sets up a kind of implied false dichotomy: if it isn’t explained by such and such, it must mean girls are dumb.

Not only does this undermine serious intellectual debate, but it often turns people off from entering the debate in the first place, because they sense the manipulative nature of the discussion. But that’s a pity, since, with the correct assumption, namely that women and men have equal talents but things are holding back women, we could probably make lots of progress on what those things are.

The second part is directly related not to the paper but to the blog post which referenced the paper, which changed the conversation from “math performance gap” to the question of “why there are no women math geniuses”. This is an interesting twist, and in my opinion warrants addressing separately.

In the third part I argue directly against the paper and its conclusions.

1. The Null Hypothesis

Needless to say, I think the onus is on the scientific community to prove that women aren’t as mathematically talented as men. In other words, I do not accept the defensive position that I need to prove we are as smart: the null hypothesis is that a series of effects, one of them stereotype threat, explains any perceived difference in talent.

In his now famous lecture at NBER in 2005, Larry Summers putatively discusses the issue of why there are fewer tenured women in science and math departments at top universities. However, if you read the transcript, you will note that, when he gets to the “different availability of aptitude at the high end” part, he does us a favor of sorts by admitting what his underlying working hypothesis is: that girls aren’t as good at math. His argument using standard deviations of test scores is ridiculous, especially if you consider 1) how differently women do versus men on the same test in different conditions, 2) how much that difference has itself changed over time, and of course 3) the question of what the tests themselves are measuring.

To test why this null hypothesis is so damaging, my friend Catherine Good suggested the following thought experiment: imagine if he’d gone up to the podium and, instead of saying that women aren’t all that good at math and it was partly explained by when he’d given boyish toys to his twin girls that they took care of them instead of constructed things, he had instead substituted gender with race. Here’s the passage:

There may also be elements, by the way, of differing, there is some, particularly in some attributes, that bear on engineering, there is reasonably strong evidence of taste differences between little girls and little boys that are not easy to attribute to socialization. I just returned from Israel, where we had the opportunity to visit a kibbutz, and to spend some time talking about the history of the kibbutz movement, and it is really very striking to hear how the movement started with an absolute commitment, of a kind one doesn’t encounter in other places, that everybody was going to do the same jobs. Sometimes the women were going to fix the tractors, and the men were going to work in the nurseries, sometimes the men were going to fix the tractors and the women were going to work in the nurseries, and just under the pressure of what everyone wanted, in a hundred different kibbutzes, each one of which evolved, it all moved in the same direction. So, I think, while I would prefer to believe otherwise, I guess my experience with my two and a half year old twin daughters who were not given dolls and who were given trucks, and found themselves saying to each other, look, daddy truck is carrying the baby truck, tells me something. And I think it’s just something that you probably have to recognize.

It begs the question, why did the women in kibbutz quit working on tractors? The way Larry tells his story, he makes it clear he thinks that it’s because the women wanted it that way (thus his story about the twins). But surely it is as plausible that: 1) Men, having a vested interest in proving their manhood (which they do and in cultures around the world leads to certain types of work being seen as “manly”) weren’t keen about day care duty and/or 2) women were hesitant to cross the lines of gender stereotype (it might lead them to be perceived as being masculine, or even worse, emasculating). And it also isn’t hard to imagine that parents ooh and ahh more when small children play with what are perceived to be gender-appropriate toys and are quietly or even vocally uncomfortable when boys play with dolls and girls play with trucks.

One last word about the null hypothesis and why I’m so devoted to this issue: when I and two other girls (and, as it happens, no boys) in the 6th grade did well enough to go into a special, advanced 7th grade algebra class, my (female) teacher brought us up to the front of the room and told the three of us “I don’t see why you would challenge yourselves like this anyway since you are girls, and you won’t be needing math when you grow up.” I was the only one of the three of us to actually choose that class, and I was the only girl in the algebra class. One of my friends was one of two women in a class of 45 students studying artificial intelligence at Yale. She was expecting praise for being one of only two students to get a program to work on a particularly tough assignment. Instead, she was accused by the professor of stealing the code from her male classmate. She left the major. Until stories like this become rare, or even uncommon, I will assume that there’s too much cultural influence to figure out the real story.

Going back to Larry Summers, his lecture did two things: 1) it breathed new life into the age-old stereotype that women aren’t as good at math as men, and 2) it attributed that difference to an underlying innate ability difference- that is, he conveyed a “fixed ability mindset” regarding math (more on mindsets below). As the leader of an educational institution he introduced the two ideas that together are like a powder keg: they can undermine women’s feelings of belonging in math, which in turn informs their mathematics achievement and intrinsic motivation to remain in math.

Now more about Catherine Good. She talked at that same conference where Larry Summers put his foot in his mouth; in fact she was the speaker after Larry at that conference, and she was talking about her paper that gives evidence that the above “powder keg” message tends to push women out of math (but Larry didn’t stick around long enough to hear her talk, unfortunately). She is also an expert on stereotype threat and helped me look at the study. More on her thoughts below, but I still want to talk about the concept of “genius.”

2. Women and the concept of genius

Let’s define, as one of the commenters does from the blog, a “genius woman in math” to be any woman who has won a Fields Medal. Since there are no women who have won Fields Medals (versus 52 men), this is a pretty tight definition. I would argue, and I might in another post, that even without the above definition, the concept of “genius” is a social construct which is rarely if ever applied to women, except perhaps after they’re dead. Please comment with counterexamples if you know of any.

So here’s what I think. There are lots of reasons that women don’t win Fields Medals. I will name a few.

• Fields Medals are awarded to mathematicians under the age of 40, for some reason, and women mathematicians typically do good work into their retirement age, whereas men usually do their best work young (this also explains why Harvard has so much trouble hiring women- by the time they are convinced the woman is a genius, she’s 55 and has grandchildren and frankly probably sees the offer as tokenism).
• The commenter who defined a “math genius” as a Fields Medalist said that it would be an objective measure. But Fields Medals are awarded by a bunch of guys who decide what’s important and who’s responsible for the important results. In other words it’s a political process.
• Women don’t care as much about winning Fields Medals. This matters, because I know of men who explicitly worked on problems in order to win the Fields Medal (you know who you are). It’s a serious and bizarre case of narrow focus.
• Why is math genius defined so narrowly? I would personally define it more broadly (a topic for another post), and there’d be plenty of women geniuses. With my definition, though, I’d guess that women who are geniuses have lots of options and they often choose something they consider more personally rewarding than an academic job.
• Women’s intelligence may also manifest in different ways: note that most of the assholes on Wall Street are men. This kind of makes sense since women are typically not as driven by testosterone and competitiveness. This doesn’t mean they aren’t geniuses or that they couldn’t have done the work the men on Wall Street did (my experience proves that).
• The Fields Medal distorts the mathematical process itself, by implying that there’s a single superstar who swoops in and solves the problem that all the other people were incapable of doing. In fact mathematics as a field is an enormous collaboration, a scientific project, where everyone depends on the community around them for coming up with questions, defining the “interestingness” of questions, and giving context to results. The idea that there’s one winner out of all of this, or even one metric by which we could measure such a winner, is silly. See this post from Quomodocumque.
• Another point about genius (in any domain): research is showing that to truly express one’s genius takes thousands of hours of practice. So genius may be a latent trait but will never be expressed without many hours of hard work. This point is very often lost and is related to women in that their apparent geniusness depends to a large extent on how supportive their environment is for all that investment of time.

3. The paper against stereotype threat

I am finally ready to address (with Catherine’s help) the issues of the paper in question, which I will repeat:

1. The papers on stereotype threat normalize with respect to SAT scores which is bad

In fact the author “discards” a bunch of stereotype threat studies on these grounds. However, it is totally standard to normalize with respect to some other metric (would you rather we didn’t normalize to anything?), and in fact it essentially penalizes the studies, since it has been shown that stereotype threat is in play even for the SATs. On the other hand, the standard for normalizing (this is called “including a covariate”) is that the groups being compared should not differ significantly in the covariate, presumably because it’s harder to argue that your are in fact correcting for that aspect. Because men and women sometimes do differ significantly in SAT scores, including them as covariates could be a technical violation of the rules of conducting a so-called ANCOVA.

Is this what the author is complaining about specifically? Did he, for example, check to see if the samples in the “discarded” studies actually differ in the covariate? It seems he’s making the assumption that they did, but it’s not clearly stated that they did. It’s certainly not a given that the men and women in these studies did differ in the covariate, and he needs to make that precise. If they did not, then there’s no valid argument against using SAT scores.

2. Evidence for stereotype threat is therefore weak.

There is ample evidence that stereotype threat is very real. Keep in mind that the authors of this study have not shown evidence against stereotype threat, but have simply complained that they don’t like the existing studies for it. And their standard for what “replicates” the original study is overly stringent- they only wanted to include studies that found significant interactions between gender and condition. Interactions are easiest to find when you have a “crossover effect” (e.g. males are higher in condition A but lower in condition B), but often we find “span effects” in which the males and females may be equal in condition A but differ in condition B. This can also be an example of stereotype threat. For example, in a paper written by Catherine, she didn’t find a significant interaction (males and females performed equally in condition A) but when the stereotype threat was reduced, women outperformed men. To discount this and other studies as not providing evidence of stereotype threat simply because an “interaction” wasn’t found is playing games with statistics.

3. We should therefore stop putting all of our resources into combating stereotype threat.

Nobody who studies stereotype threat claims it explains everything. It is part of a larger picture. The good news is that there are interventions for it (described below).

4. We should instead do something easy like combating stereotypes themselves.

The idea that it’s “easy” to combat stereotypes is completely naive. There are tons of ways that stereotyping is understood to be very difficult, if not impossible, to get rid of. Some of them have to do with an evolutionary need to simplify first impressions of people (i.e. categorize) so that we can tell if they are an immediate threat to our safety. This may be the most baffling part of the whole thing, because the authors should really know better.

I want to end on a positive note, because the news is actually pretty good. There is a way to combat stereotype threat, and I’ve tried it and it works. To understand it, it helps to think about the way people think about intelligence itself. As a simplification, people either think that intelligence is fixed and rigid (you’re either born with it or you’re not) or they think that intelligence is malleable and can be learned and practiced.

It turns out that if someone believes the latter “malleable intelligence” view, then they work hard and are hopeful and stereotype threat is to a large extent alleviated. Whereas if they’re convinced of the former mindset for intelligence, the effect of stereotype threat is more pronounced. In situations where the stereotype is salient (“girls are bad at math” is salient when taking a math test), the situation itself can convey a mindset of fixed ability and all the hallmark responses that go along with that mindset then follow. To encourage a malleable view of intelligence can help combat that fixed view and thus the threat of the stereotype.

The way I used this information was as follows. I started a class in teaching proof techniques at Barnard College (there were both Barnard students and Columbia students in the class). At the beginning of every class for the first two weeks I described how mathematicians aren’t born knowing how to prove things, but rather they learn techniques, and practice them until they are proficient. Note I wasn’t directly confronting or addressing stereotypes, but rather setting up the mindset where the studies have shown stereotypes have less negative power.

The class went great, and is still going on. I will post soon about my experiences starting that class and others like it.