Women, Math, and the Addition of Stereotypes
Do women often perform less well at higher math because of the stereotype that they have less ability than men, or is there another reason for the achievement gap?
Women and math have a checkered history in the popular imagination.
Remember the Barbie doll that said “Math class is tough”? Mattel removed that phrase from the doll’s repertoire in 1992 after an uproar from women’s groups. Thirteen years later, Lawrence H. Summers, then president of Harvard University, suggested that women may be “innately less able to succeed in math and science careers” and later apologized for those remarks, although he eventually resigned his post.
The debate gained new life in January when University of Leeds psychologist Gijsbert Stoet and University of Missouri psychologist David C. Geary published a meta-analysis of nine studies arguing that the stereotype of women being poor at math did not explain a gender gap in higher math achievement. Evidence of such a causal relationship “is weak at best,” they concluded in the Review of General Psychology. (Here’s a video of Stoet explaining their thesis.)
Their study led them to conclude that the wrong problem is being addressed, that programs devoted to erasing “stereotype threat” do more harm than good by allocating resources to a nonexistent problem. That outcome “really irritate(s) me,” Geary said, because redressing this gender gap is important “for our economy and for our future.”
Egged on by a press release that went further than Stoet and Geary’s study, media outlets in the U.S., Europe, and Asia trumpeted their research as “debunking” the theory that gender stereotyping and math achievement are related, even though the researchers themselves acknowledge such stereotyping may harm some women.
But their paper and the hoopla surrounding it stirred up other psychologists who have spent decades studying stereotyping. That larger camp points to a 2008 meta-analysis of the research on stereotype threat that examined 72 studies and found strong effects of stereotype threat on women’s higher level math performance.
Stoet and Geary did not examine programs that have improved women’s science, technology, engineering, and math performance by addressing women’s own doubt in their innate ability. “That’s the most important piece,” said social psychologist Claude M. Steele. “When you do something in the real world you get real meaningful improvement.”
Steele’s 1999 study with University of Waterloo social psychologist Steven Spencer launched the theory that belittling women’s ability, called “stereotype threat,” can affect women’s math performance, particularly when doing difficult, high-level math. (His book, Whistling Vivaldi: And Other Clues to How Stereotypes Affect Us appeared in 2010.)
Steele, who has also published extensively on the effect of stereotype threat on black students, criticizes the methods used in Stoet and Geary’s paper. “I cannot imagine how this got published. I’ve never seen such a manipulative work in all my years in science,” said the former provost of Columbia University and now the dean of Stanford University’s School of Education at Stanford University. “It’s astonishing.”
Geary and Stoet began their analysis by looking at 141 studies, but eliminated most of those for various methodological and statistical reasons, concluding those that remained demonstrated “little to no significant stereotype theory effect.” Because they included only nine of the published studies on stereotype threat, for his part Steele sees that as “good evidence of their manipulation of the data set to produce the result they want to produce.”
While Stoet and Geary excluded any studies that used covariant analysis – in this case, adjustment for performance on previous mathematics tests — Spencer countered that even without that correction, the studies still show variance between women affected by stereotype ideas and those not affected, regardless of initial differences. Stoet and Geary also left unpublished studies out of their meta-analysis, and Steele noted not only that that such “file-drawer” articles most often fail to replicate the original study, but also that good meta-analyzers contact “everybody in the field” and hound them for their unpublished work.
Geary replied that it is “very difficult” to find unpublished studies.
One brain study showed that women who were not told, “Research has shown gender difference is math ability and performance” before doing math problems showed heightened brain activity in the neural networks associated with mathematical learning. But women who did receive this message showed no activation in those networks, but did show increased activity in a part of the brain associated with social and emotional processing.
Stoet and Geary excluded this brain study from their meta-analysis and all others that didn’t test men as well as women. “Our goal was to examine studies that were able to replicate the original study [i.e. Steele’s], which (correctly so) included both men and women,” says Geary. “Our focus was on the replicability of the most basic design.” They determined that only 20 of the 141 studies replicated the original study.
Their critics, such as Steele, argue that exact replications are not necessary for scientific proof, and furthermore that similar — but not exactly the same — studies do support the impact of stereotype threat. Because the initial 1999 research and more than 10 subsequent studies found that telling men about gender differences didn’t affect their math performance, Steele said, researchers stopped including men in their studies and concentrated on women.
“Once research shows a result multiple times,” Steele explains, “you don’t have to keep showing it in every new experiment. Research literature matures and moves on to the next most important question.”
“If you just want to know whether removing stereotypes improves women’s performance,” adds Spencer, “you don’t have to compare them to men.”
However, Geary says the original findings that included men have not been “solidly replicated.” Plus, he added, “no one has tested the possibility that if you tell men they won’t do well on [a] math test, their performance drops.” Participants in those studies later said they assumed, however, that “gender differences” meant that men did better than women. As Virginia Woolf wrote in A Room of One’s Own (and Steele and Spencer quoted in their study),“There was an enormous body of masculine opinion to the effect that nothing could be expected of women intellectually.”
Do biological differences between men’s and women’s brains help account for the gender gap in higher-level math, a conclusion Steele believes that Geary and Stoet wish to bolster? “I have argued that the differences in some (not all) areas of math may be related to more basic differences between men and women,” said Geary, “but, at the same time, I have argued that the [achievement] gap can nevertheless be closed with targeted intervention (e.g. teaching girls and women to spatially diagram math word problems.)”
Focusing on stereotype threat will prevent such interventions, he argued.
Innate differences between men and women exist not only in math, Geary said. “There are clearly many biological differences between men and women,” he said, “and math is probably one of those domains in which the biology is of lower importance.”
Steele and Spencer, along with Diane Quinn of the University of Connecticut, have crafted a response to Stoet and Geary’s paper and submitted it to the Review of General Psychology.