Sexism, Testing, and “Academic Ability”
When I was teaching my undergraduate course on evolutionary psychology, my approach to testing and assessment was unique. You can read about that philosophy in more detail here,
but the gist of my method was specifically avoiding multiple-choice
formats in favor of short-essay questions with unlimited revision
ability on the part of the students. I favored this exam format for a
number of reasons, chief among which was that (a) I didn’t feel multiple
choice tests were very good at assessing how well students understood
the material (memorization and good guessing does not equal understanding),
and (b) I didn’t really care about grading my students as much as I
cared about getting them to learn the material. If they didn’t grasp it
properly on their first try (and very few students do), I wanted them to
have the ability and motivation
to continue engaging with it until they did get it right (which most
eventually did; the class average for each exam began around a 70 and
rose to a 90). For the purposes of today’s discussion, the important
point here is that my exams were a bit more cognitively challenging than
is usual and, according to a new paper, that means I had
unintentionally biased my exams in ways that disfavor “historically
underserved groups” like women and the poor.
What caught my eye about this particular paper, however, was the initial press release that accompanied it. Specifically, the authors were quoted as saying something I found, well, a bit queer:
Cognitive challenging test were indeed, well, more challenging. A statistically-average male student, for instance, would be expected to do about 12% worse on the most challenging test in their sample, relative to the easiest one. This effect was not the same between genders, however. Again, using statistically-average men and women, when the tests were the least cognitively challenging, there was effectively no performance gap (about a 1.7% expected difference favoring men); however, when the tests were the most cognitively challenging, that expected gap rose to an astonishing expected…3.2% difference. So, while the gender difference just about nominally doubled, in terms of really mattering in any practical sense of the word, its size was such that it likely wouldn’t be noticed unless one was really looking for it. A similar pattern was discovered for SES: when the tests were easy, there was effectively no difference between those low or high in SES (1.3% favoring those higher); however, when the tests were about maximally challenging, this expected difference rose to about 3.5%.
There’s a lot to say about these results and how they’re framed
within the paper. First, as I mentioned, they truly are minor
differences; there are very few cases were a 1-3% difference in test
scores is going to make-or-break a student, so I don’t think there’s any
real reason to be concerned or to adjust the tests; not practically,
anyway.
However, there are larger, theoretical issues looming in the paper. One of these is that the authors use the phrase “controlled for academic ability” so often that a reader might actually come to believe that’s what they did from simple repetition. The problem here, of course, is that the authors did not control for that; they controlled for GPA. Unfortunately for Wright et al’s (2016) presentation, those two things are not synonyms. As I said before, it is strange to say that academic ability is the same because one set of tests (incoming GPA) says they are while another set does not. The former set of tests appear to be privileged for no sound reason. Because of that unwarranted interpretation, the authors lose (or rather, purposefully remove) the ability to talk about how these gaps might be due to some performance difference. This is a useful rhetorical move if one is interested in doing advocacy – as it implies the gap is unfair and ought to be fixed somehow – but not if one is seeking the truth of the matter.
Another rather large issue in the paper is that, as far as I could tell, the authors predicted they would find these effects without ever really providing an explanation as for how or why that prediction arose. That is, what drove their expectation that men would outperform women and the rich outperform the poor? This ends up being something of a problem because, at the end of the paper, the authors do float a few possible (untested) explanations for their findings. The first of these is stereotype threat: the idea that certain groups of people will do poorly on tests because of some negative stereotype about their performance. This is a poor fit for the data for two reasons: first, while Wright et al (2016) claim that stereotype is “well-documented”, it actually fails to replicate (on top of not making much theoretical sense). Second, even if it was a real thing, stereotype threat, as it typically studied, requires that one’s sex be made salient prior to the test. As I encountered a total of zero tests during my entire college experience that made my gender salient, much less my SES, I can only assume that the tests in question didn’t do it either. In order for stereotype threat to work as an explanation, then, women and the poor would need to be under relative constant stereotype threat. In turn, this would make documenting and student stereotype threat in the first place rather difficult, as you could never have a condition where your subjects were not experiencing it. In short, then, stereotype threat seems like a bad fit.
The other explanations that are put forth for this gender difference are the possibility that women and poor students have more fixed views of intelligence instead of growth mindsets, so they withdraw from the material when challenged rather than improve (i.e., “we need to change their mindsets to close this daunting 2% gap), or the possibility that the test questions themselves are written in ways that subtly bias people’s ability to think about them (the example the authors raise is that a question written about applying some concept to sports might favor men, relative to women, as men tend to enjoy sports more). Given that the authors did have access to the test questions, it seems that they could have examined that latter possibility in at least some detail (minimally, perhaps, by looking at whether tests written by female instructors resulted in different outcomes than those written by male ones, or by examining the content of the questions themselves to see if women did worse on gendered ones). Why they didn’t conduct such analyses, I can’t say.
In summary, these very minor average differences that were uncovered
could easily be chalked up – very simply – to GPA not being a full
measure of a student’s academic ability. In fact, if the tests
determining freshman GPA aren’t the most cognitively challenging (as one
might well expect, given that students would have been taking mostly
general introductory courses with large class sizes), then this might
make the students appear to be more similar in ability than they
actually were. The matter can be thought of using this
stereotypically-male example (that will assuredly hinder women’s ability
to think about it): imagine I tested people in a room with weights
ranging from 1-15 pounds and asked them to curl each one time. This
would give me a poor sense for any underlying differences in strength
because the range of ability tested was restricted. Provided I were to
ask them to do the same with weights ranging from 1-100 pounds the next
week, I might conclude that it’s something about the weights – and not
people’s abilities – when it came to figuring out why differences
suddenly emerged (since I mistakenly believe I already controlled for
their abilities the first time).
Now I don’t know if something like that is actually responsible, but if the tests determining freshman GPA were tapping the same kinds of abilities to the same degrees as those in the biology courses studied, then controlling for GPA should have taken care of that potential issue. Since controlling for GPA did not, I feel safe assuming there being some difference in the tests in terms of what abilities they’re measuring.
References: Wright, C., Eddy, S., Wenderoth, M., Abshire, E., Blankenbiller, M., & Brownell, S. (2016). Cognitive difficulty and format of exams predicts gender and socioeconomic gaps in exam performance of students in introductory biology courses. Life Science Education, 15.
Oops…
Source: Flickr/getradwithbrad
“At first glance, one might assume the differences in exam performance are based on academic ability. However, we controlled for this in our study by including the students’ incoming grade point averages in our analysis,”So the authors appear to believe that a gap in performance on academic tests arises independent of academic abilities (whichever those entail). This raised the immediate question in my mind of how one knows that abilities are the same unless one has a method of testing them. It seems a bit strange to say that abilities are the same on the basis of one set of tests (those that provided incoming GPAs), but then to continue to suggest that abilities are the same when a different set of tests provides a contrary result. In the interests of settling my curiosity, I tracked the paper down to see what was actually reported; after all, these little news blurbs frequently get the details wrong. Unfortunately, this one appeared to capture the author’s views accurately.
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So let’s start by briefly reviewing what the authors were looking at. The paper, by Wright et al (2016),
is based on data collected from three-years worth of three introductory
biology courses spanning 26 different instructors, approximately 5,000
students, and 87 different exams.Without going into too much unnecessary
detail, the tests were assessed by independent raters for how
cognitively challenging they were, their format, and the students were
classified according to their gender and socio-economic status (SES; as measured by whether they qualified for a financial aid program).
In order to attempt and control for academic ability, Wright et al
(2016) also looked at the freshman-year GPA of the students coming into
the biology classes (based on approximately 45 credits, we are told).
Because the authors controlled for incoming GPA, they hope to persuade the reader of the following:This implies that, by at least one measure, these students have equal academic ability, and if they have differential outcomes on exams, then factors other than ability are likely influencing their performance.Now one could argue that there’s more to academic ability than is captured by a GPA – which is precisely why I will do so in a minute – but let’s continue on with what the authors found first.
Cognitive challenging test were indeed, well, more challenging. A statistically-average male student, for instance, would be expected to do about 12% worse on the most challenging test in their sample, relative to the easiest one. This effect was not the same between genders, however. Again, using statistically-average men and women, when the tests were the least cognitively challenging, there was effectively no performance gap (about a 1.7% expected difference favoring men); however, when the tests were the most cognitively challenging, that expected gap rose to an astonishing expected…3.2% difference. So, while the gender difference just about nominally doubled, in terms of really mattering in any practical sense of the word, its size was such that it likely wouldn’t be noticed unless one was really looking for it. A similar pattern was discovered for SES: when the tests were easy, there was effectively no difference between those low or high in SES (1.3% favoring those higher); however, when the tests were about maximally challenging, this expected difference rose to about 3.5%.
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Useful for both spotting statistical blips and burning insects
Source: Flickr/Landon
However, there are larger, theoretical issues looming in the paper. One of these is that the authors use the phrase “controlled for academic ability” so often that a reader might actually come to believe that’s what they did from simple repetition. The problem here, of course, is that the authors did not control for that; they controlled for GPA. Unfortunately for Wright et al’s (2016) presentation, those two things are not synonyms. As I said before, it is strange to say that academic ability is the same because one set of tests (incoming GPA) says they are while another set does not. The former set of tests appear to be privileged for no sound reason. Because of that unwarranted interpretation, the authors lose (or rather, purposefully remove) the ability to talk about how these gaps might be due to some performance difference. This is a useful rhetorical move if one is interested in doing advocacy – as it implies the gap is unfair and ought to be fixed somehow – but not if one is seeking the truth of the matter.
Another rather large issue in the paper is that, as far as I could tell, the authors predicted they would find these effects without ever really providing an explanation as for how or why that prediction arose. That is, what drove their expectation that men would outperform women and the rich outperform the poor? This ends up being something of a problem because, at the end of the paper, the authors do float a few possible (untested) explanations for their findings. The first of these is stereotype threat: the idea that certain groups of people will do poorly on tests because of some negative stereotype about their performance. This is a poor fit for the data for two reasons: first, while Wright et al (2016) claim that stereotype is “well-documented”, it actually fails to replicate (on top of not making much theoretical sense). Second, even if it was a real thing, stereotype threat, as it typically studied, requires that one’s sex be made salient prior to the test. As I encountered a total of zero tests during my entire college experience that made my gender salient, much less my SES, I can only assume that the tests in question didn’t do it either. In order for stereotype threat to work as an explanation, then, women and the poor would need to be under relative constant stereotype threat. In turn, this would make documenting and student stereotype threat in the first place rather difficult, as you could never have a condition where your subjects were not experiencing it. In short, then, stereotype threat seems like a bad fit.
The other explanations that are put forth for this gender difference are the possibility that women and poor students have more fixed views of intelligence instead of growth mindsets, so they withdraw from the material when challenged rather than improve (i.e., “we need to change their mindsets to close this daunting 2% gap), or the possibility that the test questions themselves are written in ways that subtly bias people’s ability to think about them (the example the authors raise is that a question written about applying some concept to sports might favor men, relative to women, as men tend to enjoy sports more). Given that the authors did have access to the test questions, it seems that they could have examined that latter possibility in at least some detail (minimally, perhaps, by looking at whether tests written by female instructors resulted in different outcomes than those written by male ones, or by examining the content of the questions themselves to see if women did worse on gendered ones). Why they didn’t conduct such analyses, I can’t say.
Maybe it was too much work and they lacked a growth mindset
Source: Flickr/Stephen Downes
Now I don’t know if something like that is actually responsible, but if the tests determining freshman GPA were tapping the same kinds of abilities to the same degrees as those in the biology courses studied, then controlling for GPA should have taken care of that potential issue. Since controlling for GPA did not, I feel safe assuming there being some difference in the tests in terms of what abilities they’re measuring.
References: Wright, C., Eddy, S., Wenderoth, M., Abshire, E., Blankenbiller, M., & Brownell, S. (2016). Cognitive difficulty and format of exams predicts gender and socioeconomic gaps in exam performance of students in introductory biology courses. Life Science Education, 15.
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