My last couple of posts have focused primarily on the topic of group differences and on understanding how they might come to exist. Some of the most commonly-advanced explanations for these differences concern discrimination – explicit or implicit – that serves to keep otherwise interested and qualified people out of arenas they would like to compete in. For example, few men might want to be nurses because male nurses aren’t considered for positions even if they’re qualified because of a social stigma against men in that area. If that was the explanation for these group differences, it would represent a wealth of untapped social value achievable by reducing or removing those discriminatory boundaries. On the other hand, if discrimination is not the cause of those differences, a lot of time and energy could be invested into chasing down a boogeyman without yielding much in the way of value for anyone.

Unfortunately, as we saw last time (and other times), research seeking to test these explanations can be designed or interpreted in ways that make them resilient to falsification. If the hypothesized effect attributable to discrimination is observed, it is counted as evidence consistent with the explanation; when the effect isn’t observed, however, it is not counted as evidence against the proposal. They are sure the discrimination is there; they just didn’t dig deep enough to find it. This practice can be maintained effectively in many domains because of the fuzzy nature of performance within them. That is, it’s not always clear which person would make a better manager or professor when it comes time to make a hiring decision or assess performance, so different rates of hiring or promotions cannot be clearly related to different behavior.

And if the quality of your work can’t be assessed, it also means you can never be said to fail at your job

One way of working that fuzziness out of the equation is to turn towards domains where more objective measures of performance can be obtained. While it might be difficult to say for certain that one person would make a superior manager to another – especially when they are closely matched in skills – it is quite a lot easier to see if they can complete a task with objective performance criteria, such as winning in a video game or performing pull-ups. In realms of objective performance, it doesn’t matter if people like you or not; your abilities are being tested against reality. Accordingly, domains with more objective performance criteria make for appealing research tools when it comes to assessing and understanding group differences.

On that note, Moxley, Ericsson, & Tuffiash (2017) report some interesting information concerning the board game SCRABBLE. For the handful of you who might not know what SCRABBLE is, it’s a game where each player randomly selects a number of tiles with letters on them, then uses those tiles to spell words: the larger the word or the harder the letters are to utilize, the more points the player receives. The player with the most points after the tiles have been used up wins. As it turns out, men tend to be over-represented in the upper tiers of SCRABBLE performance. Within the highest-performing competitive SCRABBLE divisions, 86% of the players are male, while only 31% of the players in the lowest-performing divisions are. This patterns holds even though most of the competitive SCRABBLE players are women. Indeed, when regular people are asked about whether they would expect more male or female SCRABBLE champions, the intuition seems to be that women should be more common (despite, for context, all 10 of the last world champions having been male).

How is that sex difference in performance to be explained? In this instance, discrimination looks to be an odd explanation: competitive SCRABBLE tournaments do not present clear barriers to entry and women appear to be at least as interested – if not more so – in SCRABBLE than men are, as inferred from participation rates. Moreover, people even seem to expect women would do better than men in that field, so an explanation along the lines of stereotype threat doesn’t work well either. According to the research of Moxley, Ericsson, & Tuffiash (2017), the explanation for most of that sex difference in performance does, in fact, relate to varying male and female interests, but perhaps not those directed at playing SCRABBLE itself. While I won’t discuss every part of the studies they undertook, I wanted to highlight some general points of this research because of how well it can highlight the difficulty and nuance in understanding sex differences and their relation to performance within a given field.

Even this vicious field of battle

The general methodology employed by the researchers involved surveying participants at National SCRABBLE competitions in 2004 and 2008 about their overall level of practice each year, both in terms of time spent studying alone and practicing seriously with others. These responses were then examined in the context of the player’s competitive SCRABBLE rating. The first study turned up several noteworthy relationships. As expected, women tended to have lower ratings than men (d = -0.74). However, it was also found that different types of SCRABBLE practice had varying impacts on player ratings. In this case, studying vocabulary had a negative impact on performance, while time spent analyzing past games and doing anagrams had a positive impact. This means that just asking people about how much they practiced SCRABBLE is not enough of a fine-tuned question for good predictive accuracy concerning performance. In this case, the practice questions asked about were unable to account for the entirety of the gender difference in performance, but they did reduce it somewhat.

This led the researchers to ask more detailed questions about SCRABBLE players’ practice in their second study. As before, women tended to have lower ratings than men (d = -0.69), but once the more refined questions about practice and experience were accounted for, there was no longer a direct effect of gender on rating. This would suggest that the performance advantage men had in SCRABBLE can be largely attributed to their spending more time engaged in solitary practice that benefits performance, while women tended to spend more time playing SCRABBLE with others; a behavior which did not yield comparable performance benefits.

The final step in this analysis was to figure out why men and women spent different amounts of time engaged in the types of practice they did. To do so, the players’ responses about how relevant, enjoyable, and effortful various types of practice felt were assessed. In order, the players felt tournament experience was the most important for improving their skills, then playing SCRABBLE itself, followed last by other types of word games. On that front, perceptions weren’t quite accurate. A similar pattern emerged in terms of which activities were rated as most enjoyable. However, there was a sex difference in that women rated playing SCRABBLE outside of tournaments as more enjoyable than men, and men rated SCRABBLE-specific practice (like anagrams) as more enjoyable than women.

Taken together, men tended to find the most-effective practice methods more enjoyable than women, and so engaged in them more. This differential involvement in effective practice in turn explained the sex difference in player rankings. Nothing too shocking, but reality often isn’t.

Published in the journal of, “I’m sorry; did you say something?”

What we see in this research is an appreciable sex difference in performance resulting from varying male and female interests, but those interests themselves are not necessarily the most obvious targets for investigation. If you were to just ask men and women whether they were interested in SCRABBLE, you might find that women had a higher average interest. If you were to just ask about how much time they spent practicing, you might not observe a sex difference capable of explaining the differences in performance. It wouldn’t be until you asked specifically about their interests in particular types of practice and understood how those related to eventual performance that you end up with a better picture of that performance gap. In this case, it seems to be the case that the sex difference is largely the product of men being more interested in specific types of practice that are ultimately more productive when it comes to improving performance. The corollary point to that is that if you were trying to reduce the male-female performance gap in SCRABBLE, if your explanation for that gap was that women are being discriminated against and so sought to reduce discrimination in the field, you’d probably do nothing to help even out the scores (though you might achieve some social maligning). 

Thankfully this kind of analysis can be reasonably undertaken in a realm where performance can be objectively assessed. If you were to think about trying this same analysis with respect to, say, the relative distribution of men and women in STEM fields, you’re in for a much rockier experience where it’s not clear how certain interests relate to ultimate performance.

References: Moxley, J., Ericsson, A., & Tuffiash, M. (2017). Gender differences in SCRABBLE performance and associated engagement in purposeful practice activities. Psychological Research, DOI 10.1007/s00426-017-0905-3

One day, three young children are talking about what they want to be when they get older. The first friend says, “I love animals, so I want to become a veterinarian.” The second says, “I love computers, so I want to become a programmer.” The third says, “I love making people laugh, so I want to become a psychology researcher.” Luckily for all these children, they all end up living a life that affords them the opportunity to pursue their desires, and each ends up working happily in the career of their choice for their entire adult life.

The first question I’d like to consider is whether any of those children made choices that were problematic. For instance, should the first child have decided to help animals, or perhaps should they have put their own interests aside and pursued another line of work because of their sex and the current sex-ratio of men and women in that field? Would your answer change if you found out the sex of each of the children in question? Answer as if the second child was a boy, then think about whether your answer would change if you found out she was a girl.

Well if you wanted to be a vet, you should have been born a boy

This hypothetical example should, hopefully, highlight a fact that some people seem to lose track of from time to time: broad demographic groups are not entities themselves; only made up of their individual members. Once one starts talking about how gender inequality in professions ought to be reduced – such that you see a greater representation of 50/50 men and women across a greater number of fields – you are, by default, talking about how some people need to start making choices less in line with their interests, skills, and desires to reach that parity. This can end up yielding strange outcomes, such as a gender studies major telling a literature major she should have gone into math instead. 

Speaking of which, a paper I wanted to examine today (Riegle-Crumb, King, & Moore, 2016) begins laying on the idea of gender inequality across majors rather thick. Unless I misread their meaning, they seem to think that gender segregation in college majors ought to be disrupted and, accordingly, sought to understand what happens to men and women who make non-normative choices in selecting a college major, relative to their more normative peers. Specifically, they set out to examine what happens to men who major both in male- and female-dominated fields: are they likely to persist in their chosen field of study in the same or different percentages? The same question was asked of women as well. Putting that into a quick example, you might consider how likely a man who initially majors in nursing is to switch or stay in his program, relative to one who majors in computer science. Similarly, you might think about the fate of a woman who majors in physics, compared to one who majors in psychology.

The authors expected that women would be more likely to drop out of male-dominated fields because they encounter a “chilly” social climate there and face stereotype threat, compared to their peers in female-dominated fields. By contrast, men were expected to drop out of female-dominated fields more often as they begin to confront the prospect of earning less money in the future and/or lose social status on account of emasculation brought on by their major (whether perceived or real).

To test these predictions, Riegle-Crumb, King, & Moore (2016) examined a nationally-representative sample of approximately 3,700 college students who had completed their degree. These students had been studied longitudinally, interviewed at the end of their first year of college in 2004, then again in 2006 and 2009. A gender atypical major was coded as one in which the opposite sex compromised 70% or more of the major. In the sample being examined, 14% of the males selected a gender-atypical field, while 4% of women did likewise. While this isn’t noted explicitly, I suspect some of that difference might have to do with the relative size of certain majors. For instance, psychology is one of the most popular majors in the US, but also happened to fall under the female-dominated category. That would naturally yield more men than women choosing a gender atypical major if the pattern continued into other fields.

Can’t beat that kind of ratio in the dating pool, though

Moving on to what was found, the researchers were trying to predict whether people would switch majors or not. The initial analysis found that men in male-typical majors switched about 39% of the time, compared to the 63% of men who switched from atypical majors. So the men in atypical fields were more likely to switch. There was a different story for the women, however: those in female-typical majors switched 46% of the time, compared to 41% who switched in atypical fields. The latter difference was neither statistically or practically significant. Unsurprisingly, for both men and women, those most likely to switch had lower GPAs than those who stayed, suggesting switching was due, in part, to performance.

When formally examined with a number of control variables (for social background and academic performance) included in the model, men in gender atypical fields were about 2.6 times as likely to switch majors, relative to those in male-dominated ones. The same analysis run for women found that those in atypical majors were about 0.8 times as likely to switch majors as those in female-dominated ones. Again, this difference wasn’t statistically significant. Nominally, however, women in atypical fields were more likely to stay put.

What do the authors make of this finding? Though they note correctly that their analysis says nothing of the reasons for the switch, they view the greater male-atypical pattern of switching as consistent with their expectations. I think this is probably close to the truth: as a greater proportion of a man’s future success is determined by his ability to provision mates and his social status, we might expect that men tend to migrate from majors with a lower future financial payoff to those that have a larger one. Placing that into a personal example, I might have wanted to be a musician, but the odds of landing a job as a respected rockstar seemed slim indeed. Better that I got a degree in something capable of paying the bills consistently if I care about money.

By contrast, the authors also correctly note that they don’t find evidence consistent with their prediction that women in gender-atypical fields would switch more often. This does not, however, cause them to abandon the justifications for their prediction. As far as I can tell, they still believe that factors like a chilly climate and stereotype threat are pushing women out of those majors; they just supplement that expectation by adding on that a number of factors (like the aforementioned financial ones) might be keeping them in, and the latter factors are either more common or influential (though that certainly makes you wonder why women tend to choose lower-paying fields in greater numbers the first place).

Certainly worth a 20-year career in a field you hate

This strikes me as kind of a fool-proof strategy for maintaining a belief in the prospect of nefarious social forces doing women harm. To demonstrate why, I’d like to take this moment to think about what people’s reactions to these findings might have been if the patterns for men and women were reversed. If it turned out that women in male-dominated majors were more likely to switch than their peers in female-dominated majors, would there have been calls to address the clear sexism causally responsible for that pattern? I suspect that answer is yes, judging from reactions I’ve seen in the past. So, if that result was found, the authors could point a finger at the assumed culprits. However, even when that result was not found, they can just tack on other assumptions (women remain in this major for the money) that allows the initial hypothesis of discrimination to be maintained in full force. Indeed, they end their paper by claiming, “Gender segregation in fields of study and related occupations severely constrains the life choices and chances of both women and men,” demonstrating a full commitment to being unphased by their results.

In other words, there doesn’t seem to be a pattern of data that could have been observed capable of falsifying the initial reasons these expectations were formed. Even nominally contradictory data appears to have been assimilated into their view immediately. Now I’m not going to say it’s impossible that there are large, sexist forces at work trying to push women out of gender atypical fields that are being outweighed by other forces pulling in the opposite direction; that is something that could, in theory, be happening. What I will say is that granting that possibility makes the current work a poor test of the original hypotheses, since no data could prove it wrong. If you aren’t conducting research capable of falsifying your ideas – asking yourself, “what data could prove me wrong?” – then you aren’t engaged in rigorous science. 

References: Riegle-Crumb, C., King, B., & Moore, C. (2016). Do they stay or do they go? The switching decisions of individuals who enter gender atypical college majors. Sex Roles, 74, 436-449.