Understanding Sex In Advertising

When people post videos on YouTube, one major point of interest for content creators and aggregators is to capture as much attention as possible. Your video is adrift in a sea of information and you’re trying to get as many eyes/clicks on your work as possible. In that realm, first impressions are all important: you want your video to have an attention-grabbing thumbnail image, as that will likely be the only thing viewers see before they actually click (or don’t) on it. So how do people go about capturing attention in that realm? One popular method is to ensure their thumbnail has a very emotive expression on it; a face of shock, embarrassment, stress, or any similar emotion. That’s certainly one way of attracting attention: trying to convince people there is something worth looking at, not unlike articles titled along the lines of five shocking tips for a better sex life (and number 3 will blow your mind!). Speaking of sex, that’s another popular method of grabbing attention: it’s fairly common for video thumbnails to feature people or body parts in various stages of undress. Not much will pull eyes towards a video like the promise of sex (and if you’re feeling an urge to click on that link, you’ll have experienced exactly what I’m talking about).

Case in point: most of that content is unrelated to the featured women

If sex happens to be attention grabbing, the natural question arises concerning what you might do with that attention once you have it. Much of the time, that answer will involve selling some good or service. In other words, sex is used as a form of advertising to try and sell things. “If you enjoyed that picture of a woman wearing a thong, you’ll surely love our reasonably-costed laptops!”. Something along those lines, anyway. Provided that’s your goal, lots of questions naturally start to crop up: How effective is sex at these goals? Does it capture attention well? Does it help people notice and remember your product or brand? Are those who viewed your sexy advert more likely to buy the product you’re selling? How do other factors – the sex of the person viewing the ad – contribute to your success in these realms?

These are some of the questions examined in a recent meta-analysis by Wirtz, Sparks, & Zimbres (2017). The researchers searched the literature and found about 80 studies, representing about 18,000 participants. They sought to find out what effects featuring sexually provocative material had, on average (defined in terms of style of dress, sexual behavior, innuendo, or sexual embeds, which is where hidden messages or images are placed within the ad, like the word “sex” added somewhere to the picture, which is something people apparently think is a good idea sometimes). These ads had to have been compared against a comparable, non-sexual ad for the same product to be included in the analysis to determine which was more effective.

The effectiveness of these ads were assessed across a number of domains as well, including ad recognition (in aided and unaided contexts), whether the brand being advertised in the ad could be recalled (i.e., were people paying attention to just the sex, or did they remember the product?), the positive or negative response people had to the ad, what people thought about the brand being advertised with sex, and whether the ad actually got them interested in purchasing the product (does sex sell?).

Finally, a number of potentially moderating factors that might influence these effects were considered. The first of these was gender: did these ads have different impacts on men and women? Others factors included the gender of the model used in the advertisement, the date the article was published (to see if attitudes shifted over time), the sample used (college students or not), and – most interestingly – product/ad congruity: did the type of product being advertised matter when it came to whether sex was effective? Perhaps sex might help sell a product like sun-tan lotion (as the beach might be a good place to pick up mates), but be much less effective for selling, say, laptops.

Maybe even political views

In terms of capturing attention, sex works. Of the 20 effects looking at the recall for ads, the average size was d = .38. Interesting, this effect was slightly larger for the congruent ads (d = .45), but completely reversed for the incongruent ones (d = -.45). Sex was good at getting people to remember ads selling a sex-related product, but not just generally useful. That said, they seemed better at getting people to remember just the ads. When the researchers turned to the matter of whether the brands within the ads were more likely to be recalled, the 31 effects looking at brand recognition turned out to barely break zero (d = .09). While sex might be attention-grabbing, it didn’t seem especially good at getting people to remember the objects being sold.

Regarding people’s attitudes towards the ads, sex seems like something of a wash (d = -.07). Digging a little deeper revealed a more nuanced pictured of these reactions, though: while sexual ads seemed to be a modest hit with the men (d = .27), they had the opposite effect on women (d = -.38). Women seemed to dislike the ads modestly more than men liked them, as sexual strategies theory would suggest (for the record, the type of model being depicted didn’t make much of a difference. In order, people liked males models the least (d = -.28), then female models (d = -.20), and couples were mildly positive, d = .08).

Curiously, both the men and women seemed to be agreement regarding their stance towards brands that used sex to sell things: negative, on the whole (d – =.22). For women, this makes some intuitive sense: they didn’t see to be a fan of the sexual ads, so they weren’t exactly feeling too ingratiated towards the brand itself. But why were the men negatively inclined towards the brand if they were favorably inclined towards the ads? I can only speculate on that front, but I assume it would have something to do with their inevitable disappointment: either that the brands were promising on sex the male customers likely knew they couldn’t deliver on, or perhaps the men simply wanted to enjoy the sex part and the brand itself ended up getting in their way. I can’t imagine men would be too happy with their porn time being interrupted by an ad for toilet paper or fruit snacks mid-video.

Finally, turning the matter of purchase intentions – whether the ads encouraged people to want to buy the product or not – it seemed that sex didn’t really sell, but it didn’t really seem to hurt, either (d = .01). One interesting exception in that realm was that sex appeals were actually less likely to get people to buy a product when the product being sold was incongruent with the sexual appeal (d = -.24). Putting that into a simple example, the phrase “strip club buffet” probably doesn’t wet many appetites, and wouldn’t be a strong selling point for such a venue. Sex can be something of a disease vector, and associating your food with that might illicit more than a bit of disgust.

“Oh good, I was starving. This seems like as good a place as any”

As I’ve noted before, context matching matters in advertising. If you’re looking to sell people something that highlights their individuality, then doing so in a mating context works better than in a context of fear (as animals aren’t exactly aiming to look distinct when predators are nearby). The same seems to hold for using sex. While it might be useful for getting eyes on your advertisement, sex is by no mean guaranteed to ensure that people like what they see once you have their attention. In that regard, sex – like any other advertising tool – needs to be used selectively, targeting the correct audience in the correct context if it’s going to succeed at increasing people’s interest in buying. Sex in general doesn’t sell. However, it might prove more effective for those with more promiscuous attitudes than those with more monogamous ones; it might prove useful if advertising a product related to sex or mating, but not useful for selling domain names (like the old GoDaddy commercials; coincidentally, GoDaddy was also the brand I used to register this site); it might work better if you associate your product with things that lead to sex (like status), rather than sex itself. These are all avenues worth pursuing further to see when, where, and why sex works or fails.

That said, it is still possible that sex might prove useful, even in some inappropriate contexts. Consider the following hypothetical example: people will consider buying a product only after they have seen an advertisement for it. Advertisement X isn’t sexual, but when paired with the product will increase people’s intentions to buy it by 10%. However, it will also not really get noticed by many people, as the content is bland. By contrast, advertisement Y is sexual, will decrease people’s intentions to buy a product by 10%, but will also get four-times as many eyes on it. The latter ad might well be more successful, as it will capture the eye of more potential customers that may still buy the product despite the inappropriate use of sexWhile targeting advertisements might be more effective, the attention model of advertising shouldn’t be ruled out entirely, especially if targeting advertising would prove too cumbersome.

References: Wirtz, J., Sparks, J., & Zimbres, T. (2017). The effect of exposure to sexual appeals in advertisements on memory, attitude, and purchase intention: A meta-analytic review. International Journal of Advertising, https://doi.org/10.1080/02650487.2017.1334996


Mistreated Children Misbehaving

None of us are conceived or born as full adults; we all need to grow and develop from single cells to fully-formed adults. Unfortunately – for the sake of development, anyway – the future world you will find yourself in is not always predictable, which makes development a tricky matter at times. While there are often regularities in the broader environment (such as the presence or absence of sunlight, for instance), not every individual will inhabit the same environment or, more precisely, the same place in their environment. Consider two adult males, one of whom is six-feet tall and 230 pounds of muscle, and the other being five-feet tall and 110 pounds. While the dichotomy here is stark, it serves to make a simple point: if both of these males developed in a psychological manner that led them to pursue precisely the same strategies in life – in this case, say, one involving aggressive contests for access to females – it is quite likely that the weaker male will lose out to the stronger one most (if not all) of the time. As such, in order to be more-consistently adaptive, development must be something of a fluid process that helps tailor an individual’s psychology to the unique positions they find themselves in within a particular environment. Thus, if an organism is able to use some cues within their environment to predict their likely place in it in the future (in this case, whether they would grow large or small), their development could be altered to encourage their pursuit of alternate routes to eventual reproductive success. 

Because pretending you’re cut out for that kind of life will only make it worse

Let’s take that initial example and adapt it to a new context: rather than trying to predict whether one will grow up weak or strong, a child is trying to predict the probability of receiving parental investment in the future. If parental investment is unlikely to be forthcoming, children may need to take a different approach to their development to help secure the needed resources on their own, sometimes requiring their undertaking risky behaviors; by contrast, those children who are likely to receive consistent investment might be relatively less-inclined to take such risky and costly matters into their own hands, as the risk vs. reward calculations don’t favor such behavior. Placed in an understandable analogy, a child who estimates they won’t be receiving much investment from their parents might forgo a college education (and, indeed, even much of a high-school one) because they need to work to make ends meet. When you’re concerned about where your next meal is coming from there’s less time in your schedule for studying and taking out loans to not be working for four years. By contrast, the child from a richer family has the luxury of pursuing an education likely to produce greater future rewards because certain obstacles have been removed from their path.

Now obviously going to college is not something that humans have psychological adaptations for – it wasn’t a recurrent feature of our evolutionary history as a species – but there are cognitive systems we might expect to follow different developmental trajectories contingent on such estimations of one’s likely place in the environment; these could include systems judging the relative attractiveness of short- vs long-term rewards, willingness to take risks, pursuit of aggressive resolutions to conflicts, and so on. If the future is uncertain, saving for it makes less than taking a smaller reward in the present; if you lack social or financial support, being willing to fight to defend what little you do have might sound more appealing (as losing that little bit is more impactful when you won’t have anything left). The questions of interest thus becomes, “what cues in the environment might a developing child use to determine what their future will look like?” This brings us to the current paper by Abajobir et al (2016).

One potential cue might be your experiences with maltreatment while growing up, specifically at the hands of your caregivers. Though Abajobir et al (2016) don’t make the argument I’ve been sketching out explicitly, that seems to be the direction their research takes. They seem to reason (implicitly) that parental mistreatment should be a reliable cue to the future conditions you’re liable to encounter and, accordingly, one that children could use to alter their development. For instance, abusive or neglectful parents might lead to children adopting faster life history strategies involving risk-taking, delinquency, and violence themselves (or, if they’re going the maladaptive explanatory route, the failure of parents to provide supporting environments could in some way hinder development from proceeding as it usually would, in a similar fashion to not having enough food growing up might lead to one being shorter as an adult. I don’t know which line the authors would favor from their paper). That said, there is a healthy (and convincing) literature consistent with the hypothesis that parental behavior per se is not the cause of these developmental outcomes (Harris, 2009), but rather that it simply co-occurs with them. Specifically, abusive parents might be genetically different from non-abusive ones and those tendencies could get passed onto the children, accounting for the correlation. Alternatively, parents that maltreat their children might just happen to go together with children having peer groups growing up more prone to violence and delinquency themselves. Both are caused by other third variables.

Your personality usually can’t be blamed on them; you’re you all on your own

Whatever the nature of that correlation, Abajobir et al (2016) sought to use parental maltreatment from ages 0 to 14 as a predictor of later delinquent behaviors in the children by age 21. To do so, they used a prospective cohort of children and their mothers visiting a hospital between 1981-83. The cohort was then tracked for substantiated cases of child maltreatment reported to government agencies up to age 14, and at age 21 the children themselves were surveyed (the mothers being surveyed at several points throughout that time). Out of the 7200 initial participants, 3800 completed the 21-year follow up. At that follow up point, the children were asked questions concerning how often they did things like get excessively drunk, use recreational drugs, break the law, lie, cheat, steal, destroy the property of others, or fail to pay their debts. The mothers were also surveyed on matters concerning their age when they got pregnant, their arrest records, martial stability, and the amount of supervision they gave their children (all of these factors, unsurprisingly, predicting whether or not people continued on in the study for its full duration).

In total, of the 512 eventual cases of reported child maltreatment, only 172 remained in the sample at the 21-year follow up. As one might expect, maternal factors like her education status, arrest record, economic status, and unstable marriages all predicted increased likelihood of eventual child maltreatment. Further, of the 3800 participants, only 161 of them met the criteria for delinquency at 21 years. All of the previous maternal factors predicted delinquency as well: mothers who were arrested, got pregnant earlier, had unstable marriages, less education, and less money tended to produce more delinquent offspring. Adjusting for the maternal factors, however, it was reported that childhood maltreatment still predicted delinquency, but only for the male children. Specifically, maltreatment in males was associated with approximately 2-to-3.5 times as much delinquency as the non-maltreated males. For female offspring, there didn’t seem to be any notable correlation.

Now, as I mentioned, there are some genetic confounds here. It seems probable that parents who maltreat their children are, in some very real sense, different than parents who do not, and those tendencies can be inherited. This also doesn’t necessarily point a causal finger directly at parents, as it is also likely that maltreatment correlates with other social factors, like the peer group a child is liable to have or the neighborhoods they grow up in. The authors also mention that it is possible their measures of delinquency might not capture whatever effects childhood maltreatment (or its correlates) have on females, and that’s the point I wanted to wrap up discussing. To really put these findings on context, we would need to understand what adaptive role these delinquent behaviors – or rather the psychological mechanisms underlying them – have. For instance, frequent recreational drug use and problems fulfilling financial obligations might both signal that the person in question favors short-term rewards over long-term ones; frequent trouble with the law or destroying other people’s property could signal something about how the individual in question competes for social status. Maltreatment does seem to predict (even if it might not cause) different developmental courses, perhaps reflecting an active adjustment of development to deal with local environmental demands.

 The kids at school will all think you’re such a badass for this one

As we reviewed in the initial example, however, the same strategies will not always work equally well for every person. Those who are physically weaker are less likely to successfully enact aggressive strategies, all else being equal, for reasons which should be clear. Accordingly, we might expect that men and women show different patterns of delinquency to the extent they face unique adaptive problems. For instance, we might expect that females who find themselves in particularly hostile environments preferentially seek out male partners capable of enacting and defending against such aggression, as males tend to be more physically formidable (which is not to say that the women themselves might not be more physically aggressive as well). Any hypothetical shifts in mating preferences like these would not be captured by the present research particularly well, but it is nice to see the authors are at least thinking about what sex differences in patterns of delinquency might exist. It would be preferable if they were asking about those differences using this kind of a functional framework from the beginning, as that’s likely to yield more profitable insights and refine what questions get asked, but it’s good to see this kind of work all the same.

References: Abajobir, A., Kisely, S., Williams, G., Strathearnd, L., Clavarino, A., & Najman, J. (2016). Gender differences in delinquency at 21 years following childhood maltreatment: A birth cohort study. Personality & Individual Differences, 106, 95-103. 

Harris, J. (2009). The Nurture Assumption: Why Children Turn Out the Way They Do. Free Press.

Spinning Sexism Research On Accuracy

When it comes to research on sexism, there appear to be many parties interested in the notion that sexism ought to be reduced. This is a laudable goal, and one that I would support; I am very much in favor in treating people as individuals rather than representatives of their race, sex, or any other demographic characteristics. It is unfortunately, however, that this goal often gets side-tracked by an entirely different one: trying to get people to reduce the extent to which people view men and women as different. What I mean by this is that I have seen many attempts to combat sexism by trying to reduce the perception that men and women differ in terms of their psychology, personality, intelligence, and so on; it’s much more seldom that those same voices appear to convince people who inaccurately perceive sex differences as unusually small to adjust their estimate upwards. In other words, rather that championing accuracy is perceptions, there appears to be a more targeted effort for minimizing particular differences; while those are sometimes the same thing (sometimes people are wrong because they overestimate), they are often not (sometimes people are wrong because they underestimate), and when those goals do overlap, the minimization side tends to win out.

Just toss your perceptions in with the rest of the laundry; they’ll shrink

In my last post, I discussed some research by Zell et al (2016) primarily in the service of examining measures of sexism and the interpretation of the data they produce (which I recommend reading first). Today I wanted to give that paper a more in-depth look to illustrate this (perhaps unconscious) goal of trying to get people to view the sexes as more similar than they actually are. Zell et al (2016) begin their introduction by suggesting that most psychological differences between men and women are small, and the cases in which medium to large differences exist – like mating preferences and aggression – tend to be rare. David Schmitt has already put remarks like that into some context, and I highly recommend you read his post on the subject. In the event you can’t be bothered to do so at the moment, one of the most important takeaway points from his post is that even if the differences in any one domain tend to be small on average, when considered across all those domains simultaneously, those small differences can aggregate into much larger ones.

Moreover, the significance of a gender difference is not necessarily determined by its absolute size, either. This was a point Steven Pinker mentioned in a somewhat-recent debate with Elizabeth Spelke (and was touched on again in a recent talk by Jon Haidt at SUNY New Paltz). To summarize this point briefly, if you’re looking at a trait in two normally-distributed populations that are, on average, quite similar, the further from that average value you get, the most extreme the difference between populations become. Pinker makes the point clear in this example:

“…it’s obvious that distributions of height for men and women overlap: it’s not the case that all men are taller than all women. But while at five foot ten there are thirty men for every woman, at six feet there are two thousand men for every woman. Now, sex differences in cognition tend not to be so extreme, but the statistical phenomenon is the same.”

Not only are small sex differences sometimes important, then, (such as when you’re trying to hire people for a job who are in the top 1% of distribution for a trait like intelligence, speed, conscientiousness; you name it) but a large number of small effects (as well as some medium and large ones) can all add up to collectively represent some rather large differences (and that assumes you’re accounting for all relevant sex differences; not just a non-representative sample of them). With all this considered, the declaration at the beginning of Zell et al’s paper that most sex differences tend to be small strikes me less as a statement of empirical concern, but rather one that serves to set up the premise for the rest of their project: specifically, the researchers wanted to test whether people’s scores on the ambivalent sexism inventory predicted (a) the extent to which they perceive sex differences as being large and (b) the extent to which they are inaccurate in their perceptions. The prediction in this case was that people who scored high on their ostensible measures of sexism would be more likely to exaggerate sex differences and more likely to be wrong about their size overall (as an aside, I don’t think those sexism questions measure what the authors hope they do; see my last post).

Pictured: Something not even close to what was being assessed in this study

In their first study, Zell et al (2016) asked about 320 participants to estimate how large they think sex differences are between men and women (from 1-99) were for 48 traits and to answer 6 questions intended to measure their hostile and benevolent sexism (as another aside, I have no idea why those 48 traits in particular were selected). These answers were then averaged for each participant to create an overall score for how large they viewed the sex differences to be, and how high they scored on hostile and benevolent sexism. When the relevant factors were plugged into their regression, the results showed that those higher in hostile (ß = .19) and benevolent (ß = .29) sexism tended to perceive sex differences as larger, on average. When examined by gender, it was found that women (ß = .41) who were higher in benevolent sexism were more likely to perceive sex differences as large (but this was not true for men: ß = .11) and – though it was not significant – the reverse pattern held for hostile sexism, such that women high in hostile sexism were nominally less likely to perceive sex differences as large (ß = -.32).

The more interesting finding, at least as far as I’m concerned, is that in spite of those scoring higher on their sexism scores perceiving sex differences to be larger, they were not really more likely to be wrong about them. Specifically, those who scored higher on benevolent sexism were slightly less accurate (ß = -.20), just as women tended to be less accurate than men (ß = -.19); however, hostile sexism scores were unrelated to accuracy altogether (ß = .003), and no interactions with gender and sexism emerged. To put that in terms of the simple correlations, hostile and benevolent sexism correlated much better with the perceived size of sex differences (rs = .26 and .43, respectively) than they did with accuracy (rs = -.12 and -.22, with the former not being significant and the latter being rather small). Now since we’re dealing with two genders, two sexism scales, and relatively small effects, it is possible that some of these findings are a bit more likely to be statistical flukes; that does tend to happen as you keep slicing data up. Nevertheless, these results are discussed repeated within the context of their paper as representing exaggerations: those scoring higher on these sexism measures are said to exaggerate sex differences, which is odd on account of them not consistently getting them all that wrong.

This interpretation extends to their second study as well. In that experiment, about 230 participants were presented with two mock abstracts and told that only one of them represented an accurate summary of psychological research on sex differences. The accurate version, of course, was the one that said sex differences were small on average and therefore concluded that men and women are very similar to each other, whereas the bogus abstract concluded that gender differences are often large and therefore men and women are very different from one another. As I reviewed in the beginning of the post, small differences can often have meaningful impacts both individually and collectively, so the lines about how men and women are very similar to each other might not reflect an entirely accurate reading of the literature even if the part about small average sex differences did. This setup is already conflating the two statements (“average effect sizes on all these traits is small” and “men and women are very similar across the board”).

“Most of the components aren’t that different from modern cars, so they’re basically the same”

As before, those higher in hostile and benevolent sexism tended to say that the larger sex difference abstract more closely reflected their personal views (women tended to select the large-difference abstract 50.4% of the time compared to men’s 44.2% as well). Now because the authors view the large sex difference abstract as being the fabricated one, they conclude that those higher in those sexism measures are less accurate and more likely to exaggerate these views (they also make a remark that their sexism measures indicate which people “endorse sexist ideologies”; a determination it’s not at all cut out for making). In other words, the authors interpret this finding as those selecting the large-differences abstract to hold “empirically unsupported” views (which in a sort-of ironic sense means that, as the late George Carlin put it, “Men are better at it” when it comes to recognizing sex differences).

This is an interesting methodological trick they employ: since they failed to find much in the way of a correlation between sexism scores and accuracy in their first study (it existed sometimes, but was quite small across the board and certainly much smaller than the perception of size correlation), they created a coarser and altogether worse measure of accuracy in the second study and use that to support their views that believing men and women tend to be rather different is wrong instead. As the old saying goes, if at first you don’t succeed, change your measures until you do.

References: Zell, E., Strickhouser, J., Lane, T., & Teeter, S. (2016). Mars, Venus, or Earth? Sexism and the exaggeration of psychological gender differences. Sex Roles, 75, 287-300.

Research Tip: Ask About What You Want To Measure

Recently I served as a reviewer for a research article that had been submitted to a journal for publication. Without going into too much detail as to why, the authors of this paper wanted to control for people’s attitudes towards casual sex when conducting their analysis. They thought that it was possible people who were more sexually-permissive when it comes to infidelity might respond to certain scenarios differently than those who were less sexually-permissive. If you were the sensible type of researcher, you might do something like ask your participants to indicate on some scale as to how acceptable or unacceptable they think sexually infidelity is, then. The authors of this particular paper opted for a different, altogether stranger route: they noted that people’s attitudes towards infidelity correlate (imperfectly) with their political ideology (i.e., whether they consider themselves to be liberals or conservatives). So, rather than ask participants directly about how acceptable infidelity is (what they actually wanted to know), they asked participants about their political ideology and used that as a control instead.

 ”People who exercise get tired, so we measured how much people napped to assess physical fitness”

This example is by no means unique; psychology researchers frequently try to ask questions about topic X in the hopes of understanding something about topic Y. This can be acceptable at times, specifically when topic Y is unusually difficult – but not impossible – to study directly. After all, if topic Y is impossible to directly study, then one obviously cannot say that studying topic X tells you something about Y with much confidence, as you would have no way of assessing the relationship between X and Y to begin with. Assuming that the relationship between X and Y has been established and it is sufficiently strong and Y is unusually difficult to study directly, then there’s a good, practical case to be made for using X instead. When that is done, however, it should always be remembered that you aren’t actually studying what you’d like to study, so it’s important to not get carried away with the interpretation of your results.

This brings us nicely to the topic of research on sexism. When people hear the word “sexism” a couple things come to mind: someone who believes one sex is (or should be) – socially, morally, legally, psychologically, etc – inferior to the other, or worth less; someone who wouldn’t want to hire a member of one sex for a job (or intentionally pays them less if they did) strictly because of that variable regardless of their qualifications; someone who inherently dislikes members of one sex. While this list is by no means exhaustive, I suspect things like these are probably the prototypical examples of sexism; some kind of explicit, negative attitude about people because of their sex per se that directly translates into behavior. Despite this, people who research sexism don’t usually ask about such matters directly, as far as I’ve seen. To be clear, they easily could ask such questions assessing such attitudes in straightforward manners (in fact, they used to do just that with measures like the “Attitudes Towards Women Scale” in the 1970s), but they do not. As I understand it, the justification for not asking about such matters directly is because it has become more difficult to find people who actually express such views (Loo & Thorpe, 1998). As attitudes had already become markedly less sexist from 1972 to 1998, one can only guess at how much more change occurred from then to now. In short, it’s becoming rare to find blatant sexists anymore, especially if you’re asking college students.

Many researchers interpret that difficulty as being the result of people still holding sexist attitudes but either (a) are not willing express them publicly for fear of condemnation, or (b) are not consciously aware that they hold such views. As such, researchers like to ask about questions about “Modern Sexism” or “Ambivalent Sexism“; they maintain the word “sexism” in their scales, but they begin to ask about things which are not what people first think of when they hear the term. They no longer ask about explicitly sexist attitudes. Therein lies something of a problem, though: if what you really want to know is whether people hold particular sexist beliefs or attitudes, you need some way of assessing those attitudes directly in order to determine that other questions which don’t directly ask about that sexism will accurately reflect it. However, if such a method of assessing those beliefs accurately, directly, and easily does exist, then it seems altogether preferable to use that method instead. In short, just ask about the things you want to ask about. 

“We wanted to measure sugar content, so we assessed how much fruit the recipe called for”

If you continue on with using an alternate measure – like using the Ambivalent Sexism Inventory (ASI), rather than the Attitudes towards Women Scale – then you really should restrict your interpretations to things you’re actually asking about. As a quick example, let’s consider the ASI, which is made up of a hostile and benevolent sexism component. Zell et al (2016) summarize the scale as follows:

“Hostile sexism is an adversarial view of gender relations in which women are perceived as seeking control over men. Benevolent sexism is a subjectively positive view of gender relations in which women are perceived as pure creatures who ought to be protected, supported, and adored; as necessary companions to make a man complete; but as weak and therefore best relegated to traditional gender roles (e.g., homemaker).”

In other words, the benevolent scale measures the extent to which women are viewed as children: incapable of making their own decisions and, as such, in need of protection and provisioning by men. The hostile scale measures the extent to which men don’t trust women and view them as enemies. Glick & Fiske (1996) claim that  ”...hostile and benevolent sexism…combine notions of the exploited group’s lack of competence to exercise structural power with self-serving “benevolent” justifications.” However, not a single measure on either the hostile or benevolent sexism inventory actually asks about female competencies or whether women ought to be restricted socially. 

To make this explicit, let’s consider the questions Zell et al (2016) used to assess both components. In terms of hostile sexism, participants were asked to indicate their agreement with the following three statements:

  • Women seek power by gaining control over men
  • Women seek special favors under the guise of equality
  • Women exaggerate their problems at work

There are a few points to make about these questions: first, they are all clearly true to some extent. I say that because these are behaviors that all kinds of people engage in. If these behaviors are not specific to one sex – if both men and women exaggerate their problems at work – then agreement with the idea that women do does not stop me from believing men do this as well and, accordingly, does not necessarily track any kind of sexist belief (the alternative, I suppose, is to believe that women never exaggerate problems, which seems unlikely). If the questions are meant to be interpreted as a relative statement (e.g., “women exaggerate their problems at work more than men do”), then that statement needs to first be assessed empirically as true or false before you can say that endorsement of it represents sexism. If women actually do tend to exaggerate problems at work more (a matter that is quite difficult to objectively determine because of what the term exaggerate means), then agreement with the statement just means you accurately perceive reality; not that you’re a sexist.

More to the point, however, none of the measures ask about what the researchers interpret them to mean: women seeking special favors does not imply they are incompetent or unfit to hold positions outside of the home, nor does it imply that one views gender relations primarily as adversarial. If those views are really what a researcher is trying to get at, then they ought to just ask about them directly. A similar story emerges for the benevolent questions:

  • Women have a quality of purity few men possess
  • Men should sacrifice to provide for women
  • Despite accomplishment, men are incomplete without women

 Again, I see no mention of women’s competency, ability, intelligence, or someone’s endorsement of strict gender roles. Saying that men ought to behave altruistically towards women in no way implies that women can’t manage without men’s help. When a man offers to pay for an anniversary dinner (a behavior which I have seen labeled sexist before), he is usually not doing so because he feels his partner is incapable of paying anymore than my helping a friend move suggests I view them as a helpless child. 

“Our saving you from this fire implies you’re unfit to hold public office”

The argument can, of course, be made that scores on the ASI are related to the things these researchers actually want to measure. Indeed, Glick & Fiske (1996) made that very argument: they report that the hostile sexism scores (controlling for the benevolent scores) did correlate with “Old Fashion Sexism” and “Attitudes towards Women” scores (rs = .43 and .60, respectively, bearing in mind that was almost 20 years ago and these attitudes are changing). However, the correlations between benevolent sexism scores and these sexist attitudes were effectively zero (rs = -.03 and .04, respectively). In other words, it appears that people endorse these statements for reasons that have nothing at all to do with whether they view women as weak, or stupid, or any other pejorative you might throw out there, and their responses may tell you nothing at all about their opinion concerning gender roles. If you want to know about those matters, then ask about them. In general, it’s fine to speculate about what your results might mean – how they can best be interpreted – but an altogether easier path is to simply ask about such matters directly and reduce the need for pointless speculation.

 References: Glick, P. & Fiske, S. (1996). The ambivalent sexism inventory: Differentiating hostile and benevolent sexism. Journal of Personality & Social Psychology, 70, 491-512.

Loo, R. & Thorpe, K. (1998). Attitudes towards women’s roles in society: A replication after 20 years. Sex Roles, 39, 903-912.

Zell, E., Strickhouser, J., Lane, T., & Teeter, S. (2016). Mars, Venus, or Earth? Sexism and the exaggeration of psychological gender differences. Sex Roles, 75, 287-300.

Money For Nothing, But The Chicks Aren’t Free

When people see young, attractive women in relationships with older and/or unattractive men, the usual perception that comes to mind is that the relationship revolves around money. This perception is usual because it tends to be accurate: women do, in fact, tend to prefer men who both have access to financial resources and who are willing to share them.  What is rather notable is that the reverse isn’t quite as a common: a young, attractive man shacking up with an older, rich woman just doesn’t call too many examples to mind. Women seem to have a much more pronounced preference for men with wealth than men have for women. While examples of such preferences playing themselves out in real life exist anecdotally, it’s always good to try and showcase their existence empirically.

Early attempts were made by Dr. West, but replications are required

This brings me to a new paper by Arnocky et al (2016) that examined how altruism affects mating success in humans (as this is still psychology research, “humans” translates roughly as “undergraduate psychology majors”, but such is the nature of convenience samples). The researchers first sought (a) to document that more altruistic people really were preferred as mating partners (spoilers: they are), and then (b) to try and explain why we might expect them to be. Let’s begin with what they found, as that much is fairly straightforward. In their first study, Arnocky et al (2016) recruited 192 women and 105 men from a Canadian university and asked them to complete a few self-report measures: an altruism scale (used to measure general dispositions towards providing aid to others when reciprocation is unlikely), a mating success scale (measuring perceptions of how desirable one tends to be towards the opposite sex), their numbers of lifetime sexual partners, as well as the number of those that were short-term, the number of times over the last month they had sex with their current partner (if they had one, which about 40% did), and a measure of their personality more generally.

These measures were then entered into a regression (controlling for personality). When it came to predicting perceived mating success, reported altruism was a significant predictor (ß = 0.25), but neither sex nor the altruism-sex interaction was. This suggests that both men and women tend become more attractive to the opposite sex if they behave more altruistically (or, conversely, that people who are more selfish are less desirable, which sounds quite plausible). However, what it means for one to be successful in the mating domain varies by sex: for men, having more sexual partners usually implies a greater level of success, whereas the same does not hold true for women as often (as gametes are easy to obtain for women, but investment is difficult). In accordance with this point, it was also found that altruism predicted the number of lifetime sexual partners overall (ß = .16), but this effect was specific to men: more altruistic men had more sexual partners (and more casual ones), whereas more altruistic women did not. Finally, within the contexts of existing relationships, altruism also (sort of) predicted the number of times someone had sex with their partner in the last month (ß = .27); while there was not a significant interaction with sex, a visual inspection of the provided graphs suggest that if this effect existed, it was being predominately carried by altruistic women having more sex within a relationship; not the men.

Now that’s all well and good, but the authors wanted to go a little further. In their second study, rather than just asking participants about how altruistic they were, they offered participants the opportunity to be altruistic: after completing the survey, participants could indicate how much (if any) of their earnings they wanted to donate to a charity of their choice. That way, you get what might be a less-biased measure of one’s actual altruism (rather than their own perception of it). Another 335 women and 189 men were recruited for this second phase and, broadly, the results follow the same general pattern, but there were some notable differences. In terms of mating success, actual altruistic donations (categorized as either making a donation or not, rather than the amount donated) were not a good predictor (ß = -.07). In terms of number of lifetime dating and sexual partners, however, the donation-by-sex interaction was significant, indicating that more charitable men – but not women – had a greater number of relationships and sexual partners (perhaps suggesting that charitable men tend to have more, but shorter, relationships, which isn’t necessarily a good thing for the women involved). Donations also failed to predict the amount of sex participants had been having in their relationship in the last month.

Guess the blood drive just isn’t a huge turn on after all

With these results in mind, there are two main points I wanted to draw attention to. The first of these concerns the measures of altruism in general: effectively charitable behaviors to strangers. While such a behavior might be a more “pure” form of altruistic tendencies as compared with, say, helping a friend move or giving money to your child, it does pose some complications for the present topic. Specifically, when looking for a desirable mate, people might not want someone who is just generally altruistic. After all, it doesn’t always do me much good if my committed partner is spending time and investing resources in other people. I would probably prefer that resources be preferentially directed at me and those I care about, rather than strangers, and I might especially dislike it if altruism directed towards strangers came at my expense (as the same resources can’t be invested in me and someone else most of the time). While it is possible that such investments in strangers could return to me later in the form of them reciprocating such aid to my partner, it seems unlikely that deficit would be entirely and consistently made up, let alone surpassed.

To make the point concrete, if someone was equally altruistic towards all people, there would be little point in forming as kind of special relationship with that kind person (friendships or otherwise) because you’d get the same benefits from them regardless of how much you invested in them (even if that amount was nothing).

This brings me to the second point I wanted to discuss: the matter of why people like the company of altruists. There are two explanations that come to mind. The first explanation is simple: people like access to resources, and altruists tend to provide them. This explanation should hardly require much in the way of testing given its truth is plainly obvious. The second explanation is more complex, and it’s one the authors favor: altruism honestly signals some positive, yet difficult-to-observe quality about the altruist. For instance, if I were to donate blood, or my time to clean up a park, this would tell you something about my underlying genetic qualities, as an individual in worse condition couldn’t shoulder the costs of altruism effectively. In this sense, altruism functions in a comparable manner to a peacock’s tail feathers; it’s a biologically-honest signal because it’s costly.

While it does have some plausibility, this signaling explanation runs into some complications. First, as the authors note, women donated more than men did (70% to 57%), despite donating predicting sexual behavior better for men. If women were donating to signal some positive qualities in the mating domain, it’s not at all clear it was working. Further, patterns of charitable donations in the US show a U-shaped distribution, whereby those with access to the most and  the fewest financial resources tend to donate more than those in the middle. This seems like a pattern the signaling explanation should not predict if altruism is meaningfully and consistently tied to important, but difficult-to-observe biological characteristics. Finally, while the argument could be made that altruism directed towards friends, sexual partners, and kin are not necessarily indicative of someone’s willingness to donate to strangers (i.e., how altruistic they are dispositionally might not predict how nepotistic they are), well, that’s kind of a problem for the altruism-as-signaling model. If donations towards strangers are fairly unpredictive of altruism towards closer relations, then they don’t really tell you what you want to know.  Specifically, if you want to know how good of a friend or dating partner someone would be for you, a better cue is how much altruism they direct towards their friends and romantic partners; not how much they direct to strangers.

“My boyfriend is so altruistic, buying drinks for other women like that”

Last, we can consider the matter of why people behave altruistically, with respect to the mating domain. (Very) broadly speaking, there are two primary challenges people need to overcome: attracting a mate and retaining them. Matters get tricky here, as altruism can be used for both of these tasks. As such, a man who is generally altruistic towards lot of people might be using altruism as a means of attracting the attention of prospective mates without necessarily intending to keep them around. Indeed, the previous point about how altruistic men report having more relationships and sexual partners could be interpreted in just such a light. There are other explanations, of course, such as the prospect that generally selfish people simply don’t have many relationships at all, but these need to be separated out. In either case, in terms of how much altruism we provide to others, I suspect that the amount provided to strangers and charitable organizations only makes up a small fraction; we give much more towards friends, family, and lovers regularly. If that’s the case, measuring someone’s willingness to donate in those fairly uncommon contexts might not capture their desirability as partner as well as we would like.

References: Arnocky, S., Piche, T., Albert, G., Ouellette, D., & Barclay, P. (2016). Altruism predicts mating success in humans. British Journal of Psychology, DOI:10.1111/bjop.12208


Skepticism Surrounding Sex

It’s a basic truth of the human condition that everybody lies; the only variable is about what

One of my favorite shows from years ago was House; a show centered around a brilliant but troubled doctor who frequently discovers the causes of his patient’s ailments through discerning what they – or others – are lying about. This outlook on people appears to be correct, at least in spirit. Because it is sometimes beneficial for us that other people are made to believe things that are false, communication is often less than honest. This dishonesty entails things like outright lies, lies by omission, or stretching the truth in various directions and placing it in different lights. Of course, people don’t just lie because deceiving others is usually beneficial. Deception – much like honesty – is only adaptive to the extent that people do reproductively-relevant things with it. Convincing your spouse that you had an affair when you didn’t is dishonest for sure, but probably not a very useful thing to do; deceiving someone about what you had for breakfast is probably fairly neutral (minus the costs you might incur from coming to be known as a liar). As such, we wouldn’t expect selection to have shaped our psychology to lie about all topics with equal frequency. Instead, we should expect that people tend to preferentially lie about particular topics in predictable ways.

Lies like, “This college degree will open so many doors for you in life”

The corollary idea to that point concerns skepticism. Distrusting the honesty of communications can protect against harmful deceptions, but it also runs the risk of failing to act on accurate and beneficial information. There are costs and benefits to skepticism as there are to deception. Just as we shouldn’t expect people to be dishonest about all topics equally often, then, we shouldn’t expect people to be equally skeptical of all the information they receive either. This is point I’ve talked about before with regards to our reasoning abilities, whereby information agreeable to our particular interests tends to be accepted less critically, while disagreeable information is scrutinized much more intensely.

This line of thought was recently applied to the mating domain in a paper by Walsh, Millar, & Westfall (2016). Humans face a number of challenges when it comes to attracting sexual partners typically centered around obtaining the highest quality of partner(s) one can (metaphorically) afford, relative to what one offers to others. What determines the quality of partners, however, is frequently context specific: what makes a good short-term partner might differ from what makes a good long-term partner and – critically, as far as the current research is concerned – the traits that make good male partners for women are not the same as those that make good females partner for men. Because women and men face some different adaptive challenges when it comes to mating, we should expect that they would also preferentially lie (or exaggerate) to the opposite sex about those traits that the other sex values the most. In turn, we should also expect that each sex is skeptical of different claims, as this skepticism should reflect the costs associated with making poor reproductive decisions on the basis of bad information.

In case that sounds too abstract, consider a simple example: women face a greater obligate cost when it comes to pregnancy than men do. As far as men are concerned, their role in reproduction could end at ejaculation (which it does, for many species). By contrast, women would be burdened with months of gestation (during which they cannot get pregnant again), as well as years of breastfeeding prior to modern advancements (during which they also usually can’t get pregnant). Each child could take years of a woman’s already limited reproductive lifespan, whereas the man has lost a few minutes. In order to ease those burdens, women often seek male partners who will stick around and invest in them and their children. Men who are willing to invest in children should thus prove to be more attractive long-term partners for women than those who are unwilling. However, a man’s willingness to stick around needs to be assessed by a woman in advance of knowing what his behavior will actually be. This might lead to men exaggerating or lie about their willingness to invest, so as to encourage women to mate with them. Women, in turn, should be preferentially skeptical of such claims, as being wrong about a man’s willingness to invest is costly indeed. The situation should be reversed for traits that men value in their partners more than women.

Figure 1: What men most often value in a woman

Three such traits for both men and women were examined by Walsh et al (2016). In their study, eight scenarios depicting a hypothetical email exchange between a man and woman who had never met were displayed to approximately 230 (mostly female; 165) heterosexual undergraduate students. For the women, these emails depicted a man messaging a woman; for men, it was a woman messaging a man. The purpose of these emails was described as the person sending them looking to begin a long-term intimate relationship with the recipient. Each of these emails described various facets of the sender, which could be broadly classified as either relevant primarily to female mating interests, relevant to male interests, or neutral. In terms of female interests, the sender described their luxurious lifestyle (cuing wealth), their desire to settle down (commitment), or how much they enjoy interacting with children (child investment). In terms of male interests, the sender talked about having a toned body (cuing physical attractiveness), their openness sexually (availability/receptivity), or their youth (fertility and mate value). In the two neutral scenarios, the sender either described their interest in stargazing or board games.

Finally, the participants were asked to rate (on a 1-5 scale) how deceitful they thought the sender was, whether they believed the sender or not, and how skeptical they were of the claims in the message. These three scores were summed for each participant to create a composite score of believability for each of the messages (the lower the score, the less believable it was rated as being). Those scores were then averaged across the female-relevant items (wealth, commitment, and childcare), the male-relevant items (attractiveness, youth, and availability), and the control conditions. (Participants also answered questions about whether the recipient should respond and how much they personally liked the sender. No statistical analyses are reported on those measures, however, so I’m going to assume nothing of note turned up)

The results showed that, as expected, the control items were believed more readily (M = 11.20) than the male (M = 9.85) or female (9.6) relevant items. This makes sense, inasmuch as believing lies about stargazing or interests in board games aren’t particularly costly for either sex in most cases, so there’s little reason to lie about them (and thus little reason to doubt them); by contrast, messages about one’s desirability as a partner have real payoffs, and so are treated more cautiously. However, an important interaction with the sex of the participant was uncovered as well: female participants were more skeptical on the female-relevant items (M = about 9.2) than males were (M = 10.6); similarly, males were more likely to be skeptical in male-relevant conditions  (M = 9.5) than females were (M = 10). Further, the scores for the individual items all showed evidence of the same sex kinds of differences in skepticism. No sex difference emerged for the control condition, also as expected.

In sum, then – while these differences were relatively small in magnitude – men tended to be more skeptical of claims that, if falsely believed, were costlier for them than women, and women tended to be more skeptical of claims that, if falsely believed, were costlier for them than men. This is a similar pattern to that found in the reasoning domain, where evidence that agrees with one’s position is accepted more readily than evidence that disagrees with it.

“How could it possibly be true if it disagrees with my opinion?”

The authors make a very interesting point towards the end of their paper about how their results could be viewed as inconsistent with the hypothesis that men have a bias to over-perceived women’s sexual interest. After all, if men are over-perceiving such interest in the first place, why would they be skeptical about claims of sexual receptivity? It is possible, of course, that men tend to over-perceive such availability in general and are also skeptical of claims about its degree (e.g., they could still be manipulated by signals intentionally sent by females and so are skeptical, but still over-perceive ambiguous or less-overt cues), but another explanation jumps out at me that is consistent with the theme of this research: perhaps when asked to self-report about their own sexual interest, women aren’t being entirely accurate (consciously or otherwise). This explanation would fit well with the fact that men and women tend to perceive a similar level of sexual interest in other women. Then again, perhaps I only see that evidence as consistent because I don’t think men, as a group, should be expected to have such a bias, and that’s biasing my skepticism in turn.

References: Walsh, M., Millar, M., & Westfall, S. (2016). The effects of gender and cost on suspicion in initial courtship communications. Evolutionary Psychological Science, DOI 10.1007/s40806-016-0062-8

Why Women Are More Depressed Than Men

Women are more likely to be depressed than men; about twice as likely here in the US, as I have been told. It’s an interesting finding, to be sure, and making sense of it poses a fun little mystery (as making sense of many things tends to). We don’t just want to know that women are more depressed than men; we also want to know why women are more depressed. So what are the causes of this difference? The Mayo Clinic floats a few explanations, noting that this sex difference appears to emerge around puberty. As such, many of the explanations they put forth center around the problems that women (but not men) might face when undergoing that transitional period in their life. These include things like increased pressure to achieve in school, conflict with parents, gender confusion, PMS, and pregnancy-related factors. They also include ever-popular suggestions such as societal biases that harm women. Now I suspect these are quite consistent with the answers you would get if queried your average Joe or Jane on the street as to why they think women are more depressed. People recognize that depression often appears to follow negative life events and stressors, and so they look for proximate conditions that they believe (accurately or not) disproportionately affect women.

Boys don’t have to figure out how to use tampons; therefore less depression

While that seems to be a reasonable strategy, it produces results that aren’t entirely satisfying. First, it seems unlikely that women face that much more stress and negative life events than men do (twice as much?) and, secondly, it doesn’t do much to help us understand individual variation. Lots of people face negative life events, but lots of them also don’t end up spiraling into depression. As I noted above, our understanding of the facts related to depression can be bolstered by answering the why questions. In this case, the focus many people have is on answering the proximate whys rather than the ultimate ones. Specifically, we want to know why people respond to these negative life events with depression in the first place; what adaptive function depression might have. Though depression reactions appear completely normal to most, perhaps owing to their regularity, we need to make that normality strange. If, for example, you imagine a new mouse mother facing the stresses of caring for her young in a hostile world, a postpartum depression on her part might seem counterproductive: faced with the challenges of surviving and caring for her offspring, what adaptive value would depressive symptoms have? How would low energy, a lack of interest in important everyday activities, and perhaps even suicidal ideation help make her situation better? If anything, they would seem to disincline her from taking care of these important tasks, leaving her and her dependent offspring worse off. This strangeness, of course, wouldn’t just exist in mice; it should be just as strange when we see it in humans.

The most compelling adaptive account of depression I’ve read (Hagen, 2003) suggests that the ultimate why of depression focuses on social bargaining. I’ve written about it before, but the gist of the idea is as follows: if I’m facing adversity that I am unlikely to be able to solve alone, one strategy for overcoming that problem is to recruit others in the world to help me. However, those other people aren’t always forthcoming with the investment I desire. If others aren’t responding to my needs adequately, it would behoove me to try and alter their behavior so as to encourage them to increase their investment in me. Depression, in this view, is adapted to do just that. The psychological mechanisms governing depression work to, essentially, place the depressed individual on a social strike. When workers are unable to effectively encourage an increased investment from their employers (perhaps in the form of pay or benefits), they will occasionally refuse to work at all until their conditions improve. While this is indeed costly for the workers, it is also costly for the employer, and it might be beneficial for the employer to cave to the demands rather than continue to face the costs of not having people work. Depression shows a number of parallels to this kind of behavior, where people withdraw from the social world – taking with them the benefits they provided to others – until other people increase their investment in the depressed individual to help see them through a tough period.

Going on strike (or, more generally, withdrawing from cooperative relationships), of course, is only one means of getting other people to increase their investment in you; another potential strategy is violence. If someone is enacting behaviors that show they don’t value me enough, I might respond with aggressive behaviors to get them to alter that valuation. Two classic examples of this could be shooting someone in self-defense or a loan-shark breaking a delinquent client’s legs. Indeed, this is precisely the type of function that Sell et al (2009) proposed that anger has: if others aren’t giving me my due, anger motivates me to take actions that could recalibrate their concern for my welfare. This leaves us with two strategies – depression and anger – that can both solve the same type of problem. The question arises, then, as to which strategy will be the most effective for a given individual and their particular circumstances. This raises a rather interesting possibility: it is possible that the sex difference in depression exists because the anger strategy is more effective for men, whereas the depression strategy is more effective for women (rather than, say, because women face more adversity than men). This would be consistent with the sex difference in depression arising around puberty as well, since this is when sex differences in strength also begin to emerge. In other words, both men and women have to solve similar social problems; they just go about it in different ways. 

“An answer that doesn’t depend on wide-spread sexism? How boring…”

Crucially, this explanation should also be able to account for within-sex differences as well: while men are more able to successfully enact physical aggression than women, not all men will be successful in that regard since not all men possess the necessary formidability. The male who is 5’5″ and 130 pounds soaking wet likely won’t win against his taller, heavier, and stronger counterparts in a fight. As such, men who are relatively weak might preferentially make use of the depression strategy, since picking fights they probably won’t win is a bad idea, while those who are on the stronger side might instead make use of anger more readily. Thankfully, a new paper by Hagen & Rosenstrom (2016) examines this very issue; at least part of it. The researchers sought to test whether upper-body strength would negatively predict depression scores, controlling for a number of other, related variables.

To do so, they accessed data from the National Health and Nutrition Examination Survey (NHANES), netting a little over 4,000 subjects ranging in age from 18-60. As a proxy for upper-body strength, the authors made use of the measures subjects had provided of their hand-grip strength. The participants had also filled out questions concerning their depression, height and weight, socioeconomic status, white blood cell count (to proxy health), and physical disabilities. The researchers predicted that: (1) depression should negatively correlate with grip-strength, controlling for age and sex, (2) that relationship should be stronger for men than women, and (3) that the relationship would persist after controlling for physical health. About 9% of the sample qualified as depressed and, as expected, women were more likely to report depression than men by about 1.7 times. Sex, on its own, was a good predictor of depression (in their regression, ß = 0.74).

When grip-strength was added into the statistical model, however, the effect of sex dropped into the non-significant range (ß = 0.03), while strength possessed good predictive value (ß = -1.04). In support of the first hypothesis, then, increased upper-body strength did indeed negatively correlate with depression scores, removing the effect of sex almost entirely. In fact, once grip strength was controlled for, men were actually slightly more likely to report depression than women (though this didn’t appear to be significant). Prediction 2 was not supported, however, with their being no significant interaction between sex and grip-strength on measures of depression. This effect persisted even when controlling for socioeconomic status, age, anthropomorphic, and hormonal variables. However, physical disability did attenuate the relationship between strength and depression quite a bit, which is understandable in light of the fact that physically-disabled individuals likely have their formidability compromised, even if they have stronger upper bodies (an example being a man in a wheelchair having good grip strength, but still not being much use in a fight). It is worth mentioning that the relationship between strength and depression appeared to grow larger over time; the authors suggest this might have something to do with older individuals having more opportunities to test their strength against others, which sounds plausible enough. 

Also worth noting is that when depression scores were replaced with suicidal ideation, the predicted sex-by-strength interaction did emerge, such that men with greater strength reported being less suicidal, while women with greater strength reported being more suicidal (the latter portion of which is curious and not predicted). Given that men succeed at committing suicide more often than women, this relationship is probably worth further examination.  

“Not today, crippling existential dread”

Taken together with findings from Sell et al (2009) – where men, but not women, who possessed greater strength reported being quicker to anger and more successful in physical conflicts – the emerging picture is one in which women tend to (not consciously) “use” depression as a means social bargaining because it tends to work better for them than anger, whereas the reverse holds true for men. To be clear, both anger and depression are triggered by adversity, but those events interact with an individual’s condition and their social environment in determining the precise response. As the authors note, the picture is likely to be a dynamic one; not one that’s as simple as “more strength = less depression” across the board. Of course, other factors that co-vary with physical strength and health – like attractiveness – could also being playing a roll in the relationship with depression, but since such matters aren’t spoken to directly by the data, the extent and nature of those other factors is speculative.

What I find very persuasive about this adaptive hypothesis, however – in addition to the reported data – is that many existing theories of depression would not make the predictions tested by Hagen & Rosenstrom (2016) in the first place. For example, those who claim something like, “depressed people perceive the world more accurately” would be at a bit of a loss to explain why those who perceive the world more accurately also seem to have lower upper-body strength (they might also want to explain why depressed people don’t perceive the world more accurately, either). A plausible adaptive hypothesis, on the other hand, is useful for guiding our search for, and understanding of, the proximate causes of depression.

References: Hagen, E.H. (2003). The bargaining model of depression. In: Genetic and Cultural Evolution of Cooperation, P. Hammerstein (ed.). MIT Press, 95-123

Hagen, E. & Rosenstrom, T. (2016). Explain the sex difference in depression with a unified bargaining model of anger and depression. Evolution, Medicine, & Public Health, 117-132

Sell, A., Tooby, J., & Cosmides, L. (2009). Formidability and the logic of human anger. Proceedings of the National Academy of Sciences, 106, 15073-78.

Chivalry Isn’t Dead, But Men Are

In the somewhat-recent past, there was a vote in the Senate held on the matter of whether women in the US should be required to sign up for the selective service – the military draft – when they turn 18. Already accepted, of course, was the idea that men should be required to sign up; what appears to be a relatively less controversial idea. This represents yet another erosion of male privilege in modern society; in this case, the privilege of being expected to fight and die in armed combat, should the need arise. Now whether any conscription is likely to happen in the foreseeable future (hopefully not) is a somewhat different matter than whether women would be among the first drafted if that happened (probably not), but the question remains as to how to explain this state of affairs. The issue, it seems, is not simply one of whether men or women are better able to shoulder the physical demands of combat, however; it extends beyond military service into intuitions about real and hypothetical harm befalling men and women in everyday life. When it comes to harm, people seem to generally care less about it happening to men.


One anecdotal example of these intuitions I’ve encountered during my own writing is when an editor at Psychology Today removed an image in one my posts of a woman undergoing bodyguard training in China by having a bottle smashed over her head (which can be seen here; it’s by no means graphic). There was a concern expressed that the image was in some way inappropriate, despite my posting of other pictures of men being assaulted or otherwise harmed. As a research-minded individual, however, I want to go beyond simple anecdotes from my own life that confirm my intuitions into the empirical world where other people publish results that confirm my intuitions. While I’ve already written about this issue a number of times, it never hurts to pile on a little more.  Recently, I came upon a paper by FeldmanHall et al (2016) that examined these intuitions about harm directed towards men and women across a number of studies that can help me do just that.

The first of the studies in the paper was a straightforward task: fifty participants were recruited from Mturk to respond to a classic morality problem called the footbridge dilemma. Here, the life of five people can be saved from a train by pushing one person in front of it. When these participants were asked whether they would push a man or woman to their death (assuming, I think, that they were going to push one of them), 88% of participants opted for killing the man. Their second study expanded a bit on that finding using the same dilemma, but asking instead how willing they would be (on a 1-10 scale) to push either a man, woman, or a person of unspecified gender without other options existing. The findings here with regard to gender were a bit less dramatic and clear-cut: participants were slightly more likely to indicate that they would push a man (M = 3.3) than a woman (M = 3.0), though female participants were nominally less likely to push a woman (roughly M = 2.3) than men were (roughly M = 3.8), perhaps counter to what might be predicted. That said, the sample size for this second study was fairly small (only about 25 per group), so that difference might not be worth making much over until more data is collected.

When faced with a direct and unavoidable trade-off between the welfare of men and women, then, the results overwhelmingly showed that the women were being favored; however, when it came to cases where men or women could be harmed alone, there didn’t seem to be a marked difference between the two. That said, that moral dilemma alone can only take us so far in understanding people’s interests about the welfare of others in no small part because of their life-and-death nature potentially introducing ceiling effects (man or woman, very few people are willing to throw someone else in front of a train). In other instances where the degree of harm is lowered – such as, say, male vs female genital cutting – differences might begin to emerge. Thankfully, FeldmanHall et al (2016) included an additional experiment that brought these intuitions out of the hypothetical and into reality while lowering the degree of harm. You can’t kill people to conduct psychological research, after all.


In the next experiment, 57 participants were recruited and given £20. At the end of the experiment, any money they had would be multiplied by ten, meaning participants could leave with a total of £200 (which is awfully generous as far as these things go). As with most psychology research, however, there was a catch: the participants would be taking part in 20 trials where £1 was at stake. A target individual – either a man or a woman – would be receiving a painful electric shock, and the participants could give up some of that £1 to reduce its intensity, with the full £1 removing the shock entirely. To make the task a little less abstract, the participants were also forced to view videos of the target receiving the shocks (which, I think, were prerecorded videos of real shocks – rather than shocks in real time – but I’m not sure from my reading of the paper if that’s a completely accurate description).

In this study, another large difference emerged: as expected, participants interacting with female targets ended up keeping less money by the end (M = £8.76) than those interacting with male targets (M = £12.54; d = .82). In other words, the main finding of interest was that participants were willing to give up substantially more money to prevent women from receiving painful shocks than they were to help men. Interestingly, this was the case in spite of the facts that (a) the male target in the videos was rated more positively overall than the female target, and (b) in a follow-up study where participants provided emotional reactions to thinking about being a participant in the former study, the amount of reported aversion to letting the target suffer shocks was similar regardless of the target’s gender. As the authors conclude:

While it is equally emotionally aversive to hurt any individual—regardless of their gender—that society perceives harming women as more morally unacceptable, suggests that gender bias and harm considerations play a large role in shaping moral action.

So, even though people find harming others – or letting them suffer harm for a personal gain – to generally be an uncomfortable experience regardless of their gender, they are more willing to help/avoid harming women than they are men, sometimes by a rather substantial margin.

Now onto the fun part: explaining these findings. It doesn’t go nearly far enough as an explanation to note that “society condones harming men more than women,” as that just restates the finding; likewise, we only get so far by mentioning that people perceive men to have a higher pain tolerance than women (because they do), as that only pushes the question back a step to the matter of why men tolerate more pain than women. As for my thoughts, first, I think these findings highlight the importance of a modular understanding of psychological systems: our altruistic and moral systems are made up of a number of component pieces, each with a distinct function, and the piece that is calculating how much harm is generated is, it would seem, not the same piece deciding whether or not to do something about it. The obvious reason for this distinction is that alleviating harm to others isn’t always adaptive to the same extent: it does me more adaptive good to help kin relative to non-kin, friends relative to strangers, and allies relative to enemies, all else being equal. 

“Just stay out of it; he’s bigger than you”

Second, it might well be the case that helping men, on average, tends to pay off less than helping women. Part of the reason for that state of affairs is that female reproductive potential cannot be replaced quite as easily as male potential; male reproductive success is constrained by the number of available women much more than female potential is by male availability (as Chris Rock put it, “any money spent on dick is a bad investment“). As such, men might become particularly inclined to invest in alleviating women’s pain as a form of mating effort. The story clearly doesn’t end there, however, or else we would predict men being uniquely likely to benefit women, rather than both sexes doing similarly. This raises two additional possibilities to me: one of these is that, if men value women highly as a form of mating effort, that increased social value could also make women more valuable to other women in turn. To place that in a Game of Thrones example, if a powerful house values their own children highly, non-relatives may come to value those same children highly as well in the hopes of ingratiating themselves to – or avoiding the wrath of – the child’s family.

The other idea that comes to mind is that men are less willing to reciprocate aid that alleviated their pain because to do so would be an admission of a degree of weakness; a signal that they honestly needed the help (and might in the future as well), which could lower their relative status. If men are less willing to reciprocate aid, that would make men worse investments for both sexes, all else being equal; better to help out the person who would experience more gratitude for your assistance and repay you in turn. While these explanations might or might not adequately explain these preferential altruistic behaviors directed towards women, I feel they’re worthwhile starting points.

References: FeldmanHall, O., Dalgleish, T., Evans, D., Navrady, L., Tedeschi, E., & Mobbs, D. (2016). Moral chivalry: Gender and harm sensitive predict costly altruism. Social Psychological & Personality Science, DOI: 10.1177/1948550616647448

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:

“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.

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%. 

Useful for both spotting statistical blips and burning insects

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.

 Maybe it was too much work and they lacked a growth mindset

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.

Psychology Research And Advocacy

I get the sense that many people get a degree in psychology because they’re looking to help others (since most clearly aren’t doing it for the pay). For those who get a degree in the clinical side of the field, this observation seems easy to make; at the very least, I don’t know of any counselors or therapists who seek to make their clients feel worse about the state their life is in and keep them there. For those who become involved in the research end of psychology, I believe this desire to help others is still a major motivator. Rather than trying to help specific clients, however, many psychological researchers are driven by a motivation to help particular groups in society: women, certain racial groups, the sexually promiscuous, the outliers, the politically liberal, or any group that the researcher believes to be unfairly marginalized, undervalued, or maligned. Their work is driven by a desire to show that the particular group in question has been misjudged by others, with those doing the misjudging being biased and, importantly, wrong. In other words, their role as a researcher is often driven by their role as an advocate, and the quality of their work and thinking can often take a back seat to their social goals.

When megaphones fail, try using research to make yourself louder

Two such examples are highlighted in a recent paper by Eagly (2016), both of which can broadly be considered to focus on the topic of diversity in the workplace. I want to summarize them quickly before turning to some of the other facets of the paper I find noteworthy. The first case concerns the prospect that having more women on corporate boards tends to increase their profitability, a point driven by a finding that Fortune 500 companies in the top quarter of female representation on boards of directors performed better than those in the bottom quarter of representation. Eagly (2016) rightly notes that such a basic data set would be all but unpublishable in academia for failing to do a lot of important things. Indeed, when more sophisticated research was considered in a meta-analysis of 140 studies, the gender diversity of the board of directors had about as close to no effect as possible on financial outcomes: the average correlations across all the studies ranged from about r = .01 all the way up to r = .05 depending on what measures were considered. Gender diversity per se seemed to have no meaningful effect despite a variety of advocacy sources claiming that increasing female representation would provide financial benefits. Rather than considering the full scope of the research, the advocates tended to cite only the most simplistic analyses that provided the conclusion they wanted (others) to hear.

The second area of research concerned how demographic diversity in work groups can affect performance. The general assumption that is often made about diversity is that it is a positive force for improving outcomes, given that a more cognitively-varied group of people can bring a greater number of skills and perspectives to bear on solving tasks than more homogeneous groups can. As it turns out, however, another meta-analysis of 146 studies concluded that demographic diversity (both in terms of gender and racial makeup) had effectively no impact on performance outcomes: the correlation for gender was r = -.01 and was r = -.05 for racial diversity. By contrast, differences in skill sets and knowledge had a positive, but still very small effect (r = .05). In summary, findings like these would suggest that groups don’t get better at solving problems just because they’re made up of enough [men/women/Blacks/Whites/Asians/etc]. Diversity in demographics per se, unsurprisingly, doesn’t help to magically solve complex problems.

While Eagly (2016) appears to generally be condemning the role of advocacy in research when it comes to getting things right (a laudable position), there were some passages in the paper that caught my eye. The first of these concerns what advocates for causes should do when the research, taken as a whole, doesn’t exactly agree with their preferred stance. In this case, Eagly (2016) focuses on the diversity research that did not show good evidence for diverse groups leading to positive outcomes. The first route one might take is to simply misrepresent the state of the research, which is obviously a bad idea. Instead, Eagly suggests advocates take one of two alternative routes: first, she recommends that researchers might conduct research into more specific conditions under which diversity (or whatever one’s preferred topic is) might be a good thing. This is an interesting suggestion to evaluate: on the one hand, people would often be inclined to say it’s a good idea; in some particular contexts diversity might be a good thing, even if it’s not always, or even generally, useful. This wouldn’t be the first time effects in psychology are found to be context-dependent. On the other hand, this suggestion also runs some serious risks of inflating type 1 errors. Specifically, if you keep slicing up data and looking at the issue in a number of different contexts, you will eventually uncover positive results even if they’re just due to chance. Repeated subgroup or subcontext analysis doesn’t sound much different from the questionable statistical practices currently being blamed for psychology’s replication problem: just keep conducting research and only report the parts of it that happened to work, or keep massaging the data until the right conclusion falls out.    

“…the rest goes in the dumpster out back”

Eagly’s second suggestion I find a bit more worrisome: arguing that relevant factors – like increases in profits, productivity, or finding better solutions – aren’t actually all that relevant when it comes to justifying why companies should increase diversity. What I find odd about this is that it seems to suggest that the advocates begin with their conclusion (in this case, that diversity in the work force ought to be increased) and then just keep looking for ways to justify it in spite of previous failures to do so. Again, while it is possible that there are benefits to diversity which aren’t yet being considered in the literature, bad research would likely result from a process where someone starts their analysis with the conclusion and keeps going until they justify it to others, no matter how often it requires shifting the goal posts. A major problematic implication with that suggestion mirrors other aspects of the questionable psychology research practices I mentioned before: when a researcher finds the conclusion they’re looking for, they stop looking. They only collect data up until the point it is useful, which rigs the system in favor of finding positive results where there are none. That could well mean, then, that there will be negative consequences to these diversity policies which are not being considered. 

What I think is a good example of this justification problem leading to shoddy research practices/interpretation follows shortly thereafter. In talking about some of these alternative benefits that more female hires might have, Eagly (2016) notes that women tend to be more compassionate and egalitarian than men; as such, hiring more women should be expected to increase less-considered benefits, such as a reduction in the laying-off of employees during economic downturns (referred to as labor hoarding), or more favorable policies towards time off for family care. Now something like this should be expected: if you have different people making the decisions, different decisions will be made. Forgoing for the moment the question of whether those different policies are better, in some objective sense of the word, if one is interested in encouraging those outcomes (that is, they’re preferred by the advocate) then one might wish to address those issue directly, rather than by proxy. That is to say if you are looking to make the leadership of some company more compassionate, then it makes sense to test for and hire more compassionate people, not hiring more women under the assumption you will be increasing compassion. 

This is an important matter because people are not perfect statistical representations of the groups to which they belong. On average, women may be more compassionate than men; the type of woman who is interested in actively pursuing a CEO position in a Fortune 500 company might not be as compassionate as your average woman, however, and, in fact, might even be less compassionate than a particular male candidate. What Eagly (2016) has ended up reaching, then, is not a justification for hiring more women; it’s a justification for hiring compassionate or egalitarian people. What is conspicuously absent from this section is a call for more research to be conducted on contexts in which men might be more compassionate than women; once the conclusion that hiring women is a good thing has been justified (in the advocate’s mind, anyway), the concerns for more information seem to sputter out. It should go without saying, but such a course of action wouldn’t be expected to lead to the most accurate scientific understanding of our world.

The solution to that problem being more diversity, of course..

To place this point in another quick example, if you’re looking to assemble a group of tall people, it would be better to use people’s height when making that decision rather than their sex, even if men do tend to be taller than women. Some advocates might suggest that being male is a good enough proxy for height, so you should favor male candidates; others would suggest that you shouldn’t be trying to assemble a group of tall people in the first place, as short people offer benefits that tall ones don’t; other still will argue that it doesn’t matter if short people don’t offer benefits as they should be preferentially selected to combat negative attitudes towards the short regardless (at the expense of selecting tall candidates). For what it’s worth, I find the attitude of “keep doing research until you justify your predetermined conclusion” to be unproductive and indicative of why the relationship between advocates and researchers ought not be a close one. Advocacy can only serve as a cognitive constraint that decreases research quality as the goal of advocacy is decidedly not truth. Advocates should update their conclusions in light of the research; not vice versa. 

References: Eagly, A. (2016). When passionate advocates meet research on diversity, does the honest broker stand a chance? Journal of Social Issues, 72, 199-222.