If there’s one phrase that people discussing the results of experiments have heard more than any other, a good candidate might be “correlation does not equal causation”. Correlations can often get mistaken for (at least implying) causation, especially if the results are congenial to a preferred conclusion or interpretation. This is a relatively uncontroversial matter which has been discussed to death, so there’s little need to continue on with it. There is, however, a related reasoning error people also tend to make with regard to correlation; one that is less discussed than the former. This mistake is to assume that a lack of correlation (or a very low one) means no causation. Here are two reasons one might find no correlation, despite underlying relationships: in the first case, no correlation could result from something as simple as there being no linear relationship between two variables. As correlations only measure linear relationships, distributions that resemble bell curves would tend to yield correlations equal to zero.

For the second case, consider the following example: event A causes event B, but only in the absence of variable C. If variable C randomly varies (it’s present half the time and absent the other half), [EDIT: H/T Jeff Goldberg] you might end up with no correlation, or at least a very reduced one, despite direct causation. This example becomes immediately more understandable if you relabel “A” as heterosexual intercourse, “B” as pregnancy, and “C” as contraceptives (ovulation works too, provided you also replace “absence” with presence). That said, even if contraceptives aren’t in the picture, the correlation between sexual intercourse and pregnancy is still pretty low.

And just in case you find that correlation reaching significance, there’s always this.

So why all this talk about correlation and causation? Two reasons: first, this is my website and I find the matter pretty neat. More importantly, though, I’d like to discuss the IAT (implicit association test) today; specifically, I’d like to address the matter of how well the racial IAT correlates (or rather, fails to correlate) with other measures of racial prejudice, and how we ought to interpret that result. While I have touched on this test very briefly before, it was in the context of discussing modularity; not dissecting the test itself. Since the IAT has recently crossed my academic path again on more than one occasion, I feel it’s time for a more complete engagement with it. I’ll start by discussing what the IAT is, what many people seem to think it measures, and finally what I feel it actually assesses.

The IAT was introduced by Greenwald et al in 1998. As per its namesake, the test was ostensibly designed to do something it would appear to do fairly well: measure the relative strengths of initial, automatic cognitive associations between two concepts. If you’d like to see how this test works firsthand, feel free to follow the link above, but, just in case you don’t feel like going through the hassle, here’s the basic design (using the race-version of the test): subjects are asked to respond as quickly as possible to a number of stimuli. In the first phase, subjects will view pictures of black and white faces flashed on the screen and asked to press one key if the face is black and another if it’s white. In the second phase, subjects will do the same task, but this time they’ll press one key if the word that flashes on the screen is positive and another if it’s negative. Finally, these two tasks are combined, with subjects asked to press one key if the face is white or the word is positive, and another key if the face is black or the word is negative (these conditions then flip). Different reaction times in this test are taken to be measures of implicit cognitive associations. So, if you’re faster to categorize black faces with positive words, you’re said to have a more positive association towards black people.

Having demonstrated that many people seem to show a stronger association between white faces and positive concepts, the natural question arises about how to interpret these results. Unfortunately, many psychological researchers and laypeople alike have taken a unwarranted conceptual leap: they assume that these differential association strengths imply implicit racist attitudes. This assumption happens to meet with an unfortunate snag, however, which is that these implicit associations tend to have very weak to no correlations with explicit measures of racial prejudice (even if the measures themselves, like the Modern Racism Scale, are of questionable validity to begin with). Indeed, as reviewed by Arkes & Tetlock (2004), whereas the vast majority of undergraduates tested manifest exceedingly low levels of “modern racism”, almost all of them display a stronger association between white faces and positivity. Faced with this lack of correlation, many people have gone on to make a second assumption to account for this lack, that assumption being that the implicit measure is able to tap some “truer” prejudiced attitude that the explicit measures are not as able to tease out. I can’t help but wonder, though, what those same people would have had to say if positive correlations had turned up…

“Correlations or no, there’s literally no data that could possibly prove us wrong”

Arkes & Tetlock (2004) put forth three convincing reasons to not make that conceptual jump from implicit associations to implicit attitudes. Since I don’t have the space to cover all their objections, I’ll focus on the key points of them. The first is one that I feel ought to be fairly obvious: quicker associations between whites and positive concepts are capable of being generated by merely being aware of racial stereotypes, irrespective of whether one endorses them on any level, conscious or not. Indeed, even African American subjects were found to manifest pro-white biases in these tests. One could take those results as indicative of black subjects being implicit racist against their own ethnic group, though it would seem to make more sense to interpret those results in terms of the black subjects being aware of the stereotypes they did not endorse. The latter interpretation also goes a long way towards understanding the small and inconsistent correlations between the explicit and implicit measures; the IAT is measuring a different concept (knowledge of stereotypes) than the explicit measures (endorsement of stereotypes).

In order to appreciate the next criticism of this conceptual leap, there’s an important point worth bearing in mind concerning this IAT: the test doesn’t measure where two concepts are associated in any sense whatsoever; it merely measures relative strengths of these associations (for example, “bread” might be more strongly associated with “butter” than it is with “banana”, though it might be more associated with both than with “wall”). This importance of this point is that the results of the IAT do not test whether there is a negative association towards any one group; just whether one group is rated more positively than another. While whites might have a stronger association with positive concepts than blacks, it does not follow that blacks have a negative association overall, nor that whites have a particularly positive one either. Both groups could be held in high or low regard overall, with one being slightly favored. In much the same way, I might enjoy eating both pizza and turkey sandwiches, but I would tend to enjoy eating pizza more. Since the IAT does not track whether these response time differentials are due to hostility, these results do not automatically seem to apply well to most definitions of prejudice.

Finally, the authors make the (perhaps politically incorrect) point that noticing behavioral differences between groups – racial or otherwise – and altering behavior accordingly is not, de facto, evidence of an irrational racial biases; it could well represent the proper use of Bayesian inference, passing correspondence benchmarks for rational behavior. If one group, A, happens to perform behavior X more than group B, it would be peculiar to ignore this information if you’re trying to predict the behavior of an individual from one of those groups. In fact, when people fail to do as much in other situations, people tend to call that failure a bias or an error. However, given that race is touchy political subject, people tend to condemn others for using what Arkes & Tetlock (2004) call “forbidden base rates”. Indeed, the authors report that previous research found subjects were willing to condemn an insurance company for using base rate data for the likelihood of property damage in certain neighborhoods when that base rate also happened to correlate with the racial makeup of that neighborhood (but not when those racial correlates were absent).

A result which fits nicely with other theory I’ve written about, so subscribe now and don’t miss any more exciting updates!

To end this on a lighter, (possibly) less politically charged note, a final point worth considering is that this test measures the automaticity of activation; not necessarily the pattern of activation which will eventually obtain. While my immediate reaction towards a brownie within the first 200 milliseconds might be “eat that”, that doesn’t mean that I will eventually end up eating said brownie, nor would it make me implicitly opposed toward the idea of dieting. It would seem that, in spite of these implicit associations, society as a whole has been getting less overtly racist. The need for researchers to dig this deep to try and study racism could be taken as heartening, given that we, “now attempt to gauge prejudice not by what people do, or by what people say, but rather by millisecs of response facilitation of inhibition in implicit association paradigms” (p.275). While I’m sure there are still many people who will make a lot about these reaction time differentials for reasons that aren’t entirely free from their personal politics, it’s nice to know just how much successful progress our culture seems to have made towards eliminating racism.

References: Arkes, H.R., & Tetlock, P.E. (2004). Attributions of implicit prejudice, or “Would Jesse Jackson ‘fail’ the implicit association test?” Psychological Inquiry , 15, 257-278

Greenwald, A.G., McGhee, D.E., & Schwartz, J.L.K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464-1480