David Hogg points me to a recent paper, “A Social Priming Data Set With Troubling Oddities” by Hal Pashler, Doug Rohrer, Ian Abramson, Tanya Wolfson, and Christine Harris, which begins:
Chatterjee, Rose, and Sinha (2013) presented results from three experiments investigating social priming—specifically, priming effects induced by incidental exposure to concepts relating to cash or credit cards. They reported that exposing people to cash concepts made them less generous with their time and money, whereas exposing them to credit card concepts made them more generous.
The effects reported in the Chatterjee et al. paper were large—suspiciously large.
Last year, I wrote about a study whose results were stunningly large. It was only after I learned the data had been faked—it was the notorious Lacour and Green voter canvassing paper—that I ruefully wrote that, sometimes a claim that is too good to be true, isn’t.
Pashler et al. skipped my first step and went straight to the data. After some statistical detective work, they conclude:
We are not in a position to determine exactly what series of actions and events could have resulted in this pattern of seemingly corrupted data. In our view, given the results just described, possibilities that would need to be considered would include (a) human error, (b) computer error, and (c) deliberate data fabrication.
In our opinion based solely on the analyses just described, the findings do seem potentially consistent with the disturbing third possibility: that the data records that contributed most to the priming effect were injected into the data set by means of copy-and- paste steps followed by some alteration of the pasted strings in order to mask the abnormal provenance of these data records that were driving the key effect.
No coincidence that we see fraud (or extreme sloppiness) in priming studies
How did we get to this point?
Do you think Chatterjee et al. wanted to fabricate data (if that’s what they did) or do incredibly sloppy data processing (if that’s what happened)? Do you think that, when Chatterjee, Rose, and Sinha were in grad school studying psychology or organizational behavior or whatever, they thought, When I grow up I want to be running my data through the washing machine?
No, of course not.
They were driven to cheat, or to show disrespect for their data, because there was nothing there for them to find (or, to be precise, that any effects that were there, were too small and too variable for them to have any chance of detecting; click on above kangaroo image for a fuller explanation of this point).
Nobody wants to starve. If there’s no fruit on the trees, people will forage through the weeds looking for vegetables. If there’s nothing there, they’ll start to eat dirt. The low quality of research in these subfields of social psychology is a direct consequence of there being nothing there to study. Or, to be precise, it’s a direct consequence of effects being small and highly variable across people and situations.
I’m sure these researchers would’ve loved to secure business-school teaching positions by studying large and real effects. But, to continue my analogy, they got stuck in a barren patch of the forest, eating dirt and tree bark in a desperate attempt to stay viable. It’s not a pretty sight. But I can see how it can happen. I blame them, sure (just as I blame myself for the sloppiness that led to my two erroneous published papers). But I also blame the system, the advisors and peers and journal editors and Ted talk impresarios who misled them into thinking that they were working in a productive area of science, when they weren’t. They were blindfolded and taken into some area of the outback that had nothing to eat.
Outback, huh? I just realize what I wrote. It was unintentional, and I think I was primed by the kangaroo picture.
In all seriousness, I have no doubt that priming occurs—I see it all the time in my own life. My skepticism is with the claim of huge indirect priming effects. As Wagenmakers et al. put it, quoting Hal Pashler, “disbelief does in fact remain an option.” Especially because, as discussed in the present post, if these effects were really present, they’d be interfering with each other all over the place, and these sorts of crude experiments wouldn’t work anyway.
It’s all about the incentives
So . . . you take a research area with small and highly variable effects, but where this is not well understood so you can get publications in top journals with statistically significant results . . . this creates very little incentive to do careful research. I mean, what’s the point? If there’s essentially nothing going on and you’re gonna have to p-hack your data anyway, why not just jump straight to the finish line. Chatterjee et al. could’ve spent 3 years collecting data on 1000 people, they still probably would’ve had to twist the data to get what they needed for publication.
And that’s the other side of the coin. Very little incentive to do careful research, but a very big incentive to cheat or to be so sloppy with your data that maybe you can happen upon a statistically significant finding.
Bad bad incentives + Researchers in a tough position with their careers = Bad situation.