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Notes -
Dan Ariely Is In The News Again
[previous discussion here, hat-tip to TraceWoodgrains for bringing it to my twitter feed]
Data Colada Reports:
For those who missed the first one, at least one of the studies in this data is fake. There's an orgy of suspicious problems with it: incredibly implausible reported numbers, ridiculously strong effect size, negative correlations for things that should have been strongly tied together, sample 'twins', inconsistent rounding where only three out of 360 survey values were not rounded to the nearest 5%, miscalculated statistics, means changing from a preprint without derived statistics changing with them. About the only thing missing is the text format mistake showing exactly what was copied and pasted in the Honesty trials; we should be glad there's not a spurious western blot that someone made its way in. DataColada doesn't even cover all of it: there's numbers in the underlying spreadsheet that don't match in the basic math sense.
There's also a bit of a buried lead, though. DataColada needed the raw data to do this analysis. That doesn't mean anyone needed that data to make a serious criticism. The errors here are not subtle. You can tell there's something fucky wucky with this paper just by looking at its charts, and there's only one for this study.
The experimental protocol described people receiving three papers with a hundred errors each, and were told that they would receive 10 cents for every correction, but would be penalized a dollar for every day they were late.
(errors and delay calculated from the underlying source data, but nothing within a round-to-10% value works, either, so this fails the MK1 Eyeball Test. These study's test involve machine-generated papers intended to be meaningless, so a best-case scenario of 70% errors found, while suspiciously even, is at least plausible even for MIT's best and brightest.)
The chart isn't, to be clear, close to a possible combination. The chart doesn't even have a negative value reported, period. And if you start thinking about what a negative value for earnings means -- any further work would have no value, and soon no plausible expected value -- and quickly many the numbers don't really make sense. At best, the trial used a different protocol than its own minimal results show, in a form clearly visible by just reading the paper. A flat rate for participation isn't impossible, but if it was included in the measurement, it should have been in the methods section. It wasn't.
But now we actually have e-mail conversations showing that wasn't the case, either: "There was no show up fee and we did not say anything about the fact that the payment rule could mean that subjects will lose money (and as far as I remember we never had to deal with this problem)". That's for a trial where, by the published protocol and data, a full 19 out of 60 participants had negative earnings, and even assuming a flat $10 participation fee needed to make the chart work, 8 participants would still have had negative earnings.
A fellow academic, Kyle Hyndman, received that e-mail in 2014.
Hyndman, to his credit, did forward the data and conversations to DataColada in 2023. And he was working on a replication attempt most of the time, albeit probably as a low-priority for a decade. He did, after his replication effort, publish in a footnote that : "In October 2024, at the request of the editors, we shared with Dan Ariely an analysis of the contents from the file purportedly for their Study 2 and asked for permission to include a summary of it in the paper. Dan Ariely denied our request, arguing, among other things, that the files we received may not be the actual data."
That does seem to be the defense, for another way this study rhymes with past scandals. Ariely can't remember the actual study protocol, can't be sure this data was what actually got published, and doesn't want a summary of the data that does exist being republished. It is quite possible no records of the experimental protocol exist to prove or disprove anything. I've got a search tool running through old MIT classifieds, and that's more a hope than a process.
There's a plausible, if disturbing, option that had the data fabrication been revealed before the replication failure and the separate scandals in other papers, it would have blown over. There's a plausible, if even more disturbing, option that even with those other data points, it will just blow over again. Maybe a retraction -- and to be fair, there's at least been a request for one -- probably Ariely doesn't get another tv show, probably not a slapped wrist, almost certainly no serious investigation by his school.
But that still leaves a lot of questions. Ariely has over a hundred other papers he's authored or coauthored listed on his own CV. It was plausible for the Honesty trial that no one else should have looked at Ariely's data, or looked at his conclusions with a skeptical eye. It remains plausible that, for two papers, his coauthors and peer reviewers didn't look at the paper with a skeptical eye. Two isn't feeling like a very real number here.
Trace mentioned this story with the opening line "I feel for the coauthor [Wertenbroch] here – he seems to have acted generally honorably and been caught off guard by having a liar for a collaborator." Before I read the paper, my gutcheck was "On one hand, it's nice to see Wertenbroch moving on it. After Gino, I'd seriously worried about the possibility Ariely had just been able to thrive so well because no one he was working with wanted honesty, either."
And then I saw that chart, counted on my fingers, and blinked.
Wertenbroch is, notably, CC'd on that 2014 e-mail where Ariely said that his study did not have any students with a negative balance, and had no participation fee to explain that 10 dollar offset. He could, plausibly, have not reviewed the data or the paper in a decade, and not realized the discrepancy even then. Leaves a bit of a question about what he does do, though. He could, plausibly, have not touched or looked at a single part of the study methodology or data in this entire paper.
In the Honesty trials from the previous scandal, there was an absolute mess where Ariely was responsible for Study 3, and there were serious questions about what, if any, exposure to the fraud the other authors might face. Gino seems to have had minimal responsibility of exposure to Ariely's original data... and separately produced some falsified data in a separate study in the same paper, and multiple other studies in other papers.
DataColada ends today's post with "In our next post, we will share analyses of the Study 1 data file [...]. That experiment is quite different. Our analyses are quite different. But our conclusions are quite similar." I'm working on writing up a post on the aftermath -- or lack thereof -- on the Hindawi scandal.
Eat at Arby's.
EDIT: while DataColada does not spell out the chart discrepancy, it is in their underlying R code. So the description here is more a dumbass noticing the same thing, not me finding something they missed.
Scott found this earlier, by comparing papers between known pseudoscientific fields and soft sciences.
Do you remember where? I remember him describing Ariely et all as under challenge very early, and I don't remember too much predictive beyond a book reviewer calling him a general fraud.
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