This stuff is common, especially in elite schools, and especially among researchers with a lot of popular uptake. Stuff like this brings academic jobs.
Universities have no incentive to fire high-impact professors, and internal channels are populated by friends.
Competition for faculty positions is 1000:1 at elitist schools; jobs aren't available for those who cheat.
1) Results don't replicate.
2) Look up her article in Nature, which claims to be preregistered, then look at the actual preregistration. See the data mismatch.
3) Read her book, and look at the claims in the introduction. Compare those to effect sizes. Are the claims supported or contradicted even by her (already baked) data? By replication studies?
Read the Boaler books and studies. You'll find a lot of fake information too. Not just studies, but also historical stuff (e.g. passages about Einstein and other famous folks).
This is kind of an open secret in the ed research community. Everyone knows about it. Few dare talk in public. You can see outcomes for those who did. And for the general public, it's too technical. That a preregistration comes before data collection isn't something one can explain to a popular audience.
This is the culture at MIT, Brown, Stanford, and I believe many other elite schools. Those three are just where I have first-hand information.
> he said that all his contacts at the insurer had left and that none of them remembered what happened, either
At other times he is quoted as saying that he "doesn't remember", which does remind one of politicians or other guilty parties saying they "don't recall" any of the many remarkable details of past shenanigans.
He seems to be guilty as hell.
This doesn't prove much, but it's an embarrassing coincidence for him.
> The work was conducted over ten years ago by an insurance company with whom I partnered on this study. The data were collected, entered, merged and anonymized by the company and then sent to me. This was the data file that was used for the analysis and then shared publicly.
On a plain reading, Ariely clearly states here that the data file prepared by the insurance company was the same as the one used for the analysis and shared publicly. But we already know that he modified the file:
- He is listed as the creator and last modifier of the published Excel file.
- In an email to his collaborator Nina, he admitted that he switched the outcome labels in an attempt to make the data easier to understand. So clearly the published file was not identical to the one provided by the insurance company.
So I worry that Ariely is taking us for a ride here. He had the most incentive to get the desired results, far more than the nameless insurance company.
The original insurance company dataset, if it exists, is probably lying around in an email somewhere. They were, after all, able to provide the final version, which was prepared not too long after the original.