Reputational incentives and post-publication review: two (partial) solutions to the misinformation problem

By April 18, 2017machinelearning

So. There are erroneous analyses published in scientific journals and in the news. Here I’m not talking not about outright propaganda, but about mistakes that happen to coincide with the preconceptions of their authors.

We’ve seen lots of examples. Here are just a few:

– Political scientist Larry Bartels is committed to a model of politics in which voters make decisions based on irrelevant information. He’s published claims about shark attacks deciding elections and subliminal smiley faces determining attitudes about immigration. In both cases, second looks by others showed that the evidence wasn’t really there. I think Bartels was sincere; he just did sloppy analyses—statistics is hard!—and jumped to conclusions that supported his existing views.

– New York Times columnist David Brooks has a habit of citing statistics that fall apart under closer inspection. I think Brooks believes these things when he writes them—OK, I guess he never really believed that Red Lobster thing, he must really have been lying exercising poetic license on that one—but what’s important is that these stories work to make his political points, and he doesn’t care when they’re proved wrong.

– David’s namesake and fellow NYT op-ed columnist Arthur Brooks stepped in it one or twice when reporting survey data. He wrote that Tea Party supporters were happier than other voters, but a careful look at the data suggested the opposite. A. Brooks’s conclusions were counterintuitive and supported his political views; they just didn’t happen to line up with reality.

– The familiar menagerie from the published literature in social and behavioral sciences: himmicanes, air rage, ESP, ages ending in 9, power pose, pizzagate, ovulation and voting, ovulation and clothing, beauty and sex ratio, fat arms and voting, etc etc.

– Gregg Easterbrook writing about politics.

And . . . we have a new one. A colleague emailed me expressing annoyance at a recent NYT op-ed by historian Stephanie Coontz entitled, “Do Millennial Men Want Stay-at-Home Wives?”

Emily Beam does the garbage collection. The short answer is that, no, there’s no evidence that millennial men want stay-at-home wives. Here’s Beam:

You can’t say a lot about millennials based on talking to 66 men.

The GSS surveys are pretty small – about 2,000-3,000 per wave – so once you split by sample, and then split by age, and then exclude the older millennials (age 26-34) who don’t show any negative trend in gender equality, you’re left with cells of about 60-100 men ages 18-25 per wave. . . .

Suppose you want to know whether there is a downward trend in young male disagreement with the women-in-the-kitchen statement. Using all available GSS data, there is a positive, not statistically significant trend in men’s attitudes (more disagreement). Starting in 1988 only, there is very, very small negative, not statistically significant effect.

Only if we pick 1994 as a starting point, as Coontz does, ignoring the dip just a few years prior, do we see a negative less-than half-percentage point drop in disagreement per year, significant at the 10-percent level.

To Coontz’s (or the NYT’s) credit, they followed up with a correction, but it’s half-assed:

The trend still confirms a rise in traditionalism among high school seniors and 18-to-25-year-olds, but the new data shows that this rise is no longer driven mainly by young men, as it was in the General Social Survey results from 1994 through 2014.

And at this point I have no reason to believe anything that Coontz says on this topic, any more than I’d trust what David Brooks has to say about high school test scores or the price of dinner at Red Lobster, or Arthur Brooks on happiness measurements, or Susan Fiske on himmicanes, power pose, and air range. All these people made natural mistakes but then were overcommitted, in part I suspect because the mistaken analyses what they’d like to think is true.

But it’s good enough for the New York Times, or PPNAS, right?

The question is, what to do about it. Peer review can’t be the solution: for scientific journals, the problem with peer review is the peers, and when it comes to articles in the newspaper, there’s no way to do systematic review. The NYT can’t very well send all their demography op-eds to Emily Beam and Jay Livingston, after all. Actually, maybe they could—it’s not like they publish so many op-eds on the topic—but I don’t think this is going to happen.

So here are two solutions:

1. Reputational incentives. Make people own their errors. It’s sometimes considered rude to do this, to remind people that Satoshi Kanazawa Satoshi Kanazawa Satoshi Kanazawa published a series of papers that were dead on arrival because the random variation in his data was so much larger than any possible signal. Or to remind people that Amy Cuddy Amy Cuddy Amy Cuddy still goes around promoting power pose even thought the first author on that paper had disowned the entire thing. Or that John Bargh John Bargh John Bargh made a career out of a mistake and now refuses to admit his findings didn’t replicate. Or that David Brooks David Brooks David Brooks reports false numbers and then refused to correct them. Or that Stephanie Coontz Stephanie Coontz Stephanie Coontz jumped to conclusions based on a sloppy reading of trends from a survey.

But . . . maybe we need these negative incentives. If there’s a positive incentive for getting your name out there, there should be a negative incentive for getting it wrong. I’m not saying the positive and negative incentives should be equal, just that there more of a motivation for people to check what they’re doing.

And, yes, don’t keep it a secret that I published a false theorem once, and, another time, had to retract the entire empirical section of a published paper because we’d reverse-coded a key variable in our analysis.

2. Post-publication review.

I’ve talked about this one before. Do it for real, in scientific journals and also the newspapers. Correct your errors. And, when you do so, link to the people who did the better analyses.

Incentives and post-publication review go together. To the extent that David Brooks is known as the guy who reports made-up statistics and then doesn’t correct them—if this is his reputation—this gives the incentives for future Brookses (if not David himself) to prominently correct his mistakes. If Stephanie Coontz and the New York Times don’t want to be mocked on twitter, they’re motivated to follow up with serious corrections, not minimalist damage control.

Some perspective here

Look, I’m not talking about tarring and feathering here. The point is that incentives are real; they already exist. You really do (I assume) get a career bump from publishing in Psychological Science and PPNAS, and your work gets more noticed if you publish an op-ed in the NYT or if you’re featured on NPR or Ted or wherever. If all incentives are positive, that creates problems. It creates a motivation for sloppy work. It’s not that anyone (Brian Wanink aside) is trying to do sloppy work.

Econ gets it (pretty much) right

Say what you want about economists, but they’ve got this down. First off, they understand the importance of incentives. Second, they’re harsh, harsh critics of each other. There’s not much of an econ equivalent to quickie papers in Psychological Science or PPNAS. Serious econ papers go through tons of review. Duds still get through, of course (even some duds in PPNAS). But, overall, it seems to me that economists avoid what might be called the “happy talk” problem. When an economist publishes something, he or she tries to get it right (politically-motivated work aside), in awareness that lots of people are on the lookout for errors, and this will rebound back to the author’s reputation.

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