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Publication bias without editors? The case of preprint servers

The challenge of building outlets to host research that can't be published in top journals

Published onJun 29, 2021
Publication bias without editors? The case of preprint servers

Science has a problem. There is an element of unavoidable randomness in research. Combine that with a propensity to disproportionately publish notable research, and you end up with two factors that distort our picture of the evidence. First, results that are not sufficiently “interesting” may remain unpublished and unknown. Second, in anticipation of this, researchers might bend their research methods to force their results to be “interesting” and hence, publishable (this practice is sometimes called p-hacking). The resulting biased presentation of evidence presents a challenge for science’s cumulative project to better understand how our world works.

On the other hand, it isn’t necessarily a bad thing that top journal seek to highlight research that challenges our existing beliefs about the world. Even if there aren’t publishing space constraints in a digital world, attention is limited. It makes sense for top journals to curate the research that is most useful to an audience with limited time. That could mean privileging research that provides evidence for some specific theory of how the world works, rather than null results that maybe don’t (Frankel and Kasy 2021 develops this idea more fully).

But not all journals need to fill that role. We could create a different set of publication outlets whose primary purpose is to provide peer review, archiving, and search engine optimization services and not to curate research for casual readers. These outlets could be a home to results not publishable in top journals. In fact, some journals like this already exist, such as the Series on Unsurprising Results in Economics (acronym: SURE). That would enable the full set of results related to a particular topic to be discoverable, with a little bit of effort. We might end up with poorly informed casual readers, who only read top journals, but inventors, policy-makers, and researchers who need to get an accurate idea of the actual state of the evidence would know to dig deeper to get the whole story.

Would that work? One place to get some evidence on this is to look at our experience with preprint servers.

Preprint Servers as a Secondary Publication Outlet?

Preprint servers are places where researchers - typically with some kind of affiliation - can post work that is nominally in progress but usually quite close to a finished product. They are not subject to peer review or editorial discretion, and so could, in principle, serve as a home for research results that don’t end up getting published, perhaps because of publication bias.

Indeed, a non-negligible share of work on preprints is never published. Baumann and Wohlrabe (2020) estimate that about 25% of working papers published on major preprint servers in economics are never published. Lariviere et al. (2014) estimate 36% of working papers on arXiv are never published in a journal listed on the Web of Science, and Tsunoda et al. (2020) estimate 59% of papers posted on bioRxiv during 2013-2019 were not (yet) published.

We have some evidence these preprint servers do help mitigate censoring. Fanelli, Costas, and Ioannidis (2017) obtain 1,910 meta-analyses drawn from all areas of science, and pull from these 33,355 datapoints from original studies. They then look at the size of estimated effects in each of these disciplines for those published in peer-reviewed journals and those published elsewhere (i.e., on preprint servers, but also in conference papers, personal communications, unpublished drafts, graduate theses, etc.). The latter group - which hasn’t been through explicit peer review - is often called “gray literature.” Fanelli, Costas, and Ioannidis find gray literature articles do report smaller effect sizes than those in peer-reviewed journals. That’s consistent with papers on preprint servers facing less publication bias or pressure to engage in p-hacking.

But the effect is pretty small, explaining on the order of 1% of the variation in outcomes. That’s not too surprising, in light of a finding from Franco, Malhotra, and Simonovits (2014) I have discussed elsewhere - in their study of the social sciences, most null results were never written up at all, much less posted publicly on a preprint server. This seemed to be because the researchers believed these studies faced a hopeless path to publication, and hence weren’t worth the effort of writing up. A survey of economists by Brodeur et al. (2023) found similar results - economists who obtain null results were significantly less likely to submit papers for publication.

In fact, restricting attention to the null results that did get written up in Franco, Malhotra, and Simonovits (2014), they were published at about the same rate as positive results. One interpretation of that is sometimes null results are, in fact, interesting, and so they get written up. But in those cases, publication bias isn’t really a problem, because the results face a decent shot of getting published anyway. Indeed, preprint servers aren’t really intended to be an archive for work that can’t be published in traditional outlets; instead, they are more like a parking spot for papers that are being shopped for publication in traditional outlets. Hence, the relatively small difference between effect sizes in gray literature and journals.

Preprints and p-hacking

Franco, Malhotra, and Simonovits (2014) suggest that papers in the social sciences don’t get written up and posted to a preprint server if the results don’t look publishable. Brodeur, Cook, and Heyes (2020) and Brodeur et al. (2023) provide some complementary evidence that when authors engage in p-hacking, they do it in anticipation of the challenges of publication, not in response to pushback from peer reviewers.

Brodeur, Cook, and Heyes (2020) look for the statistical fingerprints of p-hacking in economics journals versus working papers. Brodeur et al. (2023) do the same, looking at 705 submissions to the Journal of Human Resources. In both cases, they key idea is that p-hacking leaves a different statistical fingerprint than regular publication bias. Imagine a bunch of results are plotted in a scatterplot, like in the left-most figure below, with effect sizes on the horizontal axis and the imprecision of the estimate on the vertical axis. Everything in the red triangle is not statistically significantly distinguishable from zero (the red zone widens as you go up, because as imprecision gets larger, larger and larger estimates could actually be consistent with the true effect size being zero).

In the middle figure, we illustrate the statistical footprint of publication bias: all the results in the red triangle just disappear, because they don’t get published. (I talked about how to detect his here) In the right figure, we have the statistical footprint of p-hacking. Instead of disappearing, all the effects in the red triangle get perturbed again and again until they lie outside the red triangle. This leads to a suspicious pile of results that just (barely) happen to be statistically significant and therefore publishable.

Brodeur, Cook, and Heyes (2020) and Brodeur et al. (2023) look for this kind of suspicious pileup right above the conventional thresholds for statistical significance. The set of four figures below plot the distribution of two kinds of test statistics found in various samples of economics papers. The top row, from Brodeur, Cook, and Heyes (2020) plot the distribution of something called a z-statistic, which divides a normalized version of the effect size by an estimate of precision. A big z-statistic is associated with a precisely estimated effect that is large - those are places where we can be most confident the true effect is not actually zero. A small z-statistic is a small and very imprecisely estimated effect size; those are places where we worry a lot that the true effect is actually zero and we’re just observing noise. The bottom row, from Brodeur et al. (2023) plots a closely related statistic, a p-value, which is (colloquially) the probability a given set of data would arise simply by chance, if there is no genuine effect out there.

Top row from Brodeur, Cook, and Heyes (2020), bottom row from Brodeur et al. (2023)

There are two interesting things we can read off this figure. First, we look to see if there is a suspicious pileup right above (for z-statistics, so top row) or below (for p-values, so bottom row) important thresholds. Those thresholds are indicated by vertical lines and each distribution shows spikes of test statistics just barely in the statistically significant range. In other words, lots of papers just happen to be finding with results that are barely statistically significant by conventional standards.

The second interesting thing relates to the similarity of these patterns across the four figures. In the top-right, we have the distribution of test-statistics from papers published in top 25 economics journals in 2015 and 2018. In the top-left, Brodeur and coauthors go back and identify published pre-print versions of these papers and do the same analysis. For the purposes of the current discussion, the main point is that this anomalous distribution of test statistic results is already there in the working paper stage. If we interpret this as evidence of p-hacking, it’s telling us that researchers don’t do it when reviewers complain - they do it before they even submit to reviewers.

A limitation of the top row is that we don’t actually see how peer review affects what gets published. We started with the set of published papers, and then looked back to see what those papers looked like when they were just working papers. But we don’t know if the stuff that wasn’t published was better or worse, in terms of evidence for p-hacking.

That’s where the second row comes in. Although it’s a more limited sample, in the bottom left we now have a large sample of papers that were submitted to one particular journal. In the bottom right, we have the papers that ended up being published. Again, there’s not a large difference between the two. It’s not really the case that economists submit papers without much evidence of p-hacking but then peer reviewers only publish the stuff that exhibits signs of p-hacking. If it’s there, it’s there from the start.

(Aside - Brodeur et al. 2023 actually finds some evidence that editors are a bit more likely to desk reject papers with results that are just barely statistically significant, while peer reviewers display the opposite tendency. The two effects seem to mostly wash out. For more on the relative merits of accountable individual decision-makers, such as editors, relative to peer review, see Can taste beat peer review?)

What’s the credit for a preprint?

Now, before we draw strong conclusions about the efficacy of preprint servers for reducing publication bias, we should pause to note that a journal may provide professional credit that a preprint does not. The earlier discussion of papers that find null results are often never even written up suggests it might not be worth writing up a draft merely to post it on a preprint server. But it might be worth writing up a draft if it would result in a peer reviewed journal article. You can at least list that on your CV under published and peer reviewed papers, and maybe it helps you get tenure.

Maybe that’s enough to pull null results out of the file drawer and into the public domain. But in science, a successful publication is not just one that gets published - it’s one that is influential. One proxy we can use for that is the number of citations received. And when Fanelli, Costas, and Ioannidis look at the impact of citations on bias, they find the same kind of effect as they do for publication. More cited work tends to exhibit bigger effect sizes, though again, the overall effect is pretty small.

Change methodologies or publication outlet?

Taken all together, I suspect the opportunity to publish null results somewhere won’t make a huge difference to the prevalence of p-hacking, so long as researchers continue trying to make big and bold discoveries. That kind of ambition seems likely to always provide an incentive to abandon projects that seem unlikely to deliver those kinds of results, and also to draw researchers to methods that seem to deliver them. But that doesn’t mean it’s hopeless.

Brodeur, Cook, and Heyes’ results on p-hacking in economics also found something else quite interesting. It turns out, the extent of p-hacking varies a lot by methodology. Check out the following figure, which tries to measure how the extent of p-hacking in different methods.

From Brodeur, Cook, and Heyes (2020)

Whereas there’s no significant difference between preprints and publications, there are quite large differences among economic methods. Brodeur, Cook, and Heyes argue something like 16% of statistically insignificant results in papers using instrumental variables are shifted into the significant range (upper right figure), but only 1.5% of insignificant results using randomized control trials are (lower left).

That suggests methodological changes might be able to make a big dent in some of these problems. That, in turn, is notable because publication bias may itself be partially driven by the reliability of empirical methods (see Why is publication bias worse in some disciplines than in others for more on this).

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Articles Cited

Frankel, Alexander, and Maximilian Kasy. Forthcoming. Which Findings Should be Published? American Economic Journal: Microeconomics

Baumann, Alexandra, and Klaus Wohlrabe. 2020. Where have all the working papers gone? Evidence from four major economics working paper series. Scientometrics 124: 2433-2441.

Larivière, Vincent, Cassidy R. Sugimoto, Benoit Macaluso, Staša Milojević, Blaise Cronin, and Mike Thelwall. 2014. arXiv E-prings and the journal of record: An analysis of roles and relationships. Journal of the Association for Information Science and Technology 65(6): 1157-1169.

Tsunoda, Hiroyuki, Yuan Sun, Masaki Nishizawa, Xiaomin Liu, and Kou Amano. 2020. The influence of bioRxiv on PLOS ONE’s peer-review and acceptance time. Proceedings of the Association for Information Science and Technology 57(1) e398.

Fanelli, Daniele, Rodrigo Costas, and John P. A. Ioannidis. 2017. Meta-assessment of bias in science. PNAS 114(14): 3714-3719.

Franco, Annie, Neil Malhotra, and Gabor Simonovits. 2014. Publication bias in the social sciences: Unlocking the file drawer. Science 345(6203): 1502-1505.

Brodeur, Abel, Scott E. Carrell, David N. Figlio, and Lester R. Lusher. 2023. Unpacking p-hacking and Publication Bias. NBER Working Paper 31548.

Brodeur, Abel, Nikolai Cook, and Anthony Heyes. 2020. Methods Matter: p-hacking and publication bias in causal analysis in economics. American Economic Review 110(11): 3634-60.

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