Tag: replication

A null replication in press at Psych Science – anxious attachment and sensitivity to temperature cues

Published on by Sanjay Srivastava2 Comments

Etienne LeBel writes:

My colleague [Lorne Campbell] and I just got a paper accepted at Psych Science that reports on the outcome of two strict direct replications where we  worked very closely with the original author to have all methodological design specifications as similar as those in the original study (and unfortunately did not reproduce the original finding). 

We believe this is an important achievement for the “replication movement” because it shows that (a) attitudes are changing at the journal level with regard to rewarding direct replication efforts (to our knowledge this is the first strictly direct replications to be published at a top journal like Psych Science [JPSP eventually published large-scale failed direct replications of Bem’s ESP findings, but this was of course a special case]) and (b) that direct replication endeavors can contribute new knowledge concerning a theoretical idea while maintaining a cordial, non-adversarial atmosphere with the original author. We really want to emphasize this point the most to encourage other researchers to engage in similar direct replication efforts. Science should first and foremost be about the ideas rather than the people behind the ideas; we’re hoping that examples like ours will sensibilize people to a more functional research culture where it is OK and completely normal for ideas to be revised given new evidence.

An important achievement indeed. The original paper was published in Psychological Science too, so it is especially good to see the journal owning the replication attempt. And hats off to LeBel and Campbell for taking this on. Someday direct replications will hopefully be more normal, but in world we currently live in it takes some gumption to go out and try one.

I also appreciated the very fact-focused and evenhanded tone of the writeup. If I can quibble, I would have ideally liked to see a statistical test contrasting their effect against the original one – testing the hypothesis that the replication result is different from the original result. I am sure it would have been significant, and it would have been preferable over comparing the original paper’s significant rejection of the null versus the replications non-significant test against the null. But that’s a small thing compared to what a large step forward this is.

Now let’s see what happens with all those other null replications of studies about relationships and physical warmth.

Reflections on a foray into post-publication peer review

Published on by Sanjay Srivastava6 Comments

Recently I posted a comment on a PLOS ONE article for the first time. As someone who had a decent chunk of his career before post-publication peer review came along — and has an even larger chunk of his career left with it around — it was an interesting experience.

It started when a colleague posted an article to his Facebook wall. I followed the link out of curiosity about the subject matter, but what immediately jumped out at me was that it was a 4-study sequence with pretty small samples. (See Uli Schimmack’s excellent article The ironic effect of significant results on the credibility of multiple-study articles [pdf] for why that’s noteworthy.) That got me curious about effect sizes and power, so I looked a little bit more closely and noticed some odd things. Like that different N’s were reported in the abstract and the method section. And when I calculated effect sizes from the reported means and SDs, some of them were enormous. Like Cohen’s d > 3.0 level of enormous. (If all this sounds a little hazy, it’s because my goal in this post is to talk about my experience of engaging in post-publication review — not to rehash the details. You can follow the links to the article and comments for those.)

In the old days of publishing, it wouldn’t have been clear what to do next. In principle many psych journals will publish letters and comments, but in practice they’re exceedingly rare. Another alternative would have been to contact the authors and ask them to write a correction. But that relies on the authors agreeing that there’s a mistake, which authors don’t always do. And even if authors agree and write up a correction, it might be months before it appears in print.

But this article was published in PLOS ONE, which lets readers post comments on articles as a form of post-publication peer-review (PPPR). These comments aren’t just like comments on some random website or blog — they become part of the published scientific record, linked from the primary journal article. I’m all in favor of that kind of system. But it brought up a few interesting issues for how to navigate the new world of scientific publishing and commentary.

1. Professional etiquette. Here and there in my professional development I’ve caught bits and pieces of a set of gentleman’s rules about scientific discourse (and yes, I am using the gendered expression advisedly). A big one is, don’t make a fellow scientist look bad. Unless you want to go to war (and then there are rules for that too). So the old-fashioned thing to do — “the way I was raised” — would be to contact the authors quietly and petition them to make a correction themselves, so it could look like it originated with them. And if they do nothing, probably limit my comments to grumbling at the hotel bar at the next conference.

But for PPPR to work, the etiquette of “anything public is war” has to go out the window. Scientists commenting on each other’s work needs to be a routine and unremarkable part of scientific discourse. So does an understanding that even good scientists can make mistakes. And to live by the old norms is to affirm them. (Plus, the authors chose to submit to a journal that allows public comments, so caveat author.) So I elected to post a comment and then email the authors to let them know, so they would have a chance to respond quickly if they weren’t monitoring the comments. As a result, the authors posted several comments over the next couple of days correcting aspects of the article and explaining how the errors happened. And they were very responsive and cordial over email the entire time. Score one for the new etiquette.

2. A failure of pre-publication peer review? Some of the issues I raised in my comment were indisputable factual inconsistencies — like that the sample sizes were reported differently in different parts of the paper. Others were more inferential — like that a string of significant results in these 4 studies was significantly improbable, even under a reasonable expectation of an effect size consistent with the authors’ own hypothesis. A reviewer might disagree about that (maybe they think the true effect really is gigantic). Other issues, like the too-small SDs, would have been somewhere in the middle, though they turned out to be errors after all.

Is this a mark against pre-publication peer review? Obviously it’s hard to say from one case, but I don’t think it speaks well of PLOS ONE that these errors got through. Especially because PLOS ONE is supposed to emphasize “a high technical standard” and reporting of “sufficient detail” (the reason I noticed the issue with the SDs was because the article did not report effect sizes).

But this doesn’t necessarily make PLOS ONE worse than traditional journals like Psychological Science or JPSP, where similar errors get through all the time and then become almost impossible to correct. [UPDATE: Please see my followup post about pre-publication review at PLOS ONE and other journals.]

3. The inconsistency of post-publication peer review. I don’t think post-publication peer review is a cure-all. This whole episode depended in somebody (in this case, me) noticing the anomalies and being motivated to post a comment about them. If we got rid of pre-publication peer review and if the review process remained that unsystematic, it would be a recipe for a very biased system. This article’s conclusions are flattering to most scientists’ prejudices, and press coverage of the article has gotten a lot of mentions and “hell yeah”s on Twitter from pro-science folks. I don’t think it’s hard to imagine that that contributed to it getting a pass, and that if the opposite were true the article would have gotten a lot more scrutiny both pre- and post-publication. In my mind, the fix would be to make sure that all articles get a decent pre-publication review — not to scrap it altogether. Post-publication review is an important new development but should be an addition, not a replacement.

4. Where to stop? Finally, one issue I faced was how much to say in my initial comment, and how much to follow up. In particular, my original comment made a point about the low power and thus the improbability of a string of 4 studies with a rejected null. I based that on some hypotheticals and assumptions rather than formally calculating Schimmack’s incredibility index for the paper, in part because other errors in the initial draft made that impossible. The authors never responded to that particular point, but their corrections would have made it possible to calculate an IC index. So I could have come back and tried to goad them into a response. But I decided to let it go. I don’t have an axe to grind, and my initial comment is now part of the record. And one nice thing about PPPR is that readers can evaluate the arguments for themselves. (I do wish I had cited Schimmack’s paper though, because more people should know about it.)

The PoPS replication reports format is a good start

Published on by Sanjay Srivastava

Big news today is that Perspectives on Psychological Science is going to start publishing pre-registered replication reports. The inaugural editors will be Daniel Simons and Alex Holcombe, who have done the serious legwork to make this happen. See the official announcement and blog posts by Ed Yong and Melanie Tannenbaum. (Note: this isn’t the same as the earlier plan I wrote about for Psychological Science to publish replications, but it appears to be related.)

The gist of the plan is that after getting pre-approval from the editors (mainly to filter for important but as-yet unreplicated studies), proposers will create a detailed protocol. The original authors (and maybe other reviewers?) will have a chance to review the protocol. Once it has been approved, the proposer and other interested labs will run the study. Publication will be contingent on carrying out the protocol but not on the results. Collections of replications from multiple labs will be published together as final reports.

I think this is great news. In my ideal world published replications would be more routine, and wouldn’t require all the hoopla of prior review by original authors, multiple independent replications packaged together, etc. etc. In other words, they shouldn’t be extraordinary, and they should be as easy or easier to publish than original research. I also think every journal should take responsibility for replications of its own original reports (the Pottery Barn rule). BUT… this new format doesn’t preclude any of that from also happening elsewhere. By including all of those extras, PoPS replication reports might function as a first-tier, gold standard of replication. And by doing a lot of things right (such as focusing on effect sizes rather than tallying “successful” and “failed” replications, which is problematic) they might set an example for more mundane replication reports in other outlets.

This won’t solve everything — not by a long shot. We need to change scientific culture (by which I mean institutional incentives) so that replication is a more common and more valued activity. We need funding agencies to see it that way too. In a painful coincidence, news came out today that a cognitive neuroscientist admitted to misconduct in published research. One of the many things that commonplace replications would do would be to catch or prevent fraud. But whenever I’ve asked colleagues who use fMRI whether people in their fields run direct replications, they’ve just laughed at me. There’s little incentive to run them and no money to do it even if you wanted to. All of that needs to change across many areas of science.

But you can’t solve everything at once, and the PoPS initiative is an important step forward.

What is the Dutch word for “irony?”

Published on by Sanjay Srivastava6 Comments

Breathless headline-grabbing press releases based on modest findings. Investigations driven by confirmation bias. Broad generalizations based on tiny samples.

I am talking, of course, about the final report of the Diederik Stapel investigation.

Regular readers of my blog will know that I have been beating the drum for reform for quite a while. I absolutely think psychology in general, and perhaps social psychology especially, can and must work to improve its methods and practices.

But in reading the commission’s press release, which talks about “a general culture of careless, selective and uncritical handling of research and data” in social psychology, I am struck that those conclusions are based on a retrospective review of a known fraud case — a case that the commissions were specifically charged with finding an explanation for. So when they wag their fingers about a field rife with elementary statistical errors and confirmation bias, it’s a bit much for me.

I am writing this as a first reaction based on what I’ve seen in the press. At some point when I have the time and the stomach I plan to dig into the full 100-page commission report. I hope that — as is often the case when you go from a press release to an actual report — it takes a more sober and cautious tone. Because I do think that we have the potential to learn some important things by studying how Diederik Stapel did what he did. Most likely we will learn what kinds of hard questions we need to be asking of ourselves — not necessarily what the answers to those questions will be. Remember that the more we are shocked by the commission’s report, the less willing we should be to reach any sweeping generalizations from it.

So let’s all take a deep breath, face up to the Stapel case for what it is — neither exaggerating nor minimizing it — and then try to have a productive conversation about where we need to go next.

Psychological Science to publish direct replications (maybe)

Published on by Sanjay Srivastava2 Comments

Pretty big news. Psychological Science is seriously discussing 3 new reform initiatives. They are outlined in a letter being circulated by Eric Eich, editor of the journal, and they come from a working group that includes top people from APS and several other scientists who have been active in working for reforms.

After reading it through (which I encourage everybody to do), here are my initial takes on the 3 initiatives:

Initiative 1: Create tutorials on power, effect size, and confidence intervals. There’s plenty of stuff out there already, but if PSci creates a good new source and funnels authors to it, it could be a good thing.

Initiative 2: Disclosure statements about research process (such as how sample size was determined, unreported measures, etc.) This could end up being a good thing, but it will be complicated. Simine Vazire, one of the working group members who is quoted in the proposal, puts it well:

We are essentially asking people to “incriminate” themselves — i.e., reveal information that, in the past, editors have treated as reasons not to publish a paper. If we want authors to be honest, I think they will want some explicit acknowledgement that some degree of messiness (e.g., a null result here and there) will be tolerated and perhaps even treated as evidence that the entire set of findings is even more plausible (a la [Gregory] Francis, [Uli] Schimmack, etc.).

I bet there would be low consensus about what kinds and amounts of messiness are okay, because no one is accustomed to seeing that kind of information on a large scale in other people’s studies. It is also the case that things that are problematic in one subfield may be more reasonable in another. And reviewers and editors who lack the time or local expertise to really judge messiness against merit may fall back on simplistic heuristics rather than thinking things through in a principled way. (Any psychologist who has ever tried to say anything about causation, however tentative and appropriately bounded, in data that was not from a randomized experiment probably knows what that feels like.)

Another basic issue is whether people will be uniformly honest in the disclosure statements. I’d like to believe so, but without a plan for real accountability I’m not sure. If some people can get away with fudging the truth, the honest ones will be at a disadvantage.

3. A special submission track for direct replications, with 2 dedicated Associate Editors and a system of pre-registration and prior review of protocols to allow publication decisions to be decoupled from outcomes. A replication section at a journal? If you’ve read my blog before you might guess that I like that idea a lot.

The section would be dedicated to studies previously published in Psychological Science, so in that sense it is in the same spirit as the Pottery Barn Rule. The pre-registration component sounds interesting — by putting a substantial amount of review in place before data are collected, it helps avoid the problem of replications getting suppressed because people don’t like the outcomes.

I feel mixed about another aspect of the proposal, limiting replications to “qualified” scientists. There does need to be some vetting, but my hope is that they will set the bar reasonably low. “This paradigm requires special technical knowledge” can too easily be cover for “only people who share our biases are allowed to study this effect.” My preference would be for a pro-data, pro-transparency philosophy. Make it easy for for lots of scientists to run and publish replication studies, and make sure the replication reports include information about the replicating researchers’ expertise and experience with the techniques, methods, etc. Then meta-analysts can code for the replicating lab’s expertise as a moderator variable, and actually test how much expertise matters.

My big-picture take. Retraction Watch just reported yesterday on a study showing that retractions, especially retractions due to misconduct, cause promising scientists to move to other fields and funding agencies to direct dollars elsewhere. Between alleged fraud cases like Stapel, Smeesters, and Sanna, and all the attention going to false-positive psychology and questionable research practices, psychology (and especially social psychology) is almost certainly at risk of a loss of talent and money.

Getting one of psychology’s top journals to make real reforms, with the institutional backing of APS, would go a long way to counteract those negative effects. A replication desk in particular would leapfrog psychology past what a lot of other scientific fields do. Huge credit goes to Eric Eich and everyone else at APS and the working group for trying to make real reforms happen. It stands a real chance of making our science better and improving our credibility.

What counts as a successful or failed replication?

Published on by Sanjay Srivastava

Let’s say that some theory states that people in psychological state A1 will engage in behavior B more than people in psychological state A2. Suppose that, a priori, the theory allows us to make this directional prediction, but not a prediction about the size of the effect.

A researcher designs an experiment — call this Study 1 — in which she manipulates A1 versus A2 and then measures B. Consistent with the theory, the result of Study 1 shows more of behavior B in condition A1 than A2. The effect size is d=0.8 (a large effect). A null hypothesis significance test shows that the effect is significantly different from zero, p<.05.

Now Researcher #2 comes along and conducts Study 2. The procedures of Study 2 copy Study 1 as closely as possible — the same manipulation of A, the same measure of B, etc. The result of Study 2 shows more of behavior B in condition A1 than in A2 — same direction as Study 1. In Study 2, the effect size is d=0.3 (a smallish effect). A null hypothesis significance test shows that the effect is significantly different from zero, p<.05. But a comparison of the Study 1 effect to the Study 2 effect (d=0.8 versus d=0.3) is also significant, p<.05.

Here’s the question: did Study 2 successfully replicate Study 1?

My answer is no. Here’s why. When we say “replication,” we should be talking about whether we can reproduce a result. A statistical comparison of Studies 1 and 2 shows that they gave us significantly different results. We should be bothered by the difference, and we should be trying to figure out why.

People who would call Study 2 a “successful” replication of Study 1 are focused on what it means for the theory. The theoretical statement that inspired the first study only spoke about direction, and both results came out in the same direction. By that standard you could say that it replicated.

But I have two problems with defining replication in that way. My first problem is that, after learning the results of Study 1, we had grounds to refine the theory to include statements about the likely range of the effect’s size, not just its direction. Those refinements might be provisional, and they might be contingent on particular conditions (i.e., the experimental conditions under which Study 1 was conducted), but we can and should still make them. So Study 2 should have had a different hypothesis, a more focused one, than Study 1. Theories should be living things, changing every time they encounter new data. If we define replication as testing the theory twice then there can be no replication, because the theory is always changing.

My second problem is that we should always be putting theoretical statements to multiple tests. That should be such normal behavior in science that we shouldn’t dilute the term “replication” by including every possible way of doing it. As Michael Shermer once wrote, “Proof is derived through a convergence of evidence from numerous lines of inquiry — multiple, independent inductions all of which point to an unmistakable conclusion.” We should all be working toward that goal all the time.

This distinction — between empirical results vs. conclusions about theories — goes to the heart of the discussion about direct and conceptual replication. Direct replication means that you reproduce, as faithfully as possible, the procedures and conditions of the original study. So the focus should rightly be on the result. If you get a different result, it either means that despite your best efforts something important differed between the two studies, or that one of the results was an accident.

By contrast, when people say “conceptual replication” they mean that they have deliberately changed one or more major parts of the study — like different methods, different populations, etc. Theories are abstractions, and in a “conceptual replication” you are testing whether the abstract theoretical statement (in this case, B|A1 > B|A2) is still true under a novel concrete realization of the theory. That is important scientific work, but it differs in huge, qualitative ways from true replication. As I’ve said, it’s not just a difference in empirical procedures; it’s a difference in what kind of inferences you are trying to draw (inferences about a result vs. inferences about a theoretical statement). Describing those simply as 2 varieties of the same thing (2 kinds of replication) blurs this important distinction.

I think this means a few important things for how we think about replications:

1. When judging a replication study, the correct comparison is between the original result and the new one. Even if the original study ran a significance test against a null hypothesis of zero effect, that isn’t the test that matters for the replication. There are probably many ways of making this comparison, but within the NHST framework that is familiar to most psychologists, the proper “null hypothesis” to test against is the one that states that the two studies produced the same result.

2. When we observe a difference between a replication and an original study, we should treat that difference as a problem to be solved. Not (yet) as a conclusive statement about the validity of either study. Study 2 didn’t “fail to replicate” Study 1; rather, Studies 1 and 2 produced different results when they should have produced the same, and we now need to figure out what caused that difference.

3. “Conceptual replication” should depend on a foundation of true (“direct”) replicability, not substitute for it. The logic for this is very much like how validity is strengthened by reliability. It doesn’t inspire much confidence in a theory to say that it is supported by multiple lines of evidence if all of those lines, on their own, give results of poor or unknown consistency.

Paul Meehl on replication and significance testing

Published on by Sanjay Srivastava3 Comments

Still very relevant today.

A scientific study amounts essentially to a “recipe,” telling how to prepare the same kind of cake the recipe writer did. If other competent cooks can’t bake the same kind of cake following the recipe, then there is something wrong with the recipe as described by the first cook. If they can, then, the recipe is all right, and has probative value for the theory. It is hard to avoid the thrust of the claim: If I describe my study so that you can replicate my results, and enough of you do so, it doesn’t matter whether any of us did a significance test; whereas if I describe my study in such a way that the rest of you cannot duplicate my results, others will not believe me, or use my findings to corroborate or refute a theory, even if I did reach statistical significance. So if my work is replicable, the significance test is unnecessary; if my work is not replicable, the significance test is useless. I have never heard a satisfactory reply to that powerful argument.

Meehl, P. E. (1990). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant using it. Psychological Inquiry, 1, 108-141, 173-180. [PDF]

A Pottery Barn rule for scientific journals

Published on by Sanjay Srivastava6 Comments

Proposed: Once a journal has published a study, it becomes responsible for publishing direct replications of that study. Publication is subject to editorial review of technical merit but is not dependent on outcome. Replications shall be published as brief reports in an online supplement, linked from the electronic version of the original.

*****

I wrote about this idea a year ago when JPSP refused to publish a paper that failed to replicate one of Daryl Bem’s notorious ESP studies. I discovered, immediately after writing up the blog post, that other people were thinking along similar lines. Since then I have heard versions of the idea come up here and there. And strands of it came up again in David Funder’s post on replication (“[replication] studies should, ideally, be published in the same journal that promulgated the original, misleading conclusion”) and the comments to it. When a lot of people are coming up with similar solutions to a problem, that’s probably a sign of something.

Like a lot of people, I believe that the key to improving our science is through incentives. You can finger-wag about the importance of replication all you want, but if there is nowhere to publish and no benefit for trying, you are not going to change behavior. To a large extent, the incentives for individual researchers are controlled through institutions — established journal publishers, professional societies, granting agencies, etc. So if you want to change researchers’ behavior, target those institutions.

Hence a Pottery Barn rule for journals: once you publish a study, you own its replicability (or at least a significant piece of it).

This would change the incentive structure for researchers and for journals in a few different ways. For researchers, there are currently insufficient incentives to run replications. This would give them a virtually guaranteed outlet for publishing a replication attempt. Such publications should be clearly marked on people’s CVs as brief replication reports (probably by giving the online supplement its own journal name, e.g., Journal of Personality and Social Psychology: Replication Reports). That would make it easier for the academic marketplace (like hiring and promotion committees, etc.) to reach its own valuation of such work.

I would expect that grad students would be big users of this opportunity. Others have proposed that running replications should be a standard part of graduate training (e.g., see Matt Lieberman’s idea). This would make it worth students’ while, but without the organizational overhead of Matt’s proposal. The best 1-2 combo, for grad students and PIs alike, would be to embed a direct replication in a replicate-and-extend study. Then if the “extend” part does not work out, the replication report is a fallback (hopefully with a footnote about the failed extend). And if it does, the new paper is a more cumulative contribution than the shot-in-the-dark papers we often see now.

A system like this would change the incentive structure for original studies too. Researchers would know that whatever they publish is eventually going to be linked to a list of replication attempts and their outcomes. As David pointed out, knowing that others will try to replicate your work — and in this proposal, knowing that reports of those attempts would be linked from your own paper! — would undermine the incentives to use questionable research practices far better than any heavy-handed regulatory response. (And if that list of replication attempts is empty 5 years down the road because nobody thinks it’s worth their while to replicate your stuff? That might say something too.)

What about the changed incentives for journals? One benefit would be that the increased accountability for individual researchers should lead to better quality submissions for journals that adopted this policy. That should be a big plus.

A Pottery Barn policy would also increase accountability for journals. It would become much easier to document a journal’s track record of replicability, which could become a counterweight to the relentless pursuit of impact factors. Such accountability would mean a greater emphasis on evaluating replicability during the review process — e.g., to consider statistical power, to let reviewers look at the raw data and the materials and stimuli, etc.

But sequestering replication reports into an online supplement means that the journal’s main mission can stay intact. So if a journal wants to continue to focus on groundbreaking first reports in its main section, it can continue to do so without fearing that its brand will be diluted (though I predict that it would have to accept a lower replication rate in exchange for its focus on novelty).

Replication reports would generate some editorial overhead, but not nearly as much as original reports. They could be published based directly on an editorial decision, or perhaps with a single peer reviewer. A structured reporting format like the one used at Psych File Drawer would make it easier to evaluate the replication study relative to the original. (I would add a field to describe the researchers’ technical expertise and experience with the methods, since that is a potential factor in explaining differences in results.)

Of course, journals would need an incentive to adopt the Pottery Barn rule in the first place. Competition from outlets like PLoS One (which does not consider importance/novelty in its review criteria) or Psych File Drawer (which only publishes replications) might push the traditional journals in this direction. But ultimately it is up to us scientists. If we cite replication studies, if we demand and use outlets that publish them, and if we we speak loudly enough — individually or through our professional organizations — I think the publishers will listen.

Replication, period. (A guest post by David Funder)

Published on by Sanjay Srivastava3 Comments

The following is a guest post by David Funder. David shares some of his thoughts about the best way forward through social psychology’s recent controversies over fraud and corner-cutting. David is a highly accomplished researcher with a lot of experience in the trenches of psychological science. He is also President-Elect of the Society for Personality and Social Psychology (SPSP), the main organization representing academic social psychologists — but he emphasizes that he is not writing on behalf of SPSP or its officers, and the views expressed in this essay are his own.

*****

Can we believe everything (or anything) that social psychological research tells us? Suddenly, the answer to this question seems to be in doubt. The past few months have seen a shocking series of cases of fraud –researchers literally making their data up — by prominent psychologists at prestigious universities. These revelations have catalyzed an increase in concern about a much broader issue, the replicability of results reported by social psychologists. Numerous writers are questioning common research practices such as selectively reporting only studies that “work” and ignoring relevant negative findings that arise over the course of what is euphemistically called “pre-testing,” increasing N’s or deleting subjects from data sets until the desired findings are obtained and, perhaps worst of all, being inhospitable or even hostile to replication research that could, in principle, cure all these ills.

Reaction is visible. The European Association of Personality Psychology recently held a special three-day meeting on the topic, to result in a set of published recommendations for improved research practice, a well-financed conference in Santa Barbara in October will address the “decline effect” (the mysterious tendency of research findings to fade away over time), and the President of the Society for Personality and Social Psychology was recently motivated to post a message to the membership expressing official concern. These are just three reactions that I personally happen to be familiar with; I’ve also heard that other scientific organizations and even agencies of the federal government are looking into this issue, one way or another.

This burst of concern and activity might seem to be unjustified. After all, literally making your data up is a far cry from practices such as pre-testing, selective reporting, or running multiple statistical tests. These practices are even, in many cases, useful and legitimate. So why did they suddenly come under the microscope as a result of cases of data fraud? The common thread seems to be the issue of replication. As I already mentioned, the idealistic model of healthy scientific practice is that replication is a cure for all ills. Conclusions based on fraudulent data will fail to be replicated by independent investigators, and so eventually the truth will out. And, less dramatically, conclusions based on selectively reported data or derived from other forms of quasi-cheating, such as “p-hacking,” will also fade away over time.

The problem is that, in the cases of data fraud, this model visibly and spectacularly failed. The examples that were exposed so dramatically — and led tenured professors to resign from otherwise secure and comfortable positions (note: this NEVER happens except under the most extreme circumstances) — did not come to light because of replication studies. Indeed, anecdotally — which, sadly, seems to be the only way anybody ever hears of replication studies — various researchers had noticed that they weren’t able to repeat the findings that later turned out to be fraudulent, and one of the fakers even had a reputation of generating data that were “too good to be true.” But that’s not what brought them down. Faking of data was only revealed when research collaborators with first-hand knowledge — sometimes students — reported what was going on.

This fact has to make anyone wonder: what other cases are out there? If literal faking of data is only detected when someone you work with gets upset enough to report you, then most faking will never be detected. Just about everybody I know — including the most pessimistic critics of social psychology — believes, or perhaps hopes, that such outright fraud is very rare. But grant that point and the deeper moral of the story still remains: False findings can remain unchallenged in the literature indefinitely.

Here is the bridge to the wider issue of data practices that are not outright fraudulent, but increase the risk of misleading findings making it into the literature. I will repeat: so-called “questionable” data practices are not always wrong (they just need to be questioned). For example, explorations of large, complex (and expensive) data sets deserve and even require multiple analyses to address many different questions, and interesting findings that emerge should be reported. Internal safeguards are possible, such as split-half replications or randomization analyses to assess the probability of capitalizing on chance. But the ultimate safeguard to prevent misleading findings from permanent residence in (what we think is) our corpus of psychological knowledge is independent replication. Until then, you never really know.

Many remedies are being proposed to cure the ills, or alleged ills, of modern social psychology. These include new standards for research practice (e.g., registering hypotheses in advance of data gathering), new ethical safeguards (e.g., requiring collaborators on a study to attest that they have actually seen the data), new rules for making data publicly available, and so forth. All of these proposals are well-intentioned but the specifics of their implementation are debatable, and ultimately raise the specter of over-regulation. Anybody with a grant knows about the reams of paperwork one now must mindlessly sign attesting to everything from the exact percentage of their time each graduate student has worked on your project to the status of your lab as a drug-free workplace. And that’s not even to mention the number of rules — real and imagined — enforced by the typical campus IRB to “protect” subjects from the possible harm they might suffer from filling out a few questionnaires. Are we going to add yet another layer of rules and regulations to the average over-worked, under-funded, and (pre-tenure) insecure researcher? Over-regulation always starts out well-intentioned, but can ultimately do more harm than good.

The real cure-all is replication. The best thing about replication is that it does not rely on researchers doing less (e.g., running fewer statistical tests, only examining pre-registered hypotheses, etc.), but it depends on them doing more. It is sometimes said the best remedy for false speech is more speech. In the same spirit, the best remedy for misleading research is more research.

But this research needs to be able to see the light of day. Current journal practices, especially among our most prestigious journals, discourage and sometimes even prohibit replication studies from publication. Tenure committees value novel research over solid research. Funding agencies are always looking for the next new thing — they are bored with the “same old same old” and give low priority to research that seeks to build on existing findings — much less seeks to replicate them. Even the researchers who find failures to replicate often undervalue them. I must have done something wrong, most conclude, stashing the study into the proverbial “file drawer” as an unpublishable, expensive and sad waste of time. Those researchers who do become convinced that, in fact, an accepted finding is wrong, are unlikely to attempt to publish this conclusion. Instead, the failure becomes fodder for late-night conversations, fueled by beverages at hotel bars during scientific conferences. There, and pretty much only there, can you find out which famous findings are the ones that “everybody knows” can’t be replicated.

I am not arguing that every replication study must be published. Editors have to use their judgment. Pages really are limited (though less so in the arriving age of electronic publishing) and, more importantly, editors have a responsibility to direct the limited attentional resources of the research community to articles that matter. So any replication study should be carefully evaluated for the skill with which it was conducted, the appropriate level of statistical power, and the overall importance of the conclusion. For example, a solid set of high-powered studies showing that a widely accepted and consequential conclusion was dead wrong, would be important in my book. (So would a series of studies confirming that an important surprising and counter-intuitive finding was actually true. But most aren’t, I suspect.) And this series of studies should, ideally, be published in the same journal that promulgated the original, misleading conclusion. As your mother always said, clean up your own mess.

Other writers have recently laid out interesting, ambitious, and complex plans for reforming psychological research, and even have offered visions of a “research utopia.” I am not doing that here. I only seek to convince you of one point: psychology (and probably all of science) needs more replications. Simply not ruling replication studies as inadmissible out-of-hand would be an encouraging start. Do I ask too much?

Some reflections on the Bargh-Doyen elderly walking priming brouhaha

Published on by Sanjay Srivastava4 Comments

Recently a controversy broke out over the replicability of a study John Bargh et al. published in 1996. The study reported that unconsciously priming a stereotype of elderly people caused subjects to walk more slowly. A recent replication attempt by Stephane Doyen et al., published in PLoS ONE, was unable to reproduce the results. (Less publicized, but surely relevant, is another non-replication by Hal Pashler et al.) Ed Yong wrote up an article about it  in Discover, which last week drew a sharp response from Bargh.

The broader context is that there has been a large and ongoing discussion about replication in psychology (i.e., that there isn’t enough of it). I don’t have much to say about whether the elderly-walking effect is real. But this controversy has raised a number of issues about scientific discourse online as well as about how we think about replication.

The discussion has been unnecessarily inflammatory – on all sides. Bargh has drawn a lot of criticism for his response, which among other things included factual errors about PLoS ONE, suggestions that Doyen et al. were “incompetent or ill-informed,” and a claim that Yong was practicing irresponsible journalism. The PLoS ONE editors posted a strongly worded but civil response in the comments, and Yong has written a rebuttal. As for the scientific issue — is the elderly-priming effect real? — Daniel Simons has written an excellent post on the many, many reasons why an effect might fail to replicate. A failure to replicate does not need to impeach the honesty or scientific skills of either the original researcher or the replicator. It does not even mean the effect is not real. In an ideal world, Bargh should have treated the difference between his results and those of Doyen et al. as a puzzle to be worked out, not as a personal attack to be responded to in kind.

But… it’s not as though Bargh went bananas over a dispassionate report of a non-replication. Doyen et al. strongly suggested that Bargh et al.’s procedure had been contaminated by expectancy effects. Since expectancy effects are widely known about in behavioral science (raise your hand if you have heard the phrase “double-blind”), the implication was that Bargh had been uncareful. And Ed Yong ran with that interpretation by leading off his original piece with the tale of Clever Hans. I don’t know whether Doyen or Yong meant to be inflammatory: I know nothing about Doyen; and in Yong’s case, based on his journalistic record, I doubt it (and he apparently gave Bargh plenty of opportunity to weigh in before his original post went live). But wherever you place the blame, a scientifically unfortunate result is that all of the other reasonable possibilities that Simons lists have been mostly ignored by the principals in this discussion.

Are priming effects hard to produce or easy? A number of priming researchers have suggested that priming effects are hard to get reliably. This doesn’t mean they aren’t important — experiments require isolation of the effect of interest, and the ease of isolating a phenomenon is not the same thing as its importance. (Those Higgs bosons are so hard to detect — so even if they exist they must not matter, right?) Bargh makes this point in his response too, suggesting that if Doyen et al. accidentally called subjects’ conscious attention to the elderly stereotype, that could wash out the effect (because conscious attention can easily interfere with automatic processes).

That being said… the effects in the original Bargh et al. report were big. Really big, by psychology standards. In experiment 2a, Bargh et al. report t(28) = 2.86, which corresponds to an effect size of d = 1.08. And in their replication, experiment 2b, they report t(28) = 2.16, which translates to d = 0.82. So even if we account for some shrinkage, under the right conditions it should not be hard for somebody to reproduce the elderly-walking priming effect in a new study.

The expectancy effects study is rhetorically powerful but proves little. In their Experiment 1, Doyen et al. tested the same hypothesis about priming stereotypes that Bargh tested. But in Experiment 2, Doyen et al. tested a hypothesis about experimenter expectancies. That is a completely different hypothesis. The second study tells us that experimenter expectancies can affect walking speed. But walking speed surely can be affected by more than one thing. So Experiment 2 does not tell us to what extent, if any at all, differences in walking speed were caused by experimenter expectancies in Bargh’s experiment (or for that matter, anywhere else in the natural world outside of Doyen’s lab). This is the inferential error of confusing causes of effects with effects of causes. Imagine that Doyen et al. had clubbed the subjects in the elderly-prime condition in the knee; most likely that would have slowed them down. But would we take that as evidence that Bargh et al. had done the same?

The inclusion of Experiment 2 served a strong rhetorical function, by planting in the audience’s mind the idea that the difference between Bargh vs Doyen Exp 1 was due to expectancy effects (and Ed Yong picked up and ran with this suggestion by referring to Clever Hans). But scientifically, all it shows is that expectancy effects can influence the dependent variable in the Bargh experiment. That’s not nothing, but anybody who already believes that experiments need to be double-blind should have seen that coming. If we had documentary evidence that in the actual 1996 studies Bargh et al. did not actually eliminate expectancy effects, that would be relevant. (We likely never will have such evidence; see next point.) But Experiment 2 does not shed nearly as much light as it appears to.

We need more openness with methods and materials. When I started off in psychology, someone once told me that a scientific journal article should contain everything you need to reproduce the experiment (either directly or via references to other published materials). That, of course, is almost never true and maybe is unrealistic. Especially when you factor in things like lab skills, many of which are taught via direct apprenticeship rather than in writing, and which matter just as much in behavioral experiments as they do in more technology-heavy areas of science.

But with all that being said, I think we could do a lot better. A big part of the confusion in this controversy is over the details of methods — what exactly did Bargh et al. do in the original study, and how closely did Doyen et al. reproduce the procedure? The original Bargh et al. article followed the standards of its day in how much methodological detail it reported. Bargh later wrote a methods chapter that described more details of the priming technique (and which he claims Doyen et al. did not follow). But in this era of unlimited online supplements, there is no reason why in future studies, all of the stimuli, instructions, etc. could not be posted. That would enormously aid replication attempts.

What makes for a “failed” replication? This turns out to be a small point in the present context but an important one in a more general sense, so I couldn’t help but make it. We should be very careful about the language of “successful” and “failed” replications when it is based on the difference between p<.05 and p>.05. That is, just because the original study could reject the null and the replication could not, that doesn’t mean that the replication is significantly different from the original study. If you are going to say you failed to replicate the original result, you should conduct a test of that difference.

As far as I can tell neither Doyen et al. nor Pashler et al. did that. So I did. I converted each study’s effect to an r effect size and then comparing the studies with a z test of the difference between independent rs, and indeed Doyen et al. and Pashler et al. each differed from Bargh’s original experiments. So this doesn’t alter the present discussion. But as good practice, the replication reports should have reported such tests.