Reflections on SIPS (guest post by Neil Lewis, Jr.)

The following is a guest post by Neil Lewis, Jr. Neil is an assistant professor at Cornell University.

Last week I visited the Center for Open Science in Charlottesville, Virginia to participate in the second annual meeting of the Society for the Improvement of Psychological Science (SIPS). It was my first time going to SIPS, and I didn’t really know what to expect. The structure was unlike any other conference I’ve been to—it had very little formal structure—there were a few talks and workshops here and there, but the vast majority of the time was devoted to “hackathons” and “unconference” sessions where people got together and worked on addressing pressing issues in the field: making journals more transparent, designing syllabi for research methods courses, forming a new journal, changing departmental/university culture to reward open science practices, making open science more diverse and inclusive, and much more. Participants were free to work on whatever issues we wanted to and to set our own goals, timelines, and strategies for achieving those goals.

I spent most of the first two days at the diversity and inclusion hackathon that Sanjay and I co-organized. These sessions blew me away. Maybe we’re a little cynical, but going into the conference we thought maybe two or three people would stop by and thus it would essentially be the two of us trying to figure out what to do to make open science more diverse and inclusive. Instead, we had almost 40 people come and spend the first day identifying barriers to diversity and inclusion, and developing tools to address those barriers. We had sub-teams working on (1) improving measurement of diversity statistics (hard to know how much of a diversity problem one has if there’s poor measurement), (2) figuring out methods to assist those who study hard-to-reach populations, (3) articulating the benefits of open science and resources to get started for those who are new, (4) leveraging social media for mentorship on open science practices, and (5) developing materials to help PIs and institutions more broadly recruit and retain traditionally underrepresented students/scholars. Although we’re not finished, each team made substantial headway in each of these areas.

On the second day, those teams continued working, but in addition we had a “re-hack” that allowed teams that were working on other topics (e.g., developing research methods syllabi, developing guidelines for reviewers, starting a new academic journal) to present their ideas and get feedback on how to make their projects/products more inclusive from the very beginning (rather than having diversity and inclusion be an afterthought as is often the case). Once again, it was inspiring to see how committed people were to making sure so many dimensions of our science become more inclusive.

These sessions, and so many others at the conference, gave me a lot of hope for the field—hope that I (and I suspect others) could really use (special shout-outs to Jessica Flake’s unconference on improving measurement, Daniel Lakens and Jeremy Biesanz’s workshop on sample size and effect size, and Liz Page-Gould and Alex Danvers’s workshop on Fundamentals of R for data analysis). It’s been a tough few years to be a scientist. I was working on my PhD in social psychology at the time that the open science collaborative published their report estimating the reproducibility of psychological science to be somewhere between one-third and one-half. Then a similar report came out about the state of cancer research – only twenty five percent of papers replicated there. Now it seems like at least once a month there is some new failed replication study or some other study comes out that has major methodological flaw(s). As someone just starting out, constantly seeing findings I learned were fundamental fail to replicate, and new work emerge so flawed, I often find myself wondering (a) what the hell do we actually know, and (b) if so many others can’t get it right, what chance do I have?

Many Big Challenges with No Easy Solutions

To try and minimize future fuck-ups in my own work, I started following a lot of methodologists on Twitter so that I could stay in the loop on what I need to do to get things right (or at least not horribly wrong). There are a lot of proposed solutions out there (and some argument about those solutions, e.g., p < .005) but there are some big ones that seem to have reached consensus, including vastly increasing the size of our samples to increase the reliability of findings. These solutions make sense for addressing the issues that got us to this point, but the more I’ve thought about and talked to others about them, the more it became clear that some may unintentionally create another problem along the way, which is to “crowd out” some research questions and researchers. For example, when talking with scholars who study hard-to-reach populations (e.g., racial and sexual minorities), a frequently voiced concern is that it is nearly impossible to recruit the sample sizes needed to meet new thresholds of evidence.

To provide an example from my own research, I went to graduate school intending to study racial-ethnic disparities in academic outcomes (particularly Black-White achievement gaps). In my first semester at the University of Michigan I asked my advisor to pay for a pre-screen of the department of psychology’s participant pool to see how many Black students I would have to work with if I pursued that line of research. There were 42 Black students in the pool that semester. Forty-two. Out of 1,157. If memory serves me well, that was actually one of the highest concentrations of Black students in the pool in my entire time there. Seeing that, I asked others who study racial minorities what they did. I learned that unless they had well-funded advisors that could afford to pay for their samples, many either shifted their research questions to topics that were more feasible to study, or they would spend their graduate careers collecting data for one or two studies. In my area, that latter approach was not practical for being employable—professional development courses taught us that search committees expect multiple publications in the flagship journals, and those flagship journals usually require multiple studies for publication.

Learning about those dynamics, I temporarily shifted my research away from racial disparities until I figured out how to feasibly study those topics. In the interim, I studied other topics where I could recruit enough people to do the multi-study papers that were expected. That is not to say I am uninterested in those other topics I studied (I very much am) but disparities were what interested me most. Now, some may read that and think ‘Neil, that’s so careerist of you! You should have pursued the questions you were most passionate about, regardless of how long it took!’ And on an idealistic level, I agree with those people. But on a practical level—I have to keep a roof over my head and eat. There was no safety net at home if I was unable to get a job at the end of the program. So I played it safe for a few years before going back to the central questions that brought me to academia in the first place.

That was my solution. Others left altogether. As one friend depressingly put it—“there’s no more room for people like us; unless we get lucky with the big grants that are harder and harder to get, we can’t ask our questions—not when power analyses now say we need hundreds per cell; we’ve been priced out of the market.” And they’re not entirely wrong. Some collaborators and I recently ran a survey experiment with Black American participants; it was a 20-minute survey with 500 Black Americans. That one study cost us $11,000. Oh, and it’s a study for a paper that requires multiple studies. The only reason we can do this project is because we have a senior faculty collaborator who has an endowed chair and hence deep research pockets.

So that is the state of affairs. The goal post keeps shifting, and it seems that those of us who already had difficulty asking our questions have to choose between pursuing the questions we’re interested in, and pursuing questions that are practical for keeping roofs over our heads (e.g., questions that can be answered for $0.50 per participant on MTurk). And for a long time this has been discouraging because it felt as though those who have been leading the charge on research reform did not care. An example that reinforces this sentiment is a quote that floated around Twitter just last week. A researcher giving a talk at a conference said “if you’re running experiments with low sample n, you’re wasting your time. Not enough money? That’s not my problem.”

That researcher is not wrong. For all the reasons methodologists have been writing about for the past few years (and really, past few decades), issues like small sample sizes do compromise the integrity of our findings. At the same time, I can’t help but wonder about what we lose when the discussion stops there, at “that’s not my problem.” He’s right—it’s not his personal problem. But it is our collective problem, I think. What questions are we missing out on when we squeeze out those who do not have the thousands or millions of dollars it takes to study some of these topics? That’s a question that sometimes keeps me up at night, particularly the nights after conversations with colleagues who have incredibly important questions that they’ll never pursue because of the constraints I just described.

A Chance to Make Things Better

Part of what was so encouraging about SIPS was that we not only began discussing these issues, but people immediately took them seriously and started working on strategies to address them—putting together resources on “small-n designs” for those who can’t recruit the big samples, to name just one example. I have never seen issues of diversity and inclusion taken so seriously anywhere, and I’ve been involved in quite a few diversity and inclusion initiatives (given the short length of my career). At SIPS, people were working tirelessly to make actionable progress on these issues. And again, it wasn’t a fringe group of women and minority scholars doing this work as is so often the case—we had one of the largest hackathons at the conference. I really wish more people were there witness it—it was amazing, and energizing. It was the best of science—a group of committed individuals working incredibly hard to understand and address some of the most difficult questions that are still unanswered, and producing practical solutions to pressing social issues.

Now it is worth noting that I had some skepticism going into the conference. When I first learned about it I went back-and-forth on whether I should go; and even the week before the conference, I debated canceling the trip. I debated canceling because there was yet another episode of the “purely hypothetical scenario” that Will Gervais described in his recent blog post:

A purely hypothetical scenario, never happens [weekly+++]

Some of the characters from that scenario were people I knew would be attending the conference. I was so disgusted watching it unfold that I had no desire to interact with them the following week at the conference. My thought as I watched the discourse was: if it is just going to be a conference of the angry men from Twitter where people are patted on the back for their snark, using a structure from the tech industry—an industry not known for inclusion, then why bother attend? Apparently, I wasn’t alone in that thinking. At the diversity hackathon we discussed how several of us invited colleagues to come who declined because, due to their perceptions of who was going to be there and how those people often engage on social media, they did not feel it was worth their time.

I went despite my hesitation and am glad I did—it was the best conference I’ve ever attended. The attendees were not only warm and welcoming in real life, they also seemed to genuinely care about working together to improve our science, and to improve it in equitable and inclusive ways. They really wanted to hear what the issues are, and to work together to solve them.

If we regularly engage with each other (both online and face-to-face) in the ways that participants did at SIPS 2017, the sky is the limit for what we can accomplish together. The climate in that space for those few days provided the optimal conditions for scientific progress to occur. People were able to let their guards down, to acknowledge that what we’re trying to do is f*cking hard and that none of us know all the answers, to admit and embrace that we will probably mess up along the way, and that’s ok. As long as we know more and are doing better today than we knew and did yesterday, we’re doing ok – we just have to keep pushing forward.

That approach is something that I hope those who attended can take away, and figure out how to replicate in other contexts, across different mediums of communication (particularly online). I think it’s the best way to do, and to improve, our science.

I want to thank the organizers for all of the work they put into the conference. You have no idea how much being in that setting meant to me. I look forward to continuing to work together to improve our science, and hope others will join in this endeavor.

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

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?