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.

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

  1. Bravo! Your excellent post motivated even the old lady to comment. FWIW, I had similar trepidation about attending and came home with similar elation. So many more challenges to solve, but such good energy.

    p.s. It was great to meet you at SiPS, Neil.

  2. Thanks, Neil, for sharing these important thoughts. Although I agree that there are barriers to collecting data with minority groups, there are also solutions that lie between the N = 42 and $11,000–the literature in developmental psychology of adolescence and emerging adults (where samples tend to be much larger than social psychology) is replete with them. One solid solution if you are interested in using college subject pools is collaborating with researchers at institutions that have greater representation of your target population. There are several examples of successful multi-institution projects (e.g., Many Labs, MUSIC, EAMMI). The MUSIC study recruited > 10,000 students across 30 colleges and universities, and they had zero funding.

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