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Sanjay Srivastava is one of the most important working psychologists and one of the thinkers in my field who has most influenced me. This is not because he has published ground-breaking papers, although he has written some influential ones.
It’s mostly because he has effectively communicated how to think about the scientific process and scientific ideals to a generation of early career researchers through insightful, accessible commentaries on psychology through non-traditional outlets. Srivastava is influential in large part because of his blog posts, his commentary on articles on Twitter, and his podcast.
His blog post on the “Pottery Barn Rule” (“You break it, you buy it”) for journals publishing failed replications is known to almost everyone interested in scientific reform, and his post rebutting Gilbert and colleagues’ reanalysis of the Reproducibility Project in Psychology (RPP) is a clear explanation of why the conclusions of several eminent authors—and peer reviewers—about replication statistics are wrong.
The Black Goat podcast, which Srivastava hosts with Alexa Tullett and Simine Vazire, offers regular analysis of the issues facing researchers interested in scientific reform, including an introduction to most major topics in the Credibility Revolution from results blind reviewing (May 31, 2017 episode), to the role of criticism in science—including the possibility of employing dedicated critics (March 6, 2019 episode) to a historical comparison of the current reform movement in psychology to previous attempts to improve the field (November 28, 2018 episode). His social media commentary manages to be irreverent and funny—and to make him often seem like “the adult in the room” when arguments between psychologists break out.
I have now been blogging once a week for six months. I don’t have the insight or reach of Srivastava, but I have enjoyed working through my thoughts on the scientific method and new psychology articles I’ve been reading. I’ve also had some really gratifying feedback from people I respect (and sometimes even strangers!) who liked a particular post.
Sometimes, however, I can’t help but wonder if I’m not being strategic enough in my use of time, especially when I’m in such a precarious moment in my career. I am on the academic job market this year, and I know I will be judged largely based on how many academic manuscripts I’ve managed to push through to publication. Blogging, even if it’s thoughtful and reasonably widely read, won’t count for much in my job application.
So why have I kept doing it, writing 1,000+ words a week here instead of trying to add that length to an existing manuscript? In part, it’s because it’s fun for me. Blogging for me has been like having a discussion in a graduate seminar, where you don’t necessarily have to have read all the major papers on a topic, but where you are actively learning, and you want to work through the ideas. Blogging allows me to express ideas about topics that are fun and fresh for me, as I’m still playing and exploring them.
At a broader level, though, it’s because I think that blogging is a valuable contribution in itself. Blogging fills the gap between dense, technical arguments—often written in an academic's "defensive crouch" to avoid reviewer critiques—and the everyday language we use to communicate our practices. Good blogging is just good explaining.
Being a good explainer is sort of a weird niche in science. It can contribute to other academic achievements—you might be able to publish a well-written paper more easily than a poorly written one, or you might get better student teaching evaluations—but there’s no part of an academic job application that’s explicitly about being good at translating complex ideas into understandable language.
Yet it’s a hugely important skill. Leona Aiken has made this point in several presentations about advanced quantitative methods: science needs “quantoids” or go-betweens who can understand complex ideas at the cutting edge (even if they aren’t developing them) and can translate them into practice for all the people in their research group who took the bare minimum in statistics. Emily Butler makes a similar point in lectures regarding multi-level modeling of experience sampling data: Raudenbush and Bryk came up with most of the important ideas, but Laurenceau and Bolger helped some of their ideas take off by translating them into a more accessible language (“almost English,” as she put it).
The clarity of explanation—the translation of complex ideas into understandable discussions—is what I admire most about Srivastava’s writing. The recognition he gets for it in the field is important for me to see because it reminds me that pursuing one of my signature strengths can be valued in the field.
I don’t know how much longer I will be able to continue in the career I’ve spent a decade pursuing (I think I will eventually land a tenure-track position, but the odds for any particular job are low). This has made me want to make sure I get my best contribution out before I might have to move on. For better or worse, I think it’s trying to explain ideas about how to do science here.
I hope that some of what I've written will eventually join the ranks of the blog posts other early career researchers like me consider important reading for translating statistics and theory into practice. This includes posts from The 100% CI blog, Dorothy Bishop's blog, Simine Vazire's blog, Ruben Aslan's blog, Joe Hilgard's blog, Daniel Lakens' blog, Tal Yarkoni's blog, Andrew Gelman's blog, James Heathers' Medium account, episodes of the Two Psychologists Four Beers podcast, and the Everything Hertz podcast. (I'm sure I'll kick myself for forgetting others after this is published—this is the tip of the iceberg, not an exhaustive list.)
If you’re wondering why people should blog—or why I blog—it’s because I think that translating complex ideas about the scientific process into an understandable format is important work. And if you think you can make a contribution this way, I’d encourage you to try it, too.
