Katie Corker Profile picture
Jan 13 15 tweets 4 min read
A long thread about the meandering, winding road to an accepted paper. Hope you have patience because this tale nearly starts 10 years ago with @DermotLynott.
In early 2013, I was a brand new assistant professor, just into my first tenure track gig. A call for replication studies had come out, which would ultimately become the special issue in Social Psychology (2014), which was the first all #RegisteredReports endeavor.
Even then, I was into replication and what would become the open science movement in psychology, so I was eager to submit something to the special issue. Plus, it seemed a good opportunity to get something going with my students at my new (teaching focused) job.
Back then, there was a list of studies that people were eager to see replicated. Near the top of the list was this sexy study where participants acted more prosocially after holding a warm cup of coffee than an iced coffee.
I'd done my training in social cognition and social priming, so the study seemed a good fit for me. I made my case, proposing the study to the editors. Imagine my surprise when they wrote back to say that I wasn't the only one interested in this study!
They proposed that Dermot and I work together, a sort of blind date research collaboration. I have to admit, I was quite nervous to work with someone I didn't know *at all.* Now that we've been working together for a decade, I can safely say it's one of the best collabs I've had.
Our joint work was ultimately published in the special issue (in 2014). That might have been the end of it...but in fall 2014, Dermot reached out again, asking if I wanted to keep working on this topic. He proposed a meta-analysis of studies in the area.
This turned out to be synergistic, because I had an interest in meta-analysis and other projects cooking in the area. It seemed simple enough - gather up the studies and average them together, right?
Oh boy was I wrong! As anyone who has done this kind of work knows, meta-analyses are HARD. Like really hard! (Or at least hard to do well and transparently.) The fruits of our labors are now finally accepted for publication, nearly 8.5 years after starting this part of the work.
I'm quite proud of the paper itself. We used just about every bias correction and detection technique that we could find. We formally evaluated risk of bias (quite rare in psych MAs). I hope you take a look and enjoy the paper itself.
As an aside, for a pair of people quite keen on preprinting, why in the world are we just sharing this now? The paper went to another journal on its way to its final home at British Journal of Social Psychology.
That journal was Psych Bull, which until last year had a "no preprinting" policy. That policy ultimately kept the paper out of the public sphere for *nearly 3 years* (first submission was in early 2020). Oof.
You might wondering, why in the heck did a paper that was only rejected once take 8.5 years from start to acceptance? Here are the facts:
- Number of children born = 4 (2 each)
- Number of new jobs = 3 (1 for me and 2 for Dermot)
- Number times moving house = at least 4 (2 each)
And, of course, there was that pesky pandemic too. Did I mention that this type of work is HARD?! I'm proud of our perseverance in seeing this through (and a bit relieved that it's finally "out").

In any event, cheers @DermotLynott - many thanks for a fruitful last decade!

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More from @katiecorker

Nov 19, 2018
As a #ML2 author, very excited to see this paper finally out! The headline that 50% of effects replicate buries the lead a bit. For me, the major finding here concerns heterogeneity. What's heterogeneity, you ask? Gather round...
Heterogeneity describes how much effects being replicated vary from lab to lab, *beyond* sampling error. Sampling error says we expect that by chance, some labs overestimate the true effect size, others underestimate. Heterogeneity says how much more we expect effects to vary.
If differences between labs are to explain differences in replicability, there MUST be sizable heterogeneity in effects. The magnitude of the between site differences limits how much variance heterogeneity can explain.
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