Articles I Like

With a background in Psychology, working at the Social Psychology department at Tilburg University, and with an interest in replication, this is what my Filter Bubble looks like (May 2013):

“OH MY GOD, THIS DOESN’T REPLICATE (Bargh), THAT DOESN’T REPLICATE (Dijksterhuis), THIS STUDY IS FLAWED (fist-clenching), THAT STUDY IS FLAWED (gaming and dyslexia), BOTH ARE UNDERPOWERED, WE NEED POST-PUBLICATION REVIEW AND PLOS ONE ALLOWS IT, YES BECAUSE THAT REPLICATION ATTEMPT IS FLAWED (Dijksterhuis), SO WE SHOULD PRE-REGISTER THEM, BUT WAIT WHAT CONSTITUTES A FAILURE TO REPLICATE (Srivastava), AND WHAT IS SOCIAL PRIMING (Zwaan), AND STAPEL AND THE NYT HAVE SOMETHING TO SAY ABOUT ALL THIS, REALLY? (Nussbaum)”

I can’t even begin to link to all the blog posts that have been written on these topics. I really like reading (most of) them. Most discussions are extremely interesting and make me feel like science might be changing quicker than anyone can anticipate or predict. I might even say that I feel like we’re in this ‘defining moment of history’ and I’m excited to see where all of this is heading. So, I am in no way suggesting that there should be less talk about all of the above.

Nevertheless, as a young student (3 years into my PhD-project), I also like to read and think about articles that provide solid empirical contributions that replicate, and that have important implications. Therefore, it would be great to read more posts that applaud papers. It’s been a while since I’ve read such a post (for an exception, see this post on a paper by my colleagues) and I think there are at least 2 explanations for this:

1. I only follow psychology / JDM people on Twitter and in blogs that talk about how science is changing.

2. Most online discussions in Psychology / JDM really just focus on the replication-preregistration-postpublicationreview kind of things in my bubble.

In any case, I thought I’d add a page to my website where I list Articles I Like. It’ll help me (and hopefully others) remember that there are many articles worth reading. Some because they provide intelligent insights, others because they present interesting datasets and analyses. I plan to update that list every now and then but you can check out the articles I remember I liked here. Finally, if you have any blogs or twitterers you think I should follow, please let me know in the comments!

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Replication in Psychological Science

If you have read my previous posts you know that I have some interest in replications. Etienne LeBel—founder of PsychDisclosure.org—definitely picked up on this. He sent an email with the good news that he and Lorne Campbell just got a paper accepted in Psychological Science. They report two really close replication attempts, both of which they had reviewed by the original author! Continue reading

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“What can we learn from van Wolferen et al.’s replication attempt? Unfortunately, not much”

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Why do some conferences have discussants? It’s awkward

If you’ve ever been at a conference that had discussants, I’d like to know if you thought it’s as awkward as I thought it was. If you’ve never attended such a conference, good for you! Read this post to know why you also want to avoid them in the future.

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Sample sizes of initial and replication studies

Yoel Inbar, Marcel Zeelenberg, and I published an article in which we try to replicate the finding that a reminder of health insurance makes people think health-related risks are less likely. We report 3 very close replications of the original study (total N = 451) and 2 conceptual replications (total N = 404). The initial study had 40 participants. In other papers, the ratio of replication-to-initial sample size easily exceeds 10 : 1 too, and that’s a good thing. Continue reading

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Financial Times MBA ranking: More papers, more pay!

The Financial Times published their Global MBA rankings, a Top-100 of all the MBA schools in the world. One interesting correlation: the higher a school ranks at academic performance, the more its MBA graduates earn.

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Sampling error

As part of a research project I was writing R code that samples and resamples data from a given population. I was surprised by how badly sampling error affects small samples and how easy it was to visualise that. Here, I’ve posted some pictures and R code so you can see for yourself.

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