Discover and read the best of Twitter Threads about #SIPS2018
Most recents (2)
Everyone makes mistakes during data analysis. Literally everyone. The question is not what errors you make, it's what systems you put into place to prevent them from happening. Here are mine. [a thread because I'm sad to miss #SIPS2018]
A big wakeup call for me was an errror I made in this paper: langcog.stanford.edu/papers/FSMJ-de…. Figure 1 is just obviously wrong in a way that I or my co-authors or the reviewers should have spotted. Yet we all missed it completely. Here's the erratum.
onlinelibrary.wiley.com/doi/abs/10.111…
onlinelibrary.wiley.com/doi/abs/10.111…
Since then, we've audited dozens of papers (I like this term much more than "data thugged" @jamesheathers). E.g. in @Tom_Hardwicke's new manuscript: osf.io/preprints/bits…. Summary: the error rate is very high. Most errors don't undermine papers, but most papers have errors.
Proud to see published my first #openscience, collaborative and cumulative science project with great collaborators @jakewomick, @TobiasRothmund, @KinglaKing, and @JohnJost1.
A perfect start to #SIPS2018 in just a few days!
journals.sagepub.com/doi/pdf/10.117… #SPPS
Thread alert (1/12)
A perfect start to #SIPS2018 in just a few days!
journals.sagepub.com/doi/pdf/10.117… #SPPS
Thread alert (1/12)
Methodologically, this work adds to the existing literature in a few meaningful ways. We used facets of established constructs to gain a more precise understanding of the psychological underpinnings of Trump supporters (2/12)
We analyzed psychological differences between politically relevant groups in the 2016 Election: Democrats, Republicans, and Trumpists in both primaries and general election. (3/12)
