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Josh McCrain

@joshmccrain.bsky.social

Political scientist at the University of Utah. Public policy, political institutions, media and politics

881 Posts3127 Follower443 Folgt
Marie Le Conte@youngvulgarian.marieleconte.com · 3w ago

arguing that Bluesky is nice because it's tiny and not growing is a bit like saying your local bar is great because it's always empty, it does make for a nice customer experience for you personally but that experience is probably going to be very time limited

Brendan Nyhan@brendannyhan.bsky.social · 2mo ago

All can be true: 1. Universities have been targeted by a decades-long bad faith campaign 2. Status quo is vastly more normal than craziest stuff on Fox/social media, esp. outside elite schools 3. Parts of academia are in rough shape Despite 1 & 2, we can and should address 3.

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Josh McCrain@joshmccrain.bsky.social · 4mo ago

not really sure this makes sense if you're also not going to require PAPs. just require them and be done with it

American Journal of Political Science (AJPS)@ajpseditor.bsky.social · 4mo ago

AJPS Editors Blog Post: Pre-Analysis Plans (PAP) Policy Change. We recently clarified our policy regarding the execution of pre-analysis plans and the reporting of supplemental materials. These changes are in line with increasing transparency in reporting ajps.org/2026/05/13/p...

jamelle@jamellebouie.net · 4mo ago

it is incredible to see folks complain that good people don't run for office, that lawmaking is a hobby for the rich, that our state legislators are run by car dealership freaks, and then insist that we shouldn't pay lawmakers any more than we do.

Josh McCrain@joshmccrain.bsky.social · 4mo ago

how are folks revamping their quant classes to use & teach agentic AI? it's certainly a tool we should teach rather than ignore, but i find it hard to think through how to incorporate into existing syllabi in a straightforward way.

Andrew Trexler@atrexler.com · 4mo ago

Thrilled to announce my paper with @dianamejordan.bsky.social & sky-less Trent Ollerenshaw is now published open-access at @apsrjournal.bsky.social. In a (very) large replication & extension, we show that design effects of repeated measure experiments are nonzero but typically small. Thread:

Abstract for "New Evidence and Design Considerations for Repeated Measure Experiments in Survey Research" by Diana Jordan, Trent Ollerenshaw, and Andrew Trexler, published in the American Political Science Review. The abstract text reads: "We re-examine recent influential claims that repeated measure experimental designs offer large precision gains without biasing treatment effect estimates in survey research. We test these claims by experimentally varying the design of six classic political science experiments across three distinct large samples of U.S. adults (total N = 13,163). In contrast to prior evidence, we observe consistent attenuation of treatment effects in repeated measure designs. However, we show in simulations that this average design effect is small enough, and the precision gains large enough, that we recommend repeated measure designs for broad application—though (large-N) post-only designs may be preferable when research priorities include estimating the precise magnitude of a treatment effect. We additionally explore how several design considerations affect the bias-precision trade-off, such as within-subject versus between-groups designs, the relative separation of repeated measures within single surveys, and differences in respondent characteristics across sample types."
Cambridge University Press Political Science & IR@cambup-polsci.cambridge.org · 4mo ago

#OpenAccess from @apsrjournal.bsky.social - New Evidence and Design Considerations for Repeated Measure Experiments in Survey Research - https://cup.org/4sVtFLj - @dianamejordan.bsky.social, TRENT OLLERENSHAW & @atrexler.com #FirstView

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