Top 15 ggplot2 extensions, by downloads during the last month.

Some surprises here, at least for me.

#rstats

Code: https://gist.github.com/carlislerainey/1f7d1da41b7f9715b1e01ebf79d21b9d

“Does Threat Cause Increases in Conservatism? Evidence from Three Large Experiments in the United States Says No”

From Abigail Cassario, Mark Brandt, and Aymin Triki

PsyArXiv Preprint: https://doi.org/10.31234/osf.io/eqznx_v1

"Improving the Teaching of Applied Statistics: Putting the Data Back into Data Analysis"

from Singer and Willet

jstor: https://www.jstor.org/stable/pdf/2685342.pdf?casa_token=351yQJiBIjkAAAAA:CCkLkUEPCJGnuWgVQY47Ret9YCe7sZafTnL5WuzPV9fmaq98lnqtAgViBKKG647ZMsrvgjPrhYd1hjelGljvviaGTDJ_RmLRLiaKbyG79YTJKbjl5A

👇good👍
1️⃣real data
2️⃣context info
3️⃣interesting
4️⃣teaches something real
5️⃣allows many methods
6️⃣raw
7️⃣case ID

"What Good is a Regression? Inference to the Best Explanation and the Practice of Political Science Research"

from Spirling and Stewart

journal: https://doi.org/10.1086/734280

"The value of preregistration for psychological science: A conceptual analysis"

from Lakens

paper: https://www.jstage.jst.go.jp/article/sjpr/62/3/62_221/_pdf/-char/ja

"Preregistration does not improve the transparent evaluation of severity in Popper’s philosophy of science or when deviations are allowed"

preprint: https://osf.io/preprints/metaarxiv/yqvtu_v1

"Social media consensus paper causes social media uproar"

link: https://www.science.org/content/article/social-media-consensus-paper-causes-social-media-uproar

original paper: https://osf.io/preprints/psyarxiv/b94dy_v1

new preprint

"On the Foundations of the Design-Based Approach"

from Aronow, Jang, and Offer-Westort

From Weingast's essay "Caltech Rules":

"Papers must focus on one main point. Do not attempt to enrich your paper with many
asides... It is far better to have a narrow, focused, and useful paper than a rich one that is ignored."

Link to essay: https://weingast.people.stanford.edu/caltech-rules-writing

From Weingast's essay "Caltech Rules":

"With rare exceptions, papers do not write themselves. Transforming a good idea into a good paper is a difficult process. A clear understanding of what each part of your paper must accomplish is essential to this process."

Link to essay: https://weingast.people.stanford.edu/caltech-rules-writing

I’d add one small thing: there’s a period for me during *re*writing process when I’m figuring out *what I’m trying to say*, not just how to say it.

From Zinsser’s *On Writing Well*.

“Keep thinking and rewriting until you say what you want to say.”

https://ia800308.us.archive.org/31/items/OnWritingWell/on-writing-well.pdf

P Aronow and Fredrik Sävje's review of "Book of Why." A really great short read.

https://arxiv.org/abs/2003.11635

Paper #4

Campos, Nicolas, and Christopher Federico. 2024. “A New Measure of Affective Polarization.”

DOI: https://doi.org/10.31219/osf.io/xg3b7

The introduction of this paper--see the third image--gives a nice overview of the ideas presented in these four papers.

Paper #3

Landry, Alexander, Eli Finkel, Rick H. Hoyle, James Druckman, and Jay Joseph Van Bavel. 2024. “Partisan Antipathy and the Erosion of Democratic Norms.”

DOI: https://doi.org/10.31234/osf.io/ahgy6

Paper #2

Finkel, Eli J., Christopher A. Bail, Mina Cikara, Peter H. Ditto, Shanto Iyengar, Samara Klar, Lilliana Mason, et al. 2020. “Political Sectarianism in America.” Science 370(6516): 533–36.

DOI: https://doi.org/10.1126/science.abe1715

Paper #1

Druckman, James N, and Matthew S Levendusky. 2019. “What Do We Measure When We Measure Affective Polarization?” Public Opinion Quarterly 83(1): 114–22.

DOI: https://doi.org/10.1093/poq/nfz003

Four new(ish) papers on measuring affective polarization: A thread 🧵

"[The] feeling thermometer measure is in fact so tied to the concept of affective polarization that often it is simply referred to as affective polarization." (from Paper #4 below)

@koen_hufkens Good question! I suspect this is due mostly (maybe “almost entirely”) to changes in journal policies. Theres likely a generational gap too, but I’d guess that’s much smaller.

How data-sharing habits have changed in political science since 1995.

These data show a massive shift in norms, requirements, and infrastructure, but also how much room we have to improve.

GitHub Gist w/ #rstats {gganimate} code: https://gist.github.com/carlislerainey/b87600c3314e1829a10b43d0c4617762

Preprint on OSF: https://osf.io/preprints/socarxiv/a5yxe