GitHub - xmarquez/democracyData: Access and manipulate most standard scholarly measures of democracy Access and manipulate most standard scholarly measures of democracy - xmarquez/democracyData

New version of my R package {democracyData} available: github.com/xmarquez/dem...

Chart showing in many countries, people see their fellow citizens as morally good.

We asked people around the world to rate the morality and ethics of others in their country.

The U.S. is the only place we surveyed where more adults describe the morality and ethics of others living in the country as bad than good. See our full morality report here: www.pewresearch.org/...

i love data, me too meme

What Americans Think of Words like “Democracy” and “Authoritarianism” In These Times.

We recently tested how civic terms landed in today's political climate with a large sample (N=5,393) of Americans. Read more at open.substack.com/pub/moreinco...

nice!

sometimes, a user just wants to point and click.

I started making this R package 6 years ago. I finally have it in a state I'm happy with, thanks to Claude Code #Rstats github.com/MattCowgill/...

dplyr 1.2.0 was released last week and since I use {dplyr} a lot in my work, I wanted to take some time to try some of the new functions.

This post provides some supplemental examples of the new functions, beyond what is provided in the new Posit materials.
#rstats

cghlewis.com/blog/dplyr_u...

dplyr 1.2.0 dplyr 1.2.0 fills in some important gaps in dplyr's API: we've added a new complement to `filter()` focused on dropping rows, and we've expanded the `case_when()` family with three new recoding and re...

dplyr 1.2.0 is out now and we are SO excited!

- `filter_out()` for dropping rows

- `recode_values()`, `replace_values()`, and `replace_when()` that join `case_when()` as a complete family of recoding/replacing tools

These are huge quality of life wins for #rstats!

tidyverse.org/blog/2026/02...

Are female economists treated differently than males in academic seminars?

These authors wanted to know whether gender shapes how scholars are treated when presenting research.

So they built a massive dataset of 2,000+ economics seminars, job talks, and conference presentations from 2019–2023...

Screenshot of the title page of an article published in the journal "Psychological Bulletin" titled: "Trust and Subjective Well-Being Across the Lifespan: A Multilevel Meta-Analysis of Cross-Sectional and Longitudinal Associations."

Trust in others and institutions predicts subjective well-being and vice versa. This makes me even more attuned to the costs of people and systems that undermine trust. The decay of trust as a public health issue. #PsychSciSky #AcademicSky #EduSky doi.org/10.1037/bul0...

YouGov poll Jan 9-11
d3nkl3psvxxpe9.cloudfront.net/documents/IC...

During the initial 36 weeks of tirzepatide treatment, participants’ weight decreased and cardiometabolic parameters improved. After withdrawal of tirzepatide, from week 36 to week 88, the mean change in waist circumference increased by weight regain category (<25% weight regain, 0.8 cm; 95% CI, −1.0 to 2.6; 25% to <50%, 5.4 cm; 95% CI, 4.0-6.8; 50% to <75%, 10.1 cm; 95% CI, 8.9-11.3; ≥75%, 14.7 cm; 95% CI, 12.7-16.7; P < .001), as did systolic blood pressure (6.8 mm Hg [95% CI, 3.9-9.7], 7.3 mm Hg [95% CI, 4.8-9.8], 9.6 mm Hg [95% CI, 7.1-12.1], and 10.4 mm Hg [95% CI, 8.0-12.8], respectively; P = .002), non–high-density lipoprotein cholesterol (−0.4% [95% CI, −7.3 to 6.5], 1.6% [95% CI, −2.3 to 5.5], 8.4% [95% CI, 3.9-12.9], and 10.8% [95% CI, 5.3-16.3], respectively), hemoglobin A1c (0.14% [95% CI, 0.06-0.22], 0.15% [95% CI, 0.09-0.21], 0.27% [95% CI, 0.21-0.33], and 0.35% [95% CI, 0.29-0.41], respectively; P < .001), and fasting insulin (−4.0% [95% CI, −20.7 to 12.7], 15.4% [95% CI, 2.3-28.5], 46.2% [95% CI, 29.5-62.9], and 26.3% [95% CI, 9.6-43.0], respectively).

I know, I get that there are formatting standards, etc. etc. but there has got to be a better way of communicating results in a world where scientific publishing is primarily digital than what's happening in this paragraph...

🚨 Now out in Psych Science 🚨

We report an adversarial collaboration (with @donandrewmoore.bsky.social) testing whether overconfidence is genuinely a trait

The paper was led by Jabin Binnendyk & Sophia Li (who is fantastic and on the job market!) Free copy here: journals.sagepub.com/eprint/7JIYS...

great font choice to match the dataviz

this is the Inclusion of Other in the Self Scale, often used in social psychology! doi.org/10.1037/0022...

eh, It wasn't that high up... and 4-year-olds can take it. 😜

But if you're looking for other good moments in the paper... I also recommend reading the acknowledgments closely.

love spotting new and fun ways how researchers show group differences. here is one from Walker et al., 2025 showing transparent, overlayed density plots with circled data labels for the mean

doi.org/10.1016/j.co...

ggplot2 4.0.0 A new major version of ggplot2 has been released on CRAN. Find out what is new here.

I am beyond excited to announce that ggplot2 4.0.0 has just landed on CRAN.

It's not every day we have a new major #ggplot2 release but it is a fitting 18 year birthday present for the package.

Get an overview of the release in this blog post and be on the lookout for more in-depth posts #rstats

The psych job market may not be dead... but it is gravely injured 😬 So far it's looking like the Trump administration's attacks on higher ed/research are going to have more than 2x the impact on the job market as the covid-19 pandemic. #psychjobs #neurojobs #academicjobs

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

great color choice

Paper on effortful leisure led by @aidanvcampbell.bsky.social with @minzlicht.bsky.social and myself now out! (www.nature.com/articles/s44...). Effort is aversive and often avoided, yet here we find effortful tasks and activities are also more meaningful, and effortful leisure is no less enjoyable.

emotion wheel starting with "joy, fear, anger" in the center and then lapsing into gibberish

a friend of mine shared this ai-generated "emotion wheel" and unfortunately i have been laughing my ass off at it for like 15 minutes now. today i am feeling Fnliinneon

Believing in Spirits and Life After Death Is Common Around the World Belief in an afterlife, God and spirits in nature is widespread globally. Older adults are more likely than younger adults to believe in God. Pew Research Center.

Majorities of adults in most countries surveyed say that animals can have spirits or spiritual energies, including:

- 83% of adults in Hindu-majority India
- 81% in Muslim-majority Turkey
- 76% in Christian-majority Argentina, and
- 70% in Jewish-majority Israel. www.pewresearch.org/...

"In January 2023, just two months after OpenAI launched ChatGPT, a survey of 1,000 college students found that nearly 90 percent of them had used the chatbot to help with homework assignments. "

While everyone gets mad about college students using AI...I am extremely curious about this very vague survey cite in the NY Mag story

Today is #EarthDay a day for Americans to come together to protect and celebrate nature.

Despite the divisiveness of contemporary climate debates, Americans agree: protecting nature is important. (1/3)

did young voters support Trump in 2024? this is a notoriously difficult group to poll but the major election surveys indicate that Trump lost among all but young white men. tufts-pol.medium.com/have-young-v...

Climate Terminology Does Not Matter Our new paper finds that swapping out one climate term for another does not meaningfully change people’s stated commitment to fight climate change

Climate Terminology Does Not Matter

Across tens of thousands of participants in two large-scale experiments, we found that labeling climate change in different ways had no effect on their stated willingness to act.
jayvanbavellab.substack.com/p/climate-te...

via @dgoldwert.bsky.social

a scatterplot of 500,000 points that is basically a black rectangle

dataviz is my passion

I was curious to see how academic articles in (Political) Communication and Political Science are being shared on X vs. Bluesky.

I find: In 2024, academic articles in our field seem to be clearly more shared on X. But by 2025, Bluesky is catching up fast.

Short 🧵 1/n