Felipe Fontana Vieira

Personally, a lot of the times I don’t even think

And then someone tells you about randomised non-comparative trial

The authors probably don’t understand the derivation either & asked a bot too 😉

Indeed, there are always better ways to say something. The thing is that a course on causal inference can go into many different directions. Part of the causal inference literature is hard to follow imo, but there are definitely many concepts that can be learned and useful without those 5 years

Elements of Structural Equation Models (SEMs) Cambridge Core - Psychology Research Methods and Statistics - Elements of Structural Equation Models (SEMs)

Well well well: www.cambridge.org/core/books/e...

rixpress is now an @ropensci.org package!

Link: docs.ropensci.org/rixpress/

Whoa—my book is up for pre-order!

𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData

The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit

The web version will stay free forever and my proceeds go to charity.

tinyurl.com/4fk56fc8

And hopefully no rethinking my rethinking 😅

Rethinking measurement invariance causally Highlights: It is preferable to work with a causal definition of measurement invariance A violation of measurement invariance is a potentially substantively interesting observation Standard tests for measurement invariance rely on strong assumptions Group differences can be thought of as descriptive results

Conceptual graph illustration the central points of the manuscript. A group variable is potentiall connected to a construct of interest which affects items. Measurement invariance is violated if the group variable directly affects the items, for example by modifying the loadings from the construct to the items, or by directly affecting an item

To make this less abstract, consider a scenario where students take an exam, R, meant to capture some ability, T, and then are admitted to a program, V, depending on their exam results: R → V. This is sufficient to result in a violation of the statistical definition of measurement invariance. Exam results and admission are not independent given ability because exam results have a direct effect on admission. Even if we know somebody’s ability (e.g., we know it’s very high), learning about their admission status (e.g., they were not admitted) can tell us something about their exam result (e.g., it may have been worse than expected). According to the causal definition, this in itself does not constitute measurement bias, which seems a sensible conclusion here. After all, the scenario does not involve any reason to believe that the measurement process varied systematically by admission status. Admission happens after the exams took place, it cannot retroactively influence the measurement process (and, for example, lead to unfair treatment depending on admission status).

New paper out with @boryslaw.bsky.social 🥳 In which we sketch out how to rethink measurement invariance causally for applied researchers. And provide a causal definition of measurement invariance!

www.sciencedirect.com/science/arti...

Wwuuuuuuut

The authors "conclude that psychometric standards must be sufficiently rigorous to distinguish genuine constructs and associations from methodological artifacts that can otherwise pose a serious validity threat." Which sounds great, but is typically *impossible* to achieve in psychology
1/5

Luc is also currently working on this package that will give you (not only) tikz-based diagrams. Still very early on, but something to look forward to!

#statstab #405 Best Practices for Estimating, Interpreting, and
Presenting Nonlinear Interaction Effects

Thoughts: Guidance on nonlinear interactions, reporting (probabilities) and visualisations.

#probit #logit #logisticregression #nonlinear #guide

sociologicalscience.com/download/vol...

The thing with dating apps is that it is literally like getting a job: it doesn’t work until it works

Fair coins tend to land on the same side they started: evidence from 350,757 flips.

That's the title of our paper summarizing ~650 hours of coin-tossing experimentation just published in the Journal of the American Statistical Association.
doi.org/10.1080/0162...

Shiny in Production 2025: Full Length Talks We are pleased to announce the full length talks for this year's Shiny in Production conference! In this blog post, we've pulled together all of the talk abstracts to give you a full view of what to expect!

We're happy to share the main talks for the Shiny in Production Conference 2025!

This year's lineup includes some great talks on using Shiny in real-world projects, from building apps to scaling them in production.

Looking forward to seeing everyone there!

#ShinyInProduction #RStats #DataScience

It was so so good to work with Monash Uni students on their first steps in C++ programming for R applications! 🚀✨ They made it! And now, they're ready to code! 🤖👾

arp.numbat.space/week11/

#rstats #rcpp #armadillo

I used to do that during my bachelors and (less but still) masters. That habit got me in touch with my previous internship supervisor. It also allowed me to exchange a few emails with cool people, like Yalom, and it is also how I got in touch with my phd supervisor

Doctoral fellow

PhD fellowship to work with me and Benedetta Franceschiello on the analysis and modelling of fast sampled fMRI data!

www.ugent.be/en/work/scie...

The landing page of the course "ggplot2 uncharted" with the title teasing it with "Master Data Visualizations with ggplot2".

Excited to launch "ggplot2 [un]charted" with @yan-holtz.bsky.social! 🎉

An online course to master #ggplot2 with exercises, quizzes, and modules—and hands-on code running in your browser!

Still WIP—sign up now for a limited discount:
👉 www.ggplot2-uncharted.com

#rstats #DataViz #DataVisualization

Fonts in R Taking control of fonts and text rendering in R can be challenging. This deep-dive teaches you everything (and then some) you need to know to keep your sanity

Mastering typefaces and fonts in #rstats has always been harder than it should.

I have tried to collect much of my relevant knowledge in this deep-dive blog post so you can spend your time picking the right typeface instead of cursing at the computer

Bad stats: A regular series exploring slip-ups, snafus and salutary lessons from the world of statistics Abstract. What is the secret to a happy life after a successful career in the statistical sciences? One answer is continuing engagement with the profession

Every month you get a "Bad stats" article as part of Significance. This month we have something on randomised non-comparative trial (RNCT): academic.oup.com/jrssig/artic...

Very beautiful moment tbh

Precise Answers to Vague Questions: Issues With Interactions - Julia M. Rohrer, Ruben C. Arslan, 2021 Psychological theories often invoke interactions but remain vague regarding the details. As a consequence, researchers may not know how to properly test them an...

Not-so-modest shoutout to our own paper on interactions in which we discuss both issues — scale dependence and confounding 😋 journals.sagepub.com/doi/10.1177/...

Omg, I recently had a chat with LLM about this and out of nowhere it finished with a quote (and more) from Alfred Korzybski: "The map is not the territory — the scale you choose shapes what you see"

Also note that "removable" here is a mathematical term: an interaction is removable if we can nullify it with a monotonic transformation. But just because an interaction is "removable" doesn't mean it isn't substantial!

Oh, completely missed that reply. Thanks!