John Sakaluk
@johnsakaluk.bsky.social
1.7K followers 670 following 260 posts
He/him/his. Associate Professor at Western University. Work: #rstats, #psychometrics, #dyadic data, #MetaAnalysis, #closerelationships, Sexuality Fun: All things cured, fermented, roasted, seared, smoked, shaken, stirred, and swizzled.
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johnsakaluk.bsky.social
🧵
Very excited (w/ @omarjcamanto.bsky.social) to share our preprint tutorial for using our R 📦 dySEM for #dyadic data analysis with latent variables, in cross-sectional data sets.

This paper has been literal years in the making, and provides three distinct tutorials.

osf.io/preprints/ps...
Reposted by John Sakaluk
laurahelmuth.bsky.social
This is how it's done: put the truth right there in the headline and sub headline.
johnsakaluk.bsky.social
That being said, I think some of the same magical patterns could be fit to certain initiatives/efforts in the OS movement. So I say all of this as a rabid supporter of some OS products (e.g., RRs) and a skeptic of others (e.g., preregistration outside of RRs) 🤷‍♂️
johnsakaluk.bsky.social
And I don't mean that figuratively. I mean people in the world today who think they are reality-bending sorcerers would look at some of how our business is conducted, and think "Those people have some serious $%&*ing magikal capabilities!"
johnsakaluk.bsky.social
To go a step beyond "storytelling"--and I think I made this argument, unhinged, in a conference room with @rogerthegs.bsky.social a handful of years ago--there is a legitimate and distressing overlap between A) contemporary social science practices and B) contemporary schools of *magical* practices
johnsakaluk.bsky.social
This looks awesome, though I have a very, very, very fuzzy memory of @minecr.bsky.social once-upon-a-time writing a really persuasive thread or piece (can't remember) to the effect of: we should stop trying to duck R's built-in contrast system (and bad things happen when we do). Ring any bells?
Reposted by John Sakaluk
solomonkurz.bsky.social
Looking for textbooks that did a good job walking out sum-to-zero coding (aka effect coding) versus treatment coding (aka dummy coding). Favorites?
johnsakaluk.bsky.social
Chapter 8 of Cohen, Cohen, West, and Aiken
Reposted by John Sakaluk
eikofried.bsky.social
Had missed this absolutely brilliant paper. They take a widely used social media addiction scale & replace 'social media' with 'friends'. The resulting scale has great psychometric properties & 69% of people have friend addictions.

link.springer.com/article/10.3...
Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts? - Behavior Research Methods
A growing number of self-report measures aim to define interactions with social media in a pathological behavior framework, often using terminology focused on identifying those who are ‘addicted’ to engaging with others online. Specifically, measures of ‘social media addiction’ focus on motivations for online social information seeking, which could relate to motivations for offline social information seeking. However, it could be the case that these same measures could reveal a pattern of friend addiction in general. This study develops the Offline-Friend Addiction Questionnaire (O-FAQ) by re-wording items from highly cited pathological social media use scales to reflect “spending time with friends”. Our methodology for validation follows the current literature precedent in the development of social media ‘addiction’ scales. The O-FAQ had a three-factor solution in an exploratory sample of N = 807 and these factors were stable in a 4-week retest (r = .72 to .86) and was validated against personality traits, and risk-taking behavior, in conceptually plausible directions. Using the same polythetic classification techniques as pathological social media use studies, we were able to classify 69% of our sample as addicted to spending time with their friends. The discussion of our satirical research is a critical reflection on the role of measurement and human sociality in social media research. We question the extent to which connecting with others can be considered an ‘addiction’ and discuss issues concerning the validation of new ‘addiction’ measures without relevant medical constructs. Readers should approach our measure with a level of skepticism that should be afforded to current social media addiction measures.
link.springer.com
johnsakaluk.bsky.social
Withdraw from social media; deny uAlberta the free marketing of its "impact".
Teach strictly to the textbook; tell students it's too risky to express opinions outside of "legislated curriculum"
Grieve the impact of censorship; fuck with their workflow if they're gonna fuck with yours
johnsakaluk.bsky.social
I'm confident Florence's union will have something to say about this, but censorship wins when we all wait for the bureaucratic process to play out without registering our malcontent in other more immediate ways of protesting. Academics have tools/levers to do this, if they would but use them.
johnsakaluk.bsky.social
Reading this exchange has also been a good reminder that there's almost always someone who knows more about a certain thing than you do, and it can be tricky to remember that when many of us (incl. myself) wear the "hat" of method/quant person in our academic communities that hype our knowledge-base
johnsakaluk.bsky.social
This 🧵 has solidified for me a belief that there just are some scientific arguments that are harder to win than others, because the devil is in the belaboured boring-ass details, and those kinds of arguments are vulnerable to alternative positions that are easier (and more entertaining) to convey.
johnsakaluk.bsky.social
This sets up an interesting rhetorical asymmetry between the two; give me 100 MS vs. S debates, and I'd conjure the more entertaining and accessible take will be the MS camp's in 95 of them.

That doesn't mean that the MS are wrong and the S are right in this (or any) instance. But >
johnsakaluk.bsky.social
and is grounded in (all things considered) more accessible prose and argumentation.

The S-camp, in contrast is...not that. These kinds of arguments, and the writing needed to convey them, are often technical, dry, lengthy, detailed, and quite sober. Indeed, how does one make a proof funny? >
johnsakaluk.bsky.social
The second, and more prominent, is how distinctive the tone of MS discourse is, especially offset against this reply from those in the S-camp. MS argumentation is often amusing, clever, funny, biting, and at times, entertaining. The approach "works" in the sense that it creates a lot of engagement >
johnsakaluk.bsky.social
The gospel truth of this is beyond my paygrade, but this 🧵 illuminated a few interesting patterns to me:

The first is that, though the credibility discourse has brought a lot of attention to theory, a lot of its arguments and tools (incl. one's I've used) lean heavily into dustbowl empiricism.
richarddmorey.bsky.social
Simonsohn has now posted a blog response to our recent paper about the poor statistical properties of the P curve. @clintin.bsky.social and I are finishing up a less-technical paper that will serve as a response. But I wanted to address a meta-issue *around* this that may clarify some things. 1/x
datacolada.bsky.social
Would p-curve work if you dropped a piano on it?
datacolada.org/129
johnsakaluk.bsky.social
Academic friends: what software or approaches are you using these days to keep track of your/your students' research projects? On sabbatical and needing some systems to stay in the know of how students' stuff is progressing. Don't want something super hardcore, but tracking stage/keeping notes, etc.
johnsakaluk.bsky.social
My editor font is comic sans
johnsakaluk.bsky.social
This is a great excerpt; can I trouble you for the source?
Reposted by John Sakaluk
ingorohlfing.bsky.social
Tidy simulation: Designing robust, reproducible, and scalable Monte Carlo simulations #StatsSky
arxiv.org/abs/2509.11741
It is not formally linked to the {tidyverse}, bit affinity is obvious.
The paper does a solid job in describing the simulation workflow, could be useful for intros to simulation
Figure 2 from preprint illustrating simulation workflow. From left to right, it goes from simulation grid, to generation function, to analysis function, to results table