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JsonGeller

@jgeller1phd.bsky.social
1.4K followers 854 following 876 posts

Eye-tracking, Pupillometry, Word nerd, Open Science, Metascience, Learning and Memory, Language, R, Stats, Quant, Director Human Neuroscience Lab @bostoncollege www.drjasongeller.com

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JsonGeller @jgeller1phd.bsky.social · 2h
Read dis!
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JsonGeller @jgeller1phd.bsky.social · 3h
I’ve used eargasm for a while as well
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JsonGeller @jgeller1phd.bsky.social · 3h
These are the best ones I’ve found and I go to many.
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JsonGeller @jgeller1phd.bsky.social · 4h
This seems like it could be fun!
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JsonGeller @jgeller1phd.bsky.social · 20h
This is great!
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JsonGeller @jgeller1phd.bsky.social · 21h
If only one could render a qmd—one can dream
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JsonGeller @jgeller1phd.bsky.social · 06/10/2026
For those examples, I would choose something beta-flavored. Simulations for non-independence would def be interesting. Maybe @rmkubinec.bsky.social has more info?
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JsonGeller @jgeller1phd.bsky.social · 06/10/2026
In the first example of the paper the original paper modeled proportions, thus the reason we use beta.
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JsonGeller @jgeller1phd.bsky.social · 06/10/2026
Successes out of known n → binomial (beta-binomial if overdispersed) Continuous, no 0s/1s → beta 0s/1s, same process as interior → ordered beta 0s/1s from a distinct process → ZIB / OIB / ZOIB .
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JsonGeller @jgeller1phd.bsky.social · 06/10/2026
Apaquarto is my go to
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JsonGeller @jgeller1phd.bsky.social · 05/10/2026
Thank you for your helpful comments!
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Reposted by JsonGeller
James Balamuta @coatless.bsky.social · 04/10/2026
webrarian is a new R package. It turns a folder of R scripts and data into a static website where R runs in the visitor's browser, with no compute server and nothing to install. blog.thecoatlessprofessor.com/posts/introd... #rstats #webr #webassembly
A card for the R package webrarian. Large text reads "A folder in. A website out." On the left, a small card lists a folder named lab-01 holding lab.R, setup.R and a data folder. A red arrow points from it to a browser window that shows a scatterplot of car weight against miles per gallon with brick-red points. The address coatless-wasm.github.io/webrarian is on the card.
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JsonGeller @jgeller1phd.bsky.social · 04/10/2026
Thank you :)
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JsonGeller @jgeller1phd.bsky.social · 03/10/2026
@dominiquemakowski.bsky.social created dominiquemakowski.github.io/cogmod/ based on some of our convos.
dominiquemakowski.github.io
Cognitive Models for Subjective Scales and Decision Making Tasks
Implements cognitive models for data from subjective (Likert or analog) scales and from decision making tasks with reaction times and choice data. Provides random generation, density functions, and cu...
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JsonGeller @jgeller1phd.bsky.social · 03/10/2026
Paul did not seem like he wanted it changed when I posted about it
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JsonGeller @jgeller1phd.bsky.social · 03/10/2026
In this case zeros can be theoretical interesting (maybe students are disengaging not paying any attention) and binomial model doesn't really get at that question
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JsonGeller @jgeller1phd.bsky.social · 03/10/2026
So many defensible options! For me it comes down to whether I care about the binomial sampling process. If it’s x/10, beta-binomial seems natural + handles overdispersion. If I want to model the proportion itself, ordered beta is appealing since it naturally accommodates 0 and 1.
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JsonGeller @jgeller1phd.bsky.social · 03/10/2026
@chelseaparlett.bsky.social you are too kind and generous! You were a huge help!
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JsonGeller @jgeller1phd.bsky.social · 03/10/2026
Andrew was inspiration behind this paper!
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
Probably doesn’t matter in your case the skewed dist probably better but see osf.io/preprints/ps...
osf.io
OSF
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
The default is actually incorrect though…
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
This is a very fun read!
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
Out of curiosity, when comparing models did you test the default exgauss family in brms?
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Reposted by JsonGeller
Henrik Singmann @singmann.bsky.social · 02/10/2026
New preprint: When should you use linear mixed models (LMMs), and when is repeated-measures (RM) ANOVA enough? LMMs offer greater flexibility. But does that make them a better default? My paper argues for statistical parsimony: use RM-ANOVA when both methods are appropriate. osf.io/preprints/ps...
osf.io
When to Use Mixed Models and When RM-ANOVA - An Argument for Statistical Parsimony
When should researchers use linear mixed-effects models (LMMs) versus repeated-measures analysis of variance (RM-ANOVA) for analysing repeated-measures data? I first outline the conceptual and statist...
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
Much appreciation to @matti.vuorre.com @rmkubinec.bsky.social @chelseaparlett.bsky.social for helping craft this beast! Also to reviewers @scoretta.bsky.social and @solomonkurz.bsky.social for making this a better paper!
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
link.springer.com/article/10.3...
link.springer.com
A beta way: A tutorial on Bayesian beta regression for psychological research - Behavior Research Methods
Rates, percentages, and proportions are common outcomes in psychology and the social sciences. These outcomes are often analyzed using models that assume normality, but this practice overlooks importa...
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JsonGeller @jgeller1phd.bsky.social · 02/10/2026
New tutorial finally in press with @rmkubinec.bsky.social @chelseaparlett.bsky.social and @matti.vuorre.com. It has everything you could possible want: Quippy headings, a whole lot of beta, bayesian analyses and frequentist analyses side by side in harmony. link.springer.com/article/10.3...
rdcu.be
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JsonGeller @jgeller1phd.bsky.social · 01/10/2026
Erin asked if you can contact her: EBuchanan@harrisburgu.edu. She said you can have more than three columns just can't scroll sideways.
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JsonGeller @jgeller1phd.bsky.social · 01/10/2026
Seems like this could be a very useful feature to have
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JsonGeller @jgeller1phd.bsky.social · 01/10/2026
I can ask Erin how hard it would to be implement something like that.
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JsonGeller @jgeller1phd.bsky.social · 01/10/2026
Yes! I just submitted one on ex Gaussian Bayesian analyses in brms—Lots of people using x and x is not really doing what a lot of people think it is.
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JsonGeller @jgeller1phd.bsky.social · 01/10/2026
Have you thought about commentary and notes option at BRM?
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Reposted by JsonGeller
Glossa Psycholinguistics @glossapsycholx.bsky.social · 30/09/2026
We're excited to announce the publication of 'Readers can recognise multiple words at a glance but out of order: Evidence from transposed word effects in Japanese' by Kondo & Yano, available here: escholarship.org/uc/item/1jd2...
escholarship.org
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JsonGeller @jgeller1phd.bsky.social · 30/09/2026
If it is tutorial based then BRM has a tutorial series it could fall under.
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JsonGeller @jgeller1phd.bsky.social · 30/09/2026
@akmontoya.bsky.social probably has some good suggestions!
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JsonGeller @jgeller1phd.bsky.social · 30/09/2026
Feel this! Too bad p < .05 or * is all you need if you use some freq methods.
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JsonGeller @jgeller1phd.bsky.social · 29/09/2026
Anyone at Brain and Language journal know how to reach an actual person LOL.
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JsonGeller @jgeller1phd.bsky.social · 22/09/2026
This is kinda scary…
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JsonGeller @jgeller1phd.bsky.social · 22/09/2026
This is great!
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Reposted by JsonGeller
Dan Quintana @dsquintana.bsky.social · 19/09/2026
New preprint! 🎉 I analysed 1660 papers from 4 psychology journals and found materials sharing went from 9% of papers in 2015 to 82% in 2025, and these materials *do* get downloaded — a median of 135 times each. BUT shared code is often hard to run. doi.org/10.31234/osf... Let's walk through it 🧵
Two-panel figure. Panel a is a flow diagram tracking 1,611 empirical psychology articles from publication year (545 in 2015, 552 in 2020, 514 in 2025) to repository-link type: 785 link an OSF project, 85 link another platform, and 741 link no repository. Of those with an OSF link, download counts were retrieved for 670 and not retrieved for 115. Panel b is a line chart of the share of empirical papers linking OSF across 2015, 2020 and 2025. The overall rate, shown as a dashed black line, rises from 9% to 57% to 82%. All four journals rise steeply and end close together: Psychological Science 92%, JESP 89%, JML 83%, Cognition 76%, with Psychological Science highest throughout.Three-panel figure. Panel a: ridgeline plot of downloads per file by material type on a log scale, with the percentage never downloaded labelled for each — archive 16% of 545 files, documents 20% of 2,970, code 12% of 3,471, other 17% of 1,646, data 21% of 10,051, media 35% of 2,120, images 35% of 4,102. Most files cluster between 1 and 10 downloads, with long right tails past 100. Panel b: ridgeline plot of downloads per paper by journal, log scale, with dashed median lines; Psychological Science is highest, then JESP, JML and Cognition. Panel c: stacked bars showing, for documents, data and code separately, the share of papers by download band (0, 1–10, 11–100, more than 100) in 2015, 2020 and 2025. The share exceeding 100 downloads falls sharply over time in all three types, from roughly two-thirds in 2015 to a quarter or less in 2025, as the 1–10 band grows.Four-panel figure. Panel a: statistical languages detected among 333 papers with retrievable code — R 88%, SPSS 12%, Stata 4%, SAS 1%. Panel b: code red flags among those 333 papers — 34% hard-code an absolute path, 40% reference a missing file — above documentation among 672 OSF-linked papers — 21% have a README, 30% are documented by README, description or wiki. Panel c: among 562 Elsevier papers with no repository link, 44% (245) host at least one journal supplementary file but only 14% (77) host data, code or an archive. Panel d: composition of those 448 hosted files — documents 52%, data 21%, media 9%, other 7%, archive 6%, code 3%, images 1%. The code panels are green, the journal-supplement panels blue.Coefficient plot (download predictors)

Dot-and-whisker plot of three standardised predictors of OSF download volume, each with a 95% confidence interval. Repository size (number of files) has the largest effect at about 0.76, citations about 0.38, and altmetric attention about 0.12. All three intervals sit entirely above zero, so each predicts more downloads, with repository size roughly twice the effect of citations and six times that of attention. X-axis: standardised effect, −0.2 to 1.0.
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JsonGeller @jgeller1phd.bsky.social · 17/09/2026
Oh man! I didn't think we could get a close replication of this study and I think Netflix has done it! I'm glad there will be a director there coaching the prisoners and guards how to act.
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JsonGeller @jgeller1phd.bsky.social · 15/09/2026
Slap a pd on it and you are good to go :D
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JsonGeller @jgeller1phd.bsky.social · 15/09/2026
quantitude.org/s2e09-manova...
quantitude.org
S2E09: MANOVA Must Die – Quantitude
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JsonGeller @jgeller1phd.bsky.social · 15/09/2026
The one with Will Ramos from The deathcore band Lorna Shore is excellent!
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JsonGeller @jgeller1phd.bsky.social · 11/09/2026
Now worries! Share the materials when you have them :).
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JsonGeller @jgeller1phd.bsky.social · 11/09/2026
Submit! Submit! Please repost if you might have interested networks.
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JsonGeller @jgeller1phd.bsky.social · 11/09/2026
I want to attend but won't be able! Will it be recorded?
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JsonGeller @jgeller1phd.bsky.social · 11/09/2026
Join @felipefv.bsky.social next week if you can! He will chat about our most recent paper showing how to use nix and rix!
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JsonGeller @jgeller1phd.bsky.social · 10/09/2026
They were in Boston. What a fantastic show! Bruce never fails to amaze me.
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JsonGeller @jgeller1phd.bsky.social · 09/09/2026
Seeing Iron Maiden tonight. I wonder if @profandyfield.com approves.
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