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Max Primbs

@maxprimbs.bsky.social

2.3K followers 190 following 260 posts

PhD Student, Radboud University (Netherlands). Researching how our social and cultural environments shape our implicit biases. Big Team Science & Statistics enthusiast.

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Max Primbs @maxprimbs.bsky.social · Dec 16

New paper out in PSPB: journals.sagepub.com/doi/10.1177/...

We investigated whether historical Ku Klux Klan presence in an area is associated with higher levels of modern-day implicit bias.

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Eric Hehman @erichehman.bsky.social · 3d

We have a new tutorial out in Social Cog methods issue, a resampling tool we made in R that basically tells you when some average is "stable" and can be used to guide data collection or test hypotheses related to variance. Quick explanation here
1/n

guilfordjournals.com/doi/10.1521/...

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Max Primbs @maxprimbs.bsky.social · 4d

Is anyone I know willing to share their cover letter for an American postdoc offering?

I have plenty of faculty materials, but none for a Postdoc yet.

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Daniel Lakens @lakens.bsky.social · 6d

End of the Paul Meehl Graduate School Symposium, with wonderful chats over dinner. Last year was great, this year even better! So nice to talk to this crowd of metascientists.

Thanks to @sajedehra.bsky.social for the organization of my favorite metascience event of the year!

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Victoria @ Real Scientists DE @realscientists.de · 7d

Guten Morgen! Heute berichte ich euch vom Paul Meehl Graduate School Meta Research Symposium, das von meiner PhD Kollegin @sajedehra.bsky.social und meinem Supervisor @lakens.bsky.social organisiert wird! 📈

Intro-Slide für das Paul Meehl Graduate School Meta Research Symposium 2025

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Daniel Lakens @lakens.bsky.social · 7d

Now @maxprimbs.bsky.social talking about his super exciting Many-Dags project, asking experts in his field of implicit researchers to draw DAGs about a theory in his field.

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Max Primbs @maxprimbs.bsky.social · 7d

Tut mir Leid der Überbringer schlechter Neuigkeiten zu sein, aber Otter sind anscheinend auch ganz schrecklich: www.alieward.com/ologies/lutr...

Lutrinology (OTTERS) with Dr. Chris J. Law — alie ward

YOU’RE NOT READY. But it’s time. Otters. Sea otters. River otters. Big beefy otters. Tiny otters. Giant river otters. Otters chasing you down the street. Dr. Chris J. Law, a professional Lutrinologist...

www.alieward.com

Reposted by Max Primbs

Eric Shuman @ericmshuman.bsky.social · 18d

I am hoping to recruit a Ph.D. student to join the SPARC (Social Psychology of Activism, Resistance, & Change) Lab at @UVAPsyc in Fall 2026! You can find more info about my research on my website (ericshuman.com), and the program here (psychology.as.virginia.edu/social-psych...).

Home

ericshuman.com

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Gordon Hodson @gordonhodsonphd.bsky.social · 21d

Cool new study by @joelleforestier.bsky.social @page-gould.bsky.social & Alison Chasteen

Can social media contact reduce prejudice?

#PrejudiceResearch

psycnet.apa.org/fulltext/202...

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Max Primbs @maxprimbs.bsky.social · 22d

Congratulations 🎉🎉🎉

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Gordon Hodson @gordonhodsonphd.bsky.social · 23d

#AcademicSky

It's also apparent that our PhD students are feeling that they don't get enough time with advisors

(I realize that "not having enough time" is a perpetual complaint in academia, but maybe we should do less X and spend more time w/ our mentees?)

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Max Primbs @maxprimbs.bsky.social · 24d

Congratulations 🎉🎉🎉

Max Primbs @maxprimbs.bsky.social · Sep 22

Thanks for sharing this, I appreciate people trying to make the hidden curriculum more open.

Wish I'd had known that when I started applying last month, haha.

Max Primbs @maxprimbs.bsky.social · Sep 20

Right on time to make sure that no US university invites an international for interviews :)

Max Primbs @maxprimbs.bsky.social · Sep 18

A meta-scientist's proudest moment.

Max Primbs @maxprimbs.bsky.social · Sep 16

📌

Max Primbs @maxprimbs.bsky.social · Sep 15

If you are still looking for an outlet, I made really good experiences with PSPB with publishing counter-intuitive or null results that contradict previous findings!

Max Primbs @maxprimbs.bsky.social · Sep 15

That would be a super cool project to do with an actual historian! The materials underlying the historical database where we got our KKK data from should have some of that information!

Reposted by Max Primbs

Samantha Joel @datingdecisions.bsky.social · Sep 11

For potential grad students: if you're interested in this kind of work on assessing and improving close relationships measures, our group just got a grant to do five more years of it. Join our team!

Samantha Joel @datingdecisions.bsky.social · Sep 10

In a new paper, my colleagues and I set out to demonstrate how method biases can create spurious findings in relationship science, by using a seemingly meaningless scale (e.g., "My relationship has very good Saturn") to predict relationship outcomes. journals.sagepub.com/doi/10.1177/...

Pseudo Effects: How Method Biases Can Produce Spurious Findings About Close Relationships - Samantha Joel, John K. Sakaluk, James J. Kim, Devinder Khera, Helena Yuchen Qin, Sarah C. E. Stanton, 2025

Research on interpersonal relationships frequently relies on accurate self-reporting across various relationship facets (e.g., conflict, trust, appreciation). Y...

journals.sagepub.com

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Max Primbs @maxprimbs.bsky.social · Sep 9

@annemscheel.bsky.social @lakens.bsky.social maybe?

Either way, their paper is a good read for students.
pubmed.ncbi.nlm.nih.gov/33326363/

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Max Primbs @maxprimbs.bsky.social · Sep 8

📌

Max Primbs @maxprimbs.bsky.social · Sep 7

📌

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Society for the Improvement of Psychological Science (SIPS) @improvingpsych.org · Aug 30

PsyArXiv is now down to only ~40% of the backlog we started with, thanks to our amazing moderators 🎉 To all mods: thank you so much for your hard work!

Did one of your preprints get approved recently? Help us show our thanks by liking and sharing this post :)

#PsyArXiv #PsychSciSky

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Gordon Hodson @gordonhodsonphd.bsky.social · Aug 28

#SocialPsychology #PsychSciSky

Submitting an EASP 2026 symposium on intergroup relations, contact, or dehumanization?

If you think I could add to your symposium, drop me a DM.

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Max Primbs @maxprimbs.bsky.social · Aug 27

Good luck 🤞

Reposted by Max Primbs

Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25

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...

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.

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