Call for Submissions for the Theory Methods Conference 2026, September 30-October 2! theorymethodssociety.org/conference.h...

We invite you to:
1) Submit your proposal: edu.nl/mj9x6
2) Invite your colleagues/lab/(PhD) students, and encourage them to submit
3) Share this post

WARN-D machine learning competition is live » Eiko Fried If you share one single thing of our team in 2026—on social media or per email with your colleagues—please let it be this machine learning competition. It was half a decade of work to get here, especi...

After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data.

If you share a single thing of my lab this year, please make it this competition.

eiko-fried.com/warn-d-machi...

Congratulations Eiko😊

We built the openESM database:
▶️60 openly available experience sampling datasets (16K+ participants, 740K+ obs.) in one place
▶️Harmonized (meta-)data, fully open-source software
▶️Filter & search all data, simply download via R/Python

Find out more:
🌐 openesmdata.org
📝 doi.org/10.31234/osf...

A huge thanks to my supervisors @donvanraven.bsky.social & Henk Kiers for their guidance and support!

For a deeper dive into statistical interactions & model misspecification, check out our related preprint: doi.org/10.31234/osf...

To interact or not to interact: The pros and cons of including interactions in linear regression models - Behavior Research Methods Interaction effects are very common in the psychological literature. However, interaction effects are typically very small and often fail to replicate. In this study, we conducted a simulation compari...

Excited to share that my first PhD paper got published! We explored the effects of correctly vs. failing to model true interactions in data. Can model misspecification lead to reversed conclusions? Which model generalizes better to a larger sample?

Read the article here: doi.org/10.3758/s134...

New preprint w/ the WARN-D team (incl. @eikofried.bsky.social @rayyantutunji.bsky.social, @aljoscharimpler.bsky.social & others): We explain our exploration of EMA items in data of ~600 individuals. We investigate distributions/changes over time/context/interindividual differences & more

Thank you both for your kind words. I had great guidance and it was a pleasure to be part of the team!