Björn Siepe

@bsiepe.bsky.social

PhD Student in Psychological Methods (Marburg University) Interested in time series, simulation studies & open science https://bsiepe.github.io

To celebrate the openESM paper release, I wrote a brief blog post summarizing recent developments: openesmdata.org/blog/2026-07... We: 💠added more data 💠provided descriptive visualizations for each item 💠created a semantic similarity mapping for items, making it easier to find related items

Updates: Paper published, new dataset & features

Our tutorial paper on openESM has been published, and we have added new datasets and features to the platform.

openesmdata.org

Daniel Heck@danielheck.bsky.social · 3w ago

Our article introducing the #openESM database for experience samling data, led by @bsiepe.bsky.social, has now been published 🎉 Paper: link.springer.com/article/10.3... openESM interactive website: openesmdata.org GitHub repository: github.com/openesm-proj...

openESM logo

Last week, I defended my PhD! The past 3.5 years were the most intense, joyful, and unpredictable years of my life. I'm grateful for learning from many inspiring researchers and making great friends along the way. Thanks to everyone who was there for me and made this time truly special!

An image of me wearing my doctoral hat, smilingly unwrapping a present. Behind me are glasses of prosecco, water, and orange juice.

In addition to a hiring PhD in ~October & hopefully some PhDs and postdocs ~February, if anyone would like to work with me as part of eg a Marie-Curie or something similar, please do get in touch. Topics include depression, classification, measurement, systems, time-series, & theory building.

We had the privilege of hosting @bsiepe.bsky.social for this talk on the openESM database! 🎉✨ If you couldn't make it to the talk, you can now view the recording on YouTube: www.youtube.com/watch?v=6HPn... #ESM #openscience #rstats #intensivelongitudinaldata

Introducing openESM: A database of openly available experience sampling datasets by Björn Siepe

YouTube video by Psych #rstats Club

youtube.com

Psych #rstats Club @psychrstats.bsky.social · 5mo ago

🔊 ❗We are excited to be collaborating with Björn Siepe @bsiepe.bsky.social for a new free upcoming talk on openESM! The talk will be “Introducing openESM: A Database of Openly Available Experience Sampling Datasets.” Check out the flyer for more details. ✨Register now: forms.gle/2W2Mhh5V2vvY...

Poster for the talk by Björn Siepe on OpenESM database. The poster contains further information about the event along with the QR code for registration.

A new paper for my PhD has been published! In psychology, the default for studying the ubiquitous explanation ‘it-depends’ is the use of an interaction/moderator. This default creates tension between theory and model, yielding false positives and negatives. doi.org/10.1007/s421...

Anything Goes: Statistical Interactions Without Substantive Theory - Computational Brain & Behavior

Conditional effects, or interaction effects, do not imply multiplicative effects. However, product terms are the default method for modeling such conditional effects in psychological research. As a result, theoretically plausible conditional effects may go undetected when the functional form is misspecified. Our study had two objectives: (1) evaluate the extent to which non-linear phenomena can be identified as spurious multiplicative (i.e., standard) interaction terms in linear models, (2) assess how well linear models capture stepwise conditional effects. In Study 1, we examined spurious interactions from non-linear main effects. We found that traditional interaction terms were associated with increased Type-I error rates and small effect sizes. Importantly, this was also the case when the predictors were uncorrelated, indicating a mechanism beyond collinearity. Additionally, we found that, if captured, the spurious interaction effects did reduce prediction error on the population level. In Study 2, we simulated genuine conditional effects, following a stepwise pattern. When effects were monotonic, product terms performed adequately, however if the conditional effect is non-monotonic a traditional interaction term in a linear model does not sufficiently capture such an effect. We conclude that relying solely on traditional interaction terms in linear models can be misleading and the failure to replicate interaction effects may partly reflect a specification crisis: Researchers default to one functional form (multiplication) while the underlying theory may dictate a different form, creating a systematic mismatch between theory and model. To validly investigate conditional effects, researchers should specify and justify the expected functional form a priori.

doi.org

Does it make sense to preregister simulation studies? This question has sparked a lot of debate. ▶️We* work through the why, when, and how ▶️We discuss different phases of methodological research to clarify where preregistration might (or might not) add value 📝 Preprint: doi.org/10.31234/osf...

Diagram showing four phases of methodological research (Theory, Exploration, Systematic Comparison, Evidence Synthesis) with an arrow indicating that preregistration usefulness increases from early to late phases. Each phase lists its aim, elements, outcome, and an example from factor retention research.

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...

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...

eiko-fried.com

Two new preprints on multilevel HMMs! Time series data is now pervasive in psychology and new methods are needed to model the dynamics in such data. Hidden Markov Models (HHMs) are powerful models for dynamics in which a system is switching between a number of discrete states.

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