We reviewed 100 psych. simulation studies & find room for improvement in planning/reporting. As a remedy, we (@BartosFra, @tmorris_mrc, @BoulesteixLaure, @Daniel_W_Heck & Samuel Pawel) present ADEMP-PreReg, a sim study preregistration & reporting template https://t.co/kmWdgydo5F

Have you run #simulation studies and encountered issues with missing results, failure, or non-convergence? In a new preprint by Samuel Pawel, @b_siepe & @annloh, we overview different approaches to address missingness and review current practices. https://t.co/KSXQz2tNht #Stats

New preprint by @b_siepe & WARN-D team. We collected information on sleep, tiredness & stress with both EMA and sensor data, and try to find out to which degree these are associated contemporaneously in a large dataset (n~800) with many timepoints (t~360). Brief 🧵

Just in time for conference season: Our paper on simulation studies in psychology was accepted at Psychological Methods! 🥳See the thread below for a summary, the updated preprint is available at

The 1st talk in Measurement & Reasoning is “Simulation Studies for Methodological Research in Psychology” by Björn Siepe @b_siepe https://t.co/JTaaRX00Ja #iccmpsyched

Should you use Likert or VAS for EMA research? In a new paper led by @jonashaslbeck & @AlbertoJover (last author @EatingLab), we assigned n~160 in a 2 week study to either 📏1-7 Likert or 🎚️1-100 Visual Analogue Scale and compared results using Bayesian multilevel models.

For those of you who are into prediction, considering joining this fascinating challenge run by my colleague @Gert_Stulp_or_G ! You will be asked to predict who will have a child in the next three years based on both panel survey data and administrative data.

1/8 New tutorial preprint led by @b_siepe in which we present different descriptive statistics & data visualization techniques with the goal to better understand EMA item functioning. Preprint: https://t.co/3RsnEuup3l Brief overview thread 🧵:

1/4 FRED is online, our free open source software to provide feedback to participants in EMA studies. We (led by @AljoschaRimpler) programmed the software to provide feedback at scale to our ~2000 WARN-D participants. Hope it will be useful to researchers & clinicians alike!

Maybe someday I will have the courage to actually go for the pun in the title... Until then, check out our new preprint on multiverse for dynamic network/GIMME models, featuring ~750k individual networks and a shiny app to explore results

Thanks to @b_siepe @BartosFra and S. Pawel for involving me in this exciting project together with @tmorris_mrc and @Daniel_W_Heck on planning and reporting of simulation studies (using ADEMP) in psychological methods research!

We believe that the time is ripe for a shift in methodological research toward more rigor and transparency in simulation studies and that our ADEMP-PreReg template can help to achieve this goal.

We provide Monte Carlo standard error calculations and sample size planning formulas for the most common performance measures and showcase the ADEMP methodology in an example simulation study.

We also summarize general recommendations to improve simulation methodology.

The template helps with design & reporting of sim studies by prompting questions about Aims, Design, Estimands, Performance Measures, Reporting, and Computational Aspects. As such, it's helpful for both sim study novices seeking guidance & experts wanting a standardized blueprint

We overview the ADEMP framework from Morris et al. (2019) adapted to simulation studies in psychology. In our review, we find that the majority of studies do not justify the sample size, do not report Monte Carlo uncertainty, and do not provide code or computational details.

👇 REGISTRATION OPEN! 👇 https://t.co/f6L33rJv6Z 📢 #OpenScience Days Marburg, part of #BrainhackGlobal 2023! 🗓️ Dec 4-5 in 🏰 Marburg, Schulstrasse 12 🏫 mornings: short intensive talks 💻 afternoons: hands-on sessions & project work 💬 tuesday 4pm: Open Science discussion

💻Test & additional plotting functions (see below for an example on posterior uncertainty coefficient matrix) implemented in the R package 'tsnet' on GitHub ( https://t.co/5oMSBqBvda); all code available on OSF. Joint work with @Daniel_W_Heck.

Brief summary: existing LASSO-based methods often work well, esp. w/ few timepoints & sparsity. Bayesian estimation is strong in denser networks. The test (which assess overall differences between networks based on matrix norms) is conservative & has good false-positive rates.

We 1) evaluate an existing Bayesian method to estimate idiographic networks & 2) propose a novel test that evaluates the evidence that differences in edges between two models are not just due to sampling variability. We then apply estimation & test methods to empirical data.