Bonus: an R package to pre-process and extract postural indicators (e.g. average CoP, sway path length, confidence ellipses). I hope this will help with transparency.
📦 : github.com/JeanMoneger/BalanceMate/
📖 : jeanmoneger.com/book/
If you work with force plates, I’d love your feedback on this! 🙏

Bottom line: Force plates are great tools! Ecological, instruction-free, and capture potentially automatic behaviors — But they’re not magic. Researchers need to be transparent about how signal is processed and stay realistic about the statistical power they can achieve.

💡Recommendations:
- Greater transparency in reporting procedures.
- Larger sample sizes
Do not base your sample size on the average postural study, our meta-analysis suggest this might not be enough. We recommend aiming to detect effect sizes consistent with other approach/avoidance measures.

Many papers miss core methods (initial posture, stimulus arousal, force-plate type, pre-processing).

Also, average sample size = 40 participants --> With this sample size, you’d be expecting an effect “detectable to the naked eye” (to paraphrase Cohen), dz ≈ 0.45!

With Dorine Vergilino-Perez, @lchaby.bsky.social, & Chrystel Besche-Richard, we meta-analysed force-plate studies on body sway while viewing (un)pleasant stimuli.

=> Weak valence effect on posture.

🧌This "bestiary" can be a vital resource for researchers, educators, and reviewers to recognize, understand, and mitigate QRPs, ultimately raising the standard of psychological research. Read the full article here: journals.sagepub.com/doi/10.1177/...