Robust Bias Detection in MLMs and its Application to Human Trait Ratings There has been significant prior work using templates to study bias against demographic attributes in MLMs. However, these have limitations: they overlook random variability of templates and target…

How to detect AI bias?

"Statistically significant" ≠ practically meaningful. Effect size matters - and template choice dramatically changes what #gender bias you detect.

Our fix: #mixedeffects + #perplexity weighting for robust detection.

arxiv.org/abs/2502.15600

#AI #NLP

Issues | Mixed Models with R This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mi...

#statstab #401 Common issues, conundrums, and other things that might come up when implementing mixed models

Thoughts: GLMMs are cool, but come with their own quirks.

#glmm #lmer #brms #mixedeffects #hierarchicalmodels #r

m-clark.github.io/mixed-models...

#statstab #329 Bayesian versus frequentist approaches in multilevel single-case designs: on power and type I error rate

Thoughts: An interesting project highlighting some benefits of #bayesian methods for #nof1 designs.

#stats #r #sced #mixedeffects

osf.io/k7b82/files/...

#statstab #328 How to Assess Task Reliability using Bayesian Mixed Models
by @Dom_Makowski

Thoughts: Nice walkthrough using {brms}, with code, data gen, and plots.

#r #bayesian #mixedeffects #reliability #brms

realitybending.github.io/post/2024-03...

#statstab #264 When estimating a treatment effect with a cluster design, you need to include varying slopes, even if the fit gives warning messages

Thoughts: Warnings are scary ⚠️ Bad model are scarier 👹

#lmer #modelfit #mixedeffects #r #randomslopes

statmodeling.stat.columbia.edu/2025/01/23/s...

#statstab #221 #brms posterior_epred() vs posterior_predict()

Thoughts: When starting off with bayesian mixed models you'll run across this issue. Here's one of the best forum posts on it.

#bayesian #mixedeffects #models #posterior #effects #prediction

discourse.mc-stan.org/t/confusion-...

Mixed model equivalence test using R and PANGEA | Peder M. Isager Personal website of Dr. Peder M. Isager

#statstab #199 Mixed model equivalence test using R and PANGEA

Thoughts: While there are easier ways to compute #EQ tests for such models now, it is nice to see how you'd do so manually.

#equivalencetests #NHST #mixedeffects #r #stats #nullresults

pedermisager.org/blog/mixed_m...

Power Simulation: A primer in 3 languages - Power Simulation in a Mixed Effects design using R

#statstab #130 Power Simulation in a Mixed Effects design using R

Thoughts: I used {faux} in my last blog post. Useful package if you think you can anticipate your data (v onerous in mixed effects).

#r #simulation #power #NHST #mixedeffects #lmer #stats

cjungerius.github.io/powersim/

Mixed Models | Mixed Models with R This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mi...

#statstab #112 Mixed Models with R

Thoughts: A very nice overview of what mixed models are, how to use them, and how to interpret the results (even mentions issues with p-values).

#rstats #r #lmer #mixedeffects

m-clark.github.io/mixed-models...

What is the minimum recommended number of groups for a random effects factor? I'm using a mixed model in R (lme4) to analyze some repeated measures data. I have a response variable (fiber content of feces) and 3 fixed effects (body mass, etc.). My study only has 6 participan...

#statstab #68 Minimum number of levels for a random effect

Thoughts: Fixed effects and random effects are not always intuitive differences. Apparently >5 is ok for an re.

#r #MLM #mixedeffects #rstats #stats

stats.stackexchange.com/questions/37...

GitHub - jmgirard/varde: R Functions for Variance Decomposition R Functions for Variance Decomposition. Contribute to jmgirard/varde development by creating an account on GitHub.

#statstab #51 R Functions for Variance Decomposition {varde}

Thoughts: A useful package to get more insight into your mixed effects model.

#r #rstats #mixedeffects #lmm

github.com/jmgirard/varde

A Visual Introduction to Hierarchical Models A visual explanation of multi-level modeling

#statstab #45 Visualizing Hierarchical Models

Thoughts: One of the coolest visualisation websites for mixed effects models I had saved.

#stats #mixedeffects #hierarchicalmodels #rstats #MLM

mfviz.com/hierarchical...

A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models - Journal of Nonverbal Behavior Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. However, this practice overlooks important sources of variability resulting in pote...

🚨New paper: A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

w/ @matti.vuorre.com

A better way to analyse lie data.

#deception #SDT #mixedeffects #brms #rstats

link.springer.com/article/10.1...

#statstab #19 Model Estimation Options, Problems, and Troubleshooting

Thoughts: A nice guide on model convergence issues, REML vs FIML, and model comparison (frequentist s)

#mixedeffects #rstats #stats

www.learn-mlms.com/07-module-7....

#statstab #16 Maximal Random Structure (mixed effects models)

Thoughts: A very nice tutorial and explanation on the benefits of Mixed effects models, including some useful advice and advanced tips.

#stats #lmm #mixedeffects #randomeffects #power

www.alexanderdemos.org/Mixed7.html#...

This tutorial was a great refresher on creating #mixedeffects #models in #R using #lme4.
http://www.bodowinter.com/tutorial/bw_LME_tutorial2.pdf