Back to thinking about group-level centering for #multilevelmodels. The advice seems to be to partition individual-level scores into two variables: the group mean and the deviation from that mean. However, I have also seen people partition scores into the group baseline and the deviation from that baseline (ie the change score). This is a particularly common way to code age in my field. How should I think about the divergent results from these two approaches?

Take a weird dive into the Intraclass Correlation Coefficient (ICC) with my newest statistics meditation! 💖🤓🌌

https://youtu.be/PqFJ2cggFfY

How can the ICC be a correlation and a proportion of variance at the same time? Zone out to this question, the chickens, and the roosters. 🐓🎧

This is probably most interesting to you if you are already mildly motivated to think about the #ICC. #Statistics #Meditation #IntraclassCorrelation #MixedModels #MultilevelModels #Correlation #VarianceComponents #STEAM

New post! If you think of "marginal effects" as slopes, the terms "marginal effects" and "conditional effects" aren't quite the same thing in the world of multilevel models, which is *so confusing*.

I recreate a post by @kristoffer to show the differences between the two kinds of effects using @vincentab 's phenomenal {marginaleffects} package

https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/

#rstats #statsodon #bayesian #MultilevelModels

I am a harmless wandering anthropologist, bringing 20 hours of free #CausalInference and #BayesianStatistics instruction to your door. From foundations of inference through DAGs, #MultilevelModels & poststratified causal effects to #GaussianProcesses, Bayesian imputation & ODEs. Theatrical trailer below. Playlist: https://www.youtube.com/playlist?list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN