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Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data.

4 votes
2 answers
70 views

When you have a variable that can be both fixed and random, is there a right way to define i...

I have an outcome variable painRating which represents how painful a participant found a sensation. I can reasonably expect ratings to be affected by a painful stimulation. Thus I would like to run a …
kaleidoscopic's user avatar
4 votes
1 answer
399 views

Should I remove a significant covariate from my mixed-effects model because it's underpowered?

I tried figuring out the answer by reading the comments in this thread but I am still confused. Should I remove a covariate from analysis when it comes as significant but power simulations finds that …
kaleidoscopic's user avatar
3 votes
1 answer
603 views

Causal inference from mixed-effects modelling on non-experimental repeated-measures data?

This is a more conceptual question and I think it highlights my lack of knowledge of what can be assumed using mixed-effects modelling on a non-experimental repeated-measures data. Let's pretend we ha …
kaleidoscopic's user avatar
5 votes
1 answer
253 views

How to enter confounding variables and variables competing for exposure into a mixed-effects...

Let's imagine I am interested in the association between happiness and pain. I run a study where I ask participants to rate both feelings, daily, for several days. However, I couldn't get all particip …
kaleidoscopic's user avatar