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I want to get a deepened understanding of random effects models and how they compare to fixed effects models. While there are some great answers already given by the community the following question remains unclear to me:

Broadly speaking one could say that before analyzing values across different units of analysis, each value in a fixed effects model gets subtracted by the mean value of the respective variable, right? Thus, all variation in a fixed effects model is relative to the unit of analysis "baseline-level". Consequently, applying fixed effects models with data with no/few variation is not useful.

I read in several publications that random-effects models are in such settings a better option. So do random effects models similar to fixed effects model consider within variation before they analyse variation between units?

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RE models rely on within- and between-group variation, while FE models only rely on within-group estimation.

However, this is only true if the explanatory variable is independent from group specific effects. If this is not the case RE are biased.

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