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I'm running a mixed-effect logistic regression in Stata 16.0.

I can't say much about the data because it's confidential. Nevertheless, here is a fictional example: If we sample students within schools, and include a random intercept for "School", why might a key fixed effect predictor in our models (e.g., food security level on GPA) shift majorly, versus when we did not include a random intercept?

I'm trying to work this out conceptually to explore my own data. If anyone can explain this in simple conceptual terms, that would be greatly appreciated.

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