I am fitting a logistic model with random effects to panel data. For my data,

Can someone point me to a discussion of the differences between the approaches embodied by the two procedures? I expect it is at a "philosophical" level, not just different pragmatic choices in the coding of the two procedures.

Since I'm analyzing panel data,

My interest is to learn a little more about these two approaches to the same problem, and to understand any differences in the underlying assumptions. I'm hoping there's something already written that I didn't find in searching Statalist and using Google to search more widely.

Thanks!

**xtlogit ..., re**and**melogit ... || panel_id:**yield essentially identical results. The only noticeable differences is that**xtlogit**takes painfully longer to run.Can someone point me to a discussion of the differences between the approaches embodied by the two procedures? I expect it is at a "philosophical" level, not just different pragmatic choices in the coding of the two procedures.

Since I'm analyzing panel data,

**xtlogit**seems the natural choice for simple random effects - I'm not looking at a more complicated mixed model that only**melogit**can handle.. I'm reassured that for this simple case,**xtlogit**and**melogit**yield essentially identical results.My interest is to learn a little more about these two approaches to the same problem, and to understand any differences in the underlying assumptions. I'm hoping there's something already written that I didn't find in searching Statalist and using Google to search more widely.

Thanks!

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