Re: st: In this particular case: should I prefer clustering or a random-effects model

[Andrea Bennett <mac.stata@gmail.com>]

Thanks for the link and your help!

I indeed do have a cluster-randomized design (treatment intervention was on the class level).

With respect to FE. I have included fixed-effects dummies for each class. This results in dropped variables (classID dummies) and renders the treatment intervention to be insignificant. Performing a standard regression with <reg score treatment controls, cluster(classID)> is fine. Performing <xtreg score treatment controls, i(classID) mle/re> is fine too while <xtreg ... , i(classID) fe> results in dropped independent variables (which measure differences on the class level).

But just from a theoretical point of view, I thought that a random effects model would be preferred because then I would treat the effects of "classID" as a random sample of the effects of all the classes in the full population.

Best regards!

Andrea


On Jul 7, 2011, at 14:37 , Austin Nichols wrote:

> f in fact you have a cluster-randomized design, you should have
> calculated power (required sample size, minimum detectable effect
> size, etc.) in advance assuming the analysis design (pooled, FE,
> multilevel hierarchical model, etc.) to be used once data is
> collected, using e.g.
> http://www.urban.org/publications/1001394.html
> or your own custom simulations, so you should not be designing the
> analysis after the data has been collected!


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