Re: st: Simultaneously accounting for clustering at two different levels with vce(cluster) option

[Misha Spisok <misha.spisok@gmail.com>]

See, also, Cameron, Gelbach, and Miller, Robust Inference with
Multi-way Clustering, for which, I believe, the authors have developed
a Stata package.

On Tue, Mar 9, 2010 at 11:40 AM, Lily Yor <lilyyor1@gmail.com> wrote:
> Hi, I have data that is clustered at two different levels -- i.e., I have
> data at classroom level and at school level.  I need to account for these
> two sets of intraclass clustering, but the clusters at each level are too
> few to run a formal multi-level model (for example, there are only 8 schools
> in my data).  Thus, I would like to run a logistic regression model (my
> dependent variable is binary -- whether a student has passed a certain test
> or not) and adjust the standard errors by using vce(cluster) option, but
> there doesn't seem to be a way to simultaneously, in a single model, account
> for clustering at both levels (i.e., classroom level and school level).
>
> The way I have approached this problem thusfar is to run the logistic
> regression model with vce(cluster) option applied to account for classroom
> clustering, and then include school dummies in the same model, but this does
> not seem to be a satisfactory solution:
>
>  logit [dep var] [ind_var1] [ind_var2] [ind_var3] [school2] [school3]
> [school4] [school5] [school6] [school7] [school8], vce(cluster classroom_id)
>
> If you have any tips and suggestions on how I could use the vce(cluster)
> option to account for both classroom and school clustering, I would very
> much appreciate your help.
>
> Thank you so much.
>
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