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

[Lily Yor <lilyyor1@gmail.com>]

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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