st: Multilevel difference modeling with suest

[Peter Goff <peter.t.goff@vanderbilt.edu>]

Hi All,

I'm trying to determine the best way to tackle what has been a bit of  
a slippery problem. My goal is to determine which factors (X) are  
predictive of the difference between how teachers perceive a  
principal's leadership (T) and how the principal perceives their own  
leadership (P). X contains some teacher-level factors (e.g., teacher  
experience) and some principal-level factors (e.g., principal gender).  
The literature suggests that the best approach to this problem is to  
model these equations jointly and then individually test for  
differences between the coefficients in X. To complicate matters  
somewhat, teachers are nested within principals so sureg or mvreg  
can't be used, since neither can accommodate the clustering. I have  
pursued several suggestions from colleagues and archived statalist  
posts (e.g., http://www.stata.com/statalist/archive/2009-04/msg01157.html) 
 that has landed me a bit further from my comfort zone that I'd like.  
I'd like to present what I have done thus far and hear if anyone has  
criticism or alternative suggestions.

reg T X
	estimates store t1
reg P X
	estimates store p1
suest t1 p1, vce(cluster prinid)
foreach x in X {
	test _b[t1_mean:`x'] - _b[p1_mean:`x'] = 0
}

In terms of an interpretation, I'd like to use the t1_mean equation  
from the suest results to make statements about how each of X factors  
relate to teachers' perceptions of leadership effectiveness; use  
p1_mean suest results to make statements about how each of X factors  
relate to the principals' perceptions of their own leadership  
effectiveness; and use the test results to make statements about how  
each of X factors relate to the teacher - principal gap. Kind thanks  
for your thoughts and insights.

Peter
peter.t.goff@vanderbilt.edu


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