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st: Multilevel difference modeling with suest


From   Peter Goff <peter.t.goff@vanderbilt.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Multilevel difference modeling with suest
Date   Sun, 18 Mar 2012 15:42:51 -0500

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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