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From | Peter Goff <peter.t.goff@vanderbilt.edu> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | RE: st: Multilevel difference modeling with suest |
Date | Mon, 19 Mar 2012 15:50:59 -0500 |
T = a + zX + e P= b + yX + u (T-P) = (a-b) + (z-y)X + (e+u)Any comments on whether the method I outline is appropriate for multi- level modeling of seemingly unrelated regression and whether I have identified the appropriate approach to test for non-zero difference between coefficients is kindly appreciated. That is, will this approach provide the appropriate standard errors to test:
z = 0 y = 0 (z-y) = 0To be clear, principal self-evaluations (P) are constant within principals but vary between principals. Teacher evaluations of principals vary within and between principals. Some of the X variables are teacher-level and vary both between and within principals; others are principal-level variables and only vary between groups.
Kind thanks, ~Peter 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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