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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Multilevel difference modeling with suest |

Date |
Mon, 19 Mar 2012 17:40:40 -0400 |

Peter Goff <peter.t.goff@vanderbilt.edu>: First, read help _mtest to see why you can't test one coef after another without distorting size of tests. However, I don't see how testing for nonzero differences in coefficients is helpful in your case. Imagine T is P times 2 plus noise; coefs in the T eq are roughly twice as large. I.e. T evaluate bad P twice as harshly and good P twice as kindly on average, but otherwise rankings are identical in expectation. What does it mean that you can reject z=y then? The equations are the same in any real sense. Using rank or standardizing need not solve the problem of comparability. Maybe you can cap ssc inst ivreg2 ivreg2 P (T=X*), cl(Pid) where the null of the overid test is that X has no impact on P except via T; if there is an independent effect of an X on P you should reject the null. This is a highly nonstandard use of an overid test, and I have not thought through the implications or assumptions. Not at all in the -mvreg- frame, for sure. Another thing to worry about: do X variables also affect skewness of T evaluations? The conditional mean may not be the best measure of a typical teacher's evaluation. On Mon, Mar 19, 2012 at 4:50 PM, Peter Goff <peter.t.goff@vanderbilt.edu> wrote: > Thanks for your thoughts on the modeling the Teacher-Principal (T-P) > difference. As it turns out, polynomial regression is a great method for > modeling differences between variables when the differenced quantity of > interest is an independent variable. However, in the situation below I'd > like to use a vector of independent variables (X) to model/predict the T-P > difference as the dependent variable. > > 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) = 0 > > To 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 > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: Multilevel difference modeling with suest***From:*Peter Goff <peter.t.goff@vanderbilt.edu>

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