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From |
Cameron McIntosh <cnm100@hotmail.com> |

To |
STATA LIST <statalist@hsphsun2.harvard.edu> |

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

Date |
Sun, 18 Mar 2012 20:18:05 -0400 |

Peter, I think the main issue is how exactly to handle the difference scores in the model... my opinion is don't bother with them at all, they're dangerous. I would strongly recommend using polynomial regression instead, for reasons discussed in detail in: Edwards, J.R. (2002). Alternatives to difference scores: Polynomial regression analysis and response surface methodology. In F. Drasgow & N. W. Schmitt (Eds.), Advances in measurement and data analysis (pp. 350-400). San Francisco: Jossey-Bass. Edwards, J.R. (2001). Ten difference score myths. Organizational Research Methods, 4, 264-286.http://public.kenan-flagler.unc.edu/faculty/edwardsj/Edwards2001b.pdf Edwards, J. R., & Cable, D. M. (2009). The value of value congruence. Journal of Applied Psychology, 94(3), 654-677.http://public.kenan-flagler.unc.edu/faculty/edwardsj/EdwardsCable2009.pdf Edwards, J.R. (2007). Polynomial regression and response surface methodology. In C. Ostroff & T.A. Judge (Eds.), Perspectives on organizational fit (pp. 361-372). San Francisco: Jossey-Bass.http://public.kenan-flagler.unc.edu/faculty/edwardsj/Edwards2007.pdf Klein, G., Jiang, J.J., & Cheney, P. (2009). Resolving Difference Score Issues in Information Systems Research. MIS Quarterly, 33(4), 811-826. Cafri, G., van den Berg, P., & Brannick, M.T. (2010). What Have the Difference Scores Not Been Telling Us? A Critique of the Use of Self—Ideal Discrepancy in the Assessment of Body Image and Evaluation of an Alternative Data-Analytic Framework. Assessment, 17(3), 361-376. Venkatesh, V., & Goyal, S. (2010). Expectation Disconfirmation and Technology Adoption: Polynomial Modeling and Response Surface Analysis. MIS Quarterly, 34(2), 281-303. Cohen, A., Nahum-Shani, I., & Doveh, E. (2010). Further Insight and Additional Inference Methods for Polynomial Regression Applied to the Analysis of Congruence. Multivariate Behavioral Research, 45(5), 828-852. Shanock, L.R., Baran, B.E., Gentry, W.A., Pattison, S.C., & Heggestad, E.D. (2010). Polynomial Regression with Response Surface Analysis: A Powerful Approach for Examining Moderation and Overcoming Limitations of Difference Scores. Journal of Business and Psychology, 25(4), 543-554. As for the nesting issue, Taylor linearization or bootstrapping would be reasonable options, as I gather that you are not really interested in explicitly modeling the hierarchical structure and only regard within-cluster correlation as a nuisance parameter. I think you could implement your polynomial regression through -cmp-. Roodman, D. (2011). Fitting fully observed recursive mixed-process models with cmp. The Stata Journal, 11(2), 159-206.http://www.stata-journal.com/article.html?article=st0224 http://ideas.repec.org/c/boc/bocode/s456882.html Hope this helps, Cam > From: 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 > > > * > * 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/ * * 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**:**st: Multilevel difference modeling with suest***From:*Peter Goff <peter.t.goff@vanderbilt.edu>

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