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From |
Debs Majumdar <debs_stata@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Comparing two response variables |

Date |
Mon, 28 Feb 2011 19:32:03 -0800 (PST) |

Hello, I should have been more clearer with the question. The regressions were performed for case-mix adjustment. The predicted values from these regressions were then used to to generate ranks. The process is as follows: The data is collected from 10000 students who had answered questions (ordinal items) regarding their teachers (500). There were two total scores computed out of their responses, one with 5 items and one with 7 (same 5 + additional 2). I have been told that this new one (total score out of the 7 items) makes more sense qualitatively. I just want to figure out if one can quantify this. Initially, case-mix adjustment is done for each of the scores with respect to student characteristics (gender, major etc.) using `-areg'. Then the dataset is collapsed by teachers and in the last step the teachers are ranked. I have the same opinion as Joerg that one one really need ans external variable to justify this. The other thing I was thinking if I can use any resampling techniques where I generate data corresponding to the null hypothesis that both are equally good and so on. I don't know if that's feasible for this case though. Thank you, Debs ----- Original Message ---- From: Joerg Luedicke <joerg.luedicke@gmail.com> To: statalist@hsphsun2.harvard.edu Sent: Mon, February 28, 2011 4:52:15 PM Subject: Re: st: Comparing two response variables On Mon, Feb 28, 2011 at 6:20 PM, Debs Majumdar <debs_stata@yahoo.com> wrote: > Is there anyway to prove Y1 is a better measure for the trait we are measuring > when compared to Y2? "Better" with regard to what? As far as I understand your question you mean something like "better" in the sense of higher validity of your measurement instrument. Only if you know exactly how the proposed relation to x1 and x2 is supposed to look like in this multivariate context, e.g. if your measure was validated before in other studies, the model could provide some evidence in favor of the one or the other. In the absence of such knowledge, there is no model that would tell you that. Basically, you need external and conceptual information to decide on what the better measure is. J. * * 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/

**Follow-Ups**:**Re: st: Comparing two response variables***From:*Joerg Luedicke <joerg.luedicke@gmail.com>

**References**:**st: Comparing two response variables***From:*Debs Majumdar <debs_stata@yahoo.com>

**Re: st: Comparing two response variables***From:*Joerg Luedicke <joerg.luedicke@gmail.com>

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