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From |
Tinna <statalist@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Can Y be a predicted variable? |

Date |
Fri, 9 Sep 2005 18:43:04 -0400 |

Thanks for the answers Constantine and Roger. I am not sure it matters now that my "genius" idea has been shot down, but let me still answer your question just for fun :) Question: why "average" the observed and predicted? What is the rationale for that and what do you get out of it? Answer: There was no special thought in giving the values 50-50 weight, but the idea behind averaging was to keep the scale largely the same as this 5 level SAHS, which is very widely used and easy to understand. I might as well have just added without dividing by two, but then the scale would have been different. Thanks Tina On 9/9/05, Constantine Daskalakis <C_Daskalakis@mail.jci.tju.edu> wrote: > At 03:45 PM 9/9/2005, Tinna wrote: > >So will I get fried if I do it my proposed way, or will the results > >just be difficult to read for non-statisticians. > >Tina > > > > At 18:31 09/09/2005, Tina wrote: > > > >Dear statalisters, > > > > > > > >I have a dependent variable in 5 levels (Self-Assessed Health Status > > > >from very good to very poor). I am currently assuming a latent > > > >continuous variable, but that is problematic for some of my analysis. > > > >I have some other measures of health in my data and was wondering if > > > >it was appropriate to create a new one that would be continuous. My > > > >suggestion would be: > > > > > > > >1. regress SAHS on other health variables. > > > >2. Predict SAHS (lets call it SAHShat) based on the previous regression. > > > >3. The new measure would be calculated as an average of SAHS and SAHShat > > > > > > > >This looks like a good idea to me, but I wonder why I don't see anyone > > > >else doing this if it is OK. Those of you that fell of your office > > > >chairs in laughter could maybe get back on and explain why not, > > > >because it seems fine idea to me right now. > > > > > > I am not laughing, but I think there are plenty of reasons why this is NOT > done. > > For one, the "predicted" variable (SAHShat) is not an observed outcome but > one that you have "imputed" (via your regression of SAHS on other health > variables). How good the prediction is can be debatable. And what beast > this "predicted" variable really is can be equally debatable. > > Second, it is not clear how you would even regress the ordinal SAHS > variable on other variables. Through some ordinal regression model? Then, > your predicted values would still be discrete. Through some linear > regression model? But if your original SAHS variable were continuous enough > for that, you wouldn't worry in the first place. > > Third, the fact that those values are imputed rather than observed adds > variability that will not be accounted for in your main analyses. Usual > methods take Y to be an actually observed outcome. > > Fourth, why "average" the observed and predicted? What is the rationale for > that and what do you get out of it? > > > In addition to Roger's suggestions, another avenue might be to actually do > a formal latent variable analysis, where you would use SAHS (and possibly > other covariates) as proxies of your unobserved latent outcome. I think > structural equations come into this but it is not my field. > > > Constantine > > > > > > The documents accompanying this transmission may contain confidential > health or business information. This information is intended for the use of > the individual or entity named above. If you have received this information > in error, please notify the sender immediately and arrange for the return > or destruction of these documents. > > ________________________________________________________________ > Constantine Daskalakis, ScD > Assistant Professor, > Thomas Jefferson University, Division of Biostatistics, > 211 S. 9th St., Suite 602, Philadelphia, PA 19107 > Tel: 215-955-5695 > Fax: 215-503-3804 > Email: c_daskalakis@mail.jci.tju.edu > Webpage: http://www.jefferson.edu/clinpharm/bio/ > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Can Y be a predicted variable?***From:*TinnaLaufey Asgeirsdottir <statalist@gmail.com>

**Re: st: Can Y be a predicted variable?***From:*Roger Newson <roger.newson@kcl.ac.uk>

**Re: st: Can Y be a predicted variable?***From:*Tinna <statalist@gmail.com>

**Re: st: Can Y be a predicted variable?***From:*Constantine Daskalakis <C_Daskalakis@mail.jci.tju.edu>

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