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From |
"Mike Hollis" <mehla@earthlink.net> |

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

Subject |
st: RE: Re: errors in outcome variables regression |

Date |
Sat, 5 Jul 2003 09:07:04 -0700 |

Measurement error in the endogeneous variable will, however, cause the residual variance for the equation to be overstated, meaning, in general, that the standard errors for the regression coefficients will be too large and the estimated t- and F-statistics will be too small. If you have a estimate of the reliability of the outcome variable, you could conceivable use this to adjust the standard errors and associated statistics, although the quality of this adjustment obviously depends on the quality of your reliability estimate. (Note, however, that the intra-class correlation coefficient is a measure of non-independence. Correcting for measurement error in your case requires something like Chronbach's alpha or, if you're lucky enough to have them, multiple indicators for the outcome variable. See Ken Bollen's _Structural Equations with Latent Variables_ for a discussion of different strategies.) If the regression coefficients in your current model are statitically significant (i.e., you're not in a situation where you're trying to correct for measurement error to reduce standard errors in an attempt to cause statistically non-significant to become significant), you might simply note the fact that you suspect your outcome variable is affected by measurement error and that this will cause the significance level of the regression coefficients in your model to be underestimated. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Scott Merryman Sent: Friday, July 04, 2003 5:58 AM To: statalist@hsphsun2.harvard.edu Subject: st: Re: errors in outcome variables regression ----- Original Message ----- From: "Margaret May" <M.T.May@bristol.ac.uk> To: <statalist@hsphsun2.harvard.edu> Sent: Friday, July 04, 2003 5:32 AM Subject: st: errors in outcome variables regression > I have been looking at the command eivreg (errors in variables regression) > which corrects the effect estimate when independent variables are measured > with error. The problem I have is looking at differences in a continuous > outcome between exposure groups where the outcome variable is measured with > error. I can estimate the reliability of the outcome measure as I have data > from a validity study so can estimate the intra-class correlation > coefficient. Is there a method for correcting for measurement error in > outcome variables? > > Margaret May > A question concerning errors in the dependent variable came up on March 6th by Charlie Trevor with replies by myself and Mark Schaffer on March 6th and 7th. My reply was: Is this necessary?

**Follow-Ups**:**Re: st: RE: Re: errors in outcome variables regression***From:*Mark Schaffer <M.E.Schaffer@hw.ac.uk>

**References**:**st: Re: errors in outcome variables regression***From:*"Scott Merryman" <smerryman@kc.rr.com>

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