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st: RE: Re: errors in outcome variables regression


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?



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