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Re: st: imputing continuous values when respondents select categories, e.g., income category


From   Richard Williams <Richard.A.Williams.5@ND.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>, "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: imputing continuous values when respondents select categories, e.g., income category
Date   Sat, 25 Apr 2009 00:28:47 -0500

At 10:23 PM 4/24/2009, Alan Acock wrote:
Richard Williams asked if I want to impute missing values or to plug in values within each interval, as opposed to assigning everybody the midpoint of the interval they select. The latter is what I want to do and it appears that the intreg command with the ystar(a,b) option in the post estimation commands is exactly what I should use. This treats income as the dependent variable, but once we estimate the value we can use that as an independent variable in other models. At least this is my understanding.

Actually, I am not sure if that is the optimal strategy or not. At a minimum, it seems there should be some sort of penalty for using estimated income rather than real income. You'll also have multicollinearity problems if all the vars used to compute estimated income are also in your other models.

Maarten Buis did touch on these issues at the summer 2008 NASUG (but I don't remember what he concluded!). See the first paper listed at

http://www.stata.com/meeting/snasug08/abstracts.html

Also, Powers & Xie discuss this sort of thing in section 6.2 of their book ("Statistical Methods for Categorical Data Analysis"). They propose a "normal score transformation" which in turn comes from Clogg & Shihadeh 1994 ("Statistical Models for Ordinal Variables"). There is no discussion of how much better it actually works though.


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Richard Williams, Notre Dame Dept of Sociology
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