Re: st: RE: Re: Missing values test

[Richard Williams <Richard.A.Williams.5@ND.edu>]

At 12:11 PM 12/2/2007, Nick Cox wrote:
> I've not done it myself, and this may well be obvious
> to those who know the literature, but surely more can
> be said.
>
> Missingness can always be represented by a dummy. So
> the structure of missing data can always be explored by
> logit regression with missingness on something as response
> w.r.t. various predictors, which may well include missingness
> on some other things as dummy predictors.
I believe Nick is talking about using the MD dummy as your dependent 
variable.  In addition, there have been proposals about using MD 
dummies as independent vars, which I'll now comment on since i have 
given partially incorrect responses in the past!

Cohen and Cohen proposed several years ago that you plug in the mean 
for missing data and then add a MD dummy variable indicator.  Allison 
discusses this technique in his green Sage book, "Missing Data".

When data exist in reality but their value is unknown (e.g. because 
of nonresponse), Allison calls this technique "remarkably simple and 
intuitively appealing." But unfortunately, "the method generally 
produces biased estimates of the coefficients."  He says that 
listwise deletion is better.

HOWEVER, as Richard Campbell recently pointed out to me, buried in 
the footnotes of Allison's book is the following:

"While the dummy variable adjustment method is clearly unacceptable 
when data are truly missing, it may still be appropriate in cases 
where the unobserved value simply does not exist.  For example, 
married respondents may be asked to rate the quality of their 
marriage, but that question has no meaning for unmarried 
respondents.  Suppose we assume that there is one linear equation for 
married couples and another equation for unmarried couples.  The 
married equation is identical to the unmarried equation except that 
it has (a) a term corresponding to the effect of marital quality on 
the dependent variable and b) a different intercept.  It's easy to 
show that the dummy variable adjustment method produces optimal 
estimates in this situation."



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Richard Williams, Notre Dame Dept of Sociology
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