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Re: st: RE: Re: Missing values test
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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