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Re: st: Missingness
From
Austin Nichols <[email protected]>
To
[email protected]
Subject
Re: st: Missingness
Date
Tue, 28 Aug 2012 09:51:02 -0400
Brendan Churchill et al.:
The original post is far from clear: is the missingness in LHS or RHS
vars, or both? Was a dummy for missingness in a RHS variable X
created and missing X recoded to zero so the dummy captures the
difference in mean outcomes conditional on other variables between
missing X and nonmissing X cases, which is known to be a problematic
approach, or was missing X never recoded so the dummy is always zero
in sample (and therefore collinear with the constant)? In any case,
the manual entry on -mi- is a good place to start, though it does not
mention an entirely different (no imputation) approach: treat missing
data as survey nonresponse and use a propensity score reweighting
method. See e.g.
http://www.stata-journal.com/sjpdf.html?articlenum=st0136_1 and
references therein.
On Tue, Aug 28, 2012 at 10:20 AM, Richard Williams
<[email protected]> wrote:
> At 02:42 AM 8/28/2012, Brendan Churchill wrote:
>>
>> Dear Statalist Users
>>
>> I am using some ordinal variables, which have some numeric missing values,
>> in a multilevel model. In some previous research, I have seen researchers
>> include a 'Missing' independent variable in their model to account for some
>> of the 'missingness' - or rather to control for the missing values, but I
>> don't quite understand how to do it in Stata or even if that's a good way to
>> do it. I've tried to make a binary variable in which the missing values are
>> coded 1 and the rest of the values are coded 0 but the model rejects this
>> because it's collinear.
>>
>> Is this how you do it? Or is there a variable for the entire data set that
>> is created to account for all missing variables?
>
>
> In his green Sage book, "Missing Data", Paul Allison explains why this is
> usually (albeit not always) a bad idea. I briefly summarize the argument on
> pp. 4-5 of
>
> http://www.nd.edu/~rwilliam/xsoc63993/l12.pdf
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