# Re: st: Treatment for Missing Values - What Options ?

 From Chao Yawo To statalist@hsphsun2.harvard.edu Subject Re: st: Treatment for Missing Values - What Options ? Date Tue, 14 Jul 2009 17:09:41 -0400

```Here is what i got when I run the suggested code, with a slight

------------
. logit mis V781_R [pweight=weight], cluster(psu), if
SexActiveHIVNegMale==1

(sum of wgt is   3.2763e+03)
Iteration 0:   log pseudolikelihood = -1188.0902
Iteration 1:   log pseudolikelihood = -1183.4275
Iteration 2:   log pseudolikelihood =  -1183.289
Iteration 3:   log pseudolikelihood = -1183.2888

Logistic regression                               Number of obs   =       3130
Wald chi2(1)    =       5.37
Prob > chi2     =     0.0205
Log pseudolikelihood = -1183.2888                 Pseudo R2       =     0.0040

(Std. Err. adjusted for 357 clusters in psu)
------------------------------------------------------------------------------
|               Robust
mis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
V781_R |  -.6133271   .2646202    -2.32   0.020    -1.131973   -.0946809
_cons |  -1.879409   .1766616   -10.64   0.000     -2.22566   -1.533159

------------

the significance mean then that the DV (V781_R) negatively predicts
the missing value (mis).  What does that mean? ...

thanks - cy

On Tue, Jul 14, 2009 at 4:42 PM, Maarten buis<maartenbuis@yahoo.co.uk> wrote:
>
> --- On Tue, 14/7/09, Chao Yawo wrote:
>> So the missing values result from interview errors, and the
>> errors are not related to my DV.  In fact, the DV had only
>> 161 missing variables.
>
> This is something you can check (assume that rep78 is your
> unsafe sex variable and mpg is your dependent variable ):
>
> *------ begin example ------
> sysuse auto, clear
> gen mis = missing(rep78)
> logit mis mpg
> *------- end example -------
>
>>  If I ignore the errors on that single IV then it implies I
>> will have to accept the lower N (sample size) my analysis,
>> and explain that in my write-up (that changes in sample
>> size for the regression result from missing values on some
>> of the covariates??
>
> This is very common, look up some leading empirical
> publications in your discipline to see what the most common
> formulation in your discipline is. You can do more: you have
> reason why your missing data is not related to your dependent
> variable, and you checked that, and the Allison reference I
> gave in my previous post explains why your results are not
> biased by these missing values.
>
> -- Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
>
>
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```