[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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

From   Maarten buis <>
Subject   Re: st: Treatment for Missing Values - What Options ?
Date   Wed, 15 Jul 2009 07:11:22 +0000 (GMT)

--- On Tue, 14/7/09, Chao Yawo wrote:

> Here is what i got when I run the
> suggested code, with a slight
> modification taking the survey design into account:
> the significance mean then that the DV (V781_R) negatively
> predicts the missing value (mis).  What does that mean? ...

It means you are in trouble, or at least that the solution
was not as easy as we thought. Since you have quite a larger
proportion of missing cases and the probability of missingness
is quite strongly related to your dependent variable (an odds
ratio of exp(-.6133271)= .54), just ignoring these missing
values will influence your results.

I would do a mixture of approaches and hope they lead to 
similar conclusions.

1) I would do -ice-

2) You could use your faithfulness variable

3) use -ice- as in 1) but now estimate a model with multiple
risky behavior variables, and combine their effects in a 
sheaf coefficient using -sheafcoef-. This way you could
diminish the influence of the imputing that many values 
values on one of the indicator variables.

You can download -sheafcoef- from SSC by typing in Stata
-ssc install sheafcoef-. There is a more extensive 
discussion of this type of models in the helpfile of 
-propcnsreg-, which can be downloaded by typing 
-ssc install propcnsreg-. In order to use -sheafcoef-
you will need to specify the -storebv- option in -mim-.

However, I would not do the dummy variable approach, for reason
already mentioned by Rich Goldstein and in an earlier post
by me:

Hope this helps,

Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen


*   For searches and help try:

© Copyright 1996–2017 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index