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

 From Svend Juul To statalist@hsphsun2.harvard.edu Subject Re: st: Treatment for Missing Values - What Options ? Date Tue, 14 Jul 2009 13:13:04 +0200

```Cy wrote:

In a previous post, I indicated there was a drastic reduction in my
sub-population size. I traced the problem to a variable with a lot of
missing cases.

As you can see from the table below, this variable elicits whether the
respondent engaged in unprotected sexual intercourse. About a third of
the cases (33.78%) are missing.

V761 -- Last intercourse used condom
-----------------------------------------------------------
|      Freq.    Percent      Valid       Cum.
---------------+--------------------------------------------
Valid   0 No   |       6012      56.16      84.81      84.81
1 Yes  |       1075      10.04      15.16      99.97
9      |          2       0.02       0.03     100.00
Total  |       7089      66.22     100.00
Missing .      |       3617      33.78
Total          |      10706     100.00
-----------------------------------------------------------

Since the dependent variable in my deals with HIV risk, I need to
include sexual risk variables such as the V761 in the model.  How do I
deal with this missing data problem, so that it does not affect my
sample size. Would an imputation work?

==========================================================

In this case, I would avoid imputation and instead generate two dummy
variables:
V761_0 = 1 if no condom use, otherwise 0
V761_miss = 1 if missing or 9, otherwise 0

. generate V761_0 = V761==0
. generate V761_miss = V761>1
. groups V761* , missing
+--------------------------------------------+
| V761   V761_0   V761_m~s   Freq.   Percent |
|--------------------------------------------|
|    0        1          0    6012     56.16 |
|    1        0          0    1075     10.04 |
|    9        0          1       2      0.02 |
|    .        0          1    3617     33.78 |
+--------------------------------------------+

-groups- is an unofficial command (ssc install groups).

Both variables should be included in your regression. You will still
have a problem interpreting what missing means, but that problem
can not be solved by imputation.

Hope this helps
Svend
________________________________________________________

Svend Juul
Institut for Folkesundhed, Afdeling for Epidemiologi
(School of Public Health, Department of Epidemiology)
Bartholins Allé 2
DK-8000 Aarhus C,  Denmark
Phone:   +45 8693 7796
Mobile:  +45 2634 7796
E-mail:  sj@soci.au.dk
_________________________________________________________

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```