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Re: st: Trying to do some multiple imputation

From   "Mosi A. Ifatunji" <>
To   "Statalist@HARVARD.EDU" <>
Subject   Re: st: Trying to do some multiple imputation
Date   Tue, 24 Jan 2006 09:03:45 -0600

Thanks Rose,

The thing is that I've done some reading on single and multiple imputation
and the literature seems to suggest that doing multiple imputation is much
better because your imputed values are informed by within and between
dataset variance. Therefore, although I am only trying to impute one value
(income), the -uvis- command does not seem appropriate, given that it
generates the new values without the benefit of between dataset variance,
because it does not generate new datasets. (Question: What is the real
difference between -impute- and -uvis-?).

So, I think I am trying to impute missing values for one variable, but I
would like to generate the multiple datasets from which to generate the new
values. If my variables were y x1 x2 x3 (with y being the variable with
missing values that I am trying to generate new values for) could you send
me and example of how I might do such a thing, from generating the multiple
datasets to getting the missing values imputed in the original dataset?

Any help would be wonderful,


On 1/24/06 8:33 AM, "Rose Medeiros" <> wrote:

> Mosi,
> If your goal is just to impute values of your income variable, you might
> use -uvis- which will impute values of the yvar and leave them in your
> initial dataset. If this is problematic because of a large number of
> missing values in the variables you are attempting to impute income
> from, you could use -mvis- and generate only one imputation by
> specifying m(1) and run your analyses on this dataset (which would also
> have imputed values for the other variables). Note that both of these
> procedures are single imputation, rather than multiple imputation. If
> you actually want to do multiple imputation, you would want to use
> -micombine- to specify the actual models you want to test, not the
> variables you are trying to impute.
> Best,
> Rose
> Mosi A. Ifatunji wrote:
>> Good people,
>> Here is my quandary. I am having a heck of a time trying to complete
>> procedures for multiple imputation using Stata 8.2.
>> My goal is to impute missing values for my income variable (v1019). I would
>> like to generate 5 new and complete datasets from which to derive my new
>> values (to be placed back into the old dataset). Here is the syntax I have
>> been using to no avail:
>> First, I use the MVIS command to generate five new datasets with values for
>> any missing values in the key variables:
>> mvis v1019 black male age2 educate using imp, m(5) genmiss(m_) cmd(regress)
>> cy(20) se(101) replace
>> And I get...
>> imputing 1..2..3..4..5..file imp.dta saved
>> Then, I open the new dataset (with all missing values imputed):
>> use imp, clear
>> Then I generate a model (from the 5 new datasets in imp.dta) that predicts
>> my income variable (v1019):
>> micombine regress v1019 black male age2 educate
>> Multiple imputation parameter estimates (5 imputations)
>> ----------------------------------------------------------------------------
>>   v1019 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
>> -------------+-------------------------------------------------------------
>>   black |  -16060.96   2412.319    -6.66   0.000    -20794.22    -11327.7
>>    male |   10331.52   2487.338     4.15   0.000     5451.057    15211.97
>>    age2 |  -114.4879   90.15501    -1.27   0.204    -291.3829    62.40716
>> educate |   4532.232    722.751     6.27   0.000     3114.107    5950.357
>>   _cons |  -8530.671    12416.8    -0.69   0.492    -32893.94     15832.6
>> ----------------------------------------------------------------------------
>> 1106 observations.
>> Now what do I do? I have been roaming through manuals and copies of the
>> Stata Journal (4-3 and 5-4) but every time to get near, the author(s) leave
>> out something important, like how exactly do I use MISET, MI SPLIT AND
>> MIJOIN to get my imputed values back to my original dataset....
>> Any help would be greatly appreciated...
>> M.
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