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RE: st: Multiple Imputation / - uvis- help


From   "Kanter, Rebecca" <rkanter@jhsph.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Multiple Imputation / - uvis- help
Date   Sun, 12 Apr 2009 09:46:16 -0400

Thanks for the suggestions all; Friedrich was right, it actually had to do with a the survey's skip pattern around this question (i.e. if they answered about not really having housing sanitation-a prior question, they "skipped" the drainage question). This was not evident from the "codebook" (made by someone other than myself)  however, especially because for the drainage question one of the answer choices is "no." Thus, thankfully I don't have to keep fiddling with uvis / ice.
Best,
Rebecca
___________________________________________
Rebecca M. Kanter
PhD Candidate
Johns Hopkins Bloomberg School of Public Health
Department of International Health
Center for Human Nutrition
________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Friedrich Huebler [fhuebler@gmail.com]
Sent: Sunday, April 12, 2009 2:02 AM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Multiple Imputation / - uvis- help

Rebecca,

Which survey data are you analyzing? Did you read the questionnaire?
The missing values may be determined by the skip pattern so that
"missing" may in fact indicate "no drainage" in most cases.

Friedrich

On Sat, Apr 11, 2009 at 9:14 PM, Kanter, Rebecca <rkanter@jhsph.edu> wrote:
> Hi,
>
> I am using factor analysis to construct a socio-econonomic status (ses) variable for my dataset (and did so), but noticed that many households are missing (MAR) information on "drainage;" thus I wanted to use multiple imputation methods to impute values for drainage (a non-ordinal categorical variable) so that there wouldn't be thousands of households with a missing ses. I wanted to impute the value based on the values each household has for the other variables used in the factor analysis (i constructed all the categorical variables into binary ones) and thought I could do this MI via uvis, below. At first, I thought I kept getting the below error because some households were missing information on drainage and another variable in this list, so I corrected that by removing them from the uvis (via the draintag==., if draintag==1 then they are missing drainage info and info on another variable listed below); but i still get the same error. If anyone can help me figure out uv!
 is!
>  and/or another way to impute the missing drainage values that would be much appreciated. (And just a note, a1-r4 and o1-k4 below are a series of household characteristic binary variables).
>
> . uvis mlogit drainage a1 a2 r1 r2 r3 r4 radio modcon eapp car camioneta vehic tvbw tvcolor refrig gastove othstove washmac boiler comp microwave phone blender vcr ventilador loghexp nfam crowding o1-k4 if draintag==., gen(md2) boot
> [imputing by drawing from conditional distribution with bootstrap]
> [perfect prediction detected: using augmlogit to impute drainage]
> equation 0 not found
> r(111);
>
>
> ___________________________________________
> Rebecca M. Kanter
> PhD Candidate
> Johns Hopkins Bloomberg School of Public Health
> Department of International Health
> Center for Human Nutrition

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