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Re: st: Missing Values in Multiple Imputation Data Sets
From
Richard Williams <[email protected]>
To
[email protected], statalist <[email protected]>
Subject
Re: st: Missing Values in Multiple Imputation Data Sets
Date
Tue, 05 Feb 2013 10:09:06 -0500
One easy thing to check: Stata has both hard and
soft missings. Soft missings (coded .) can be
imputed, hard missings (coded .a through .z)
cannot. So check to see if you are using hard
missings, and if so determine whether it is
reasonable to recode them as soft missings. You
may not want to recode if the hard missings
reflect things like Not Applicable responses.
Beyond that the messages are pretty clear. You
can increase the matsize, make sure you aren't
treating a continuous variable as categorical, etc.
Especially if you have only been using Stata for
3 weeks, I would recommend starting simple and
building up. This looks like a pretty complicated
model. Start with fewer variables and build up.
It may be easier to identify which variables are causing you grief.
At 06:55 AM 2/5/2013, Patti Fritz wrote:
Hello All,
I am a novice to Stata, having just begun using
it 3 weeks ago. Therefore, my questions are
likely very naive. I am trying to conduct
multiple imputation of a data set that primarily
contains positively skewed/frequently
over-dispersed ordinal predictor variables
(i.e., history of various forms of
family-of-origin aggression assessed on 3- or
4-point scales from not at all to a lot); my
other predictors consist of demographic
variables (e.g., age, gender, income, number of
marriages, marital status). My outcome variables
are dichotomous (partner violence) and the
sample size is N = 3,635. There is substantial
missing data on some of the items (e.g., 21%),
and complete data across all variables is only
found among 556 cases). When I attempt to
conduct mi impute chained commands (I've tried
poisson, and nbreg), there are still some
missing values in some of the imputed data sets.
Specifically, every variable still has some
missing data across the 20 imputed data sets. Is there!
a way to alleviate this problem?
I can get the mi impute mvn command to work, but
after reading some of the literature, I'm not
sure this is the best approach. In addition, I
haven't been able to get the mi chained (ologit)
command to work because I get the following error:
matsize too small
   You have attempted to create a matrix
with too many rows or columns or attempted to fit a
   model with too many variables. You need
to increase matsize; it is currently 800. Use set
   matsize; see help matsize.
   If you are using factor variables and
included an interaction that has lots of missing
   cells, either increase matsize or set
emptycells drop to reduce the required matrix size;
   see help set emptycells.
   If you are using factor variables, you
might have accidentally treated a continuous variable
   as a categorical, resulting in lots of
categories. Use the c. operator on such variables.
error occurred during imputation of g2sabuse_s05 ipvabuse_s05 s2gabuse
_g04 ipvabuse_g04
f2sslap_s94 m2sslap_s94 f2gslap_s94 m2gslap_s94
s2gslap_s94 g2sslap_s94 p2pslap_s94 f2sslap_s05
f2sabuse_s05 m2sslap_s05 m2sabuse_s05
f2gslap_g04 f2gabuse_g04 cp260hec cb103redr gb103redr on m
= 1
r(908);
I only have Stata/IC 12.1 on my laptop, but I'm
going to see if the computer labs at work have
the SE version. If yes, I'll see if I can get
the ologit command to work. Regardless, I'm
interested in finding out why missing values are
found in the imputed data sets.
Thanks in advance for your time and assistance. They are greatly appreciated!
Cheers,
Patti
Patti A. Timmons Fritz, Ph.D., C. Psych.
Associate Professor
Department of Psychology
University of Windsor
Windsor, Ontario N9B 3P4
Canada
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-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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