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Re: st: Missing Values in Multiple Imputation Data Sets

From   Richard Williams <>
To, statalist <>
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

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!



Patti A. Timmons Fritz, Ph.D., C. Psych.
Associate Professor
Department of Psychology
University of Windsor
Windsor, Ontario N9B 3P4

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