Internet Guide to SPSS
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Multiple Imputation

Multiple Imputation is a procedure to deal with missing data.

Detect patterns of missingness:

MULTIPLE IMPUTATION income educ hhsize status
  /IMPUTE METHOD=NONE
  /MISSINGSUMMARIES OVERALL VARIABLES (MAXVARS=25 MINPCTMISSING=.2) PATTERNS.

In this procedure, no imputations are performed due to subcommand IMPUTE METHOD=NONE. Subommand MISSINGSUMMARIES requests some tables and graphs that indicate the amount, the location and the patterns of missing data. Particularly, MINPCTMISSING=.2 indicates that only variables with more than .2 per cent of missing values are to be included. The default is 10 per cent, which I deem generally too high. On the other hand, .2 per cent may be too low; it all depends on your data and the purposes of your analyses.


Performing the imputations:

DATASET DECLARE ineq_mi.
DATASET DECLARE ineq_mit.
MULTIPLE IMPUTATION income educ hhsize status
  /IMPUTE METHOD=FCS NIMPUTATIONS=5 MAXPCTMISSING=NONE MAXMODELPARAM=1000
  MAXCASEDRAWS=50 MAXPARAMDRAWS=2 MAXITER=100
  /IMPUTATIONSUMMARIES MODELS descriptives
  /CONSTRAINTS income min = 0)
  /OUTFILE IMPUTATIONS=ineq_mi FCSITERATIONS=ineq_mit.

Note that DATASET DECLARE is not part of the MULTIPLE IMPUTATION command. However, it is a prerequisite for having the data sets (on which more below) available after execution of the imputations.

Now some comments on the most important keywords:


© W. Ludwig-Mayerhofer, IGSW | Last update: 29 Jun 2009