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I m p u t a t i o n
NOTE: There are no missing values in the variable chosen as the Covariate in this example,
but if there were, the following window would be displayed:
Then:
1.
If the Use hot-deck imputation option is chosen, you must select a variable in the
dropdown listbox that will be used to impute the missing values in the Covariate. The
dropdown list contains a list of all of the variables in the data set, in the same order as
they appear in the datasheet. If more than one matching respondent is found, a value is
randomly selected from within the imputation class. If no matching respondent is
found, the respondent is selected at random from all the used cases.
2.
If the Include a missingness indicator is chosen for a covariate x , then the
independent variable x is changed into Rx * x and the intercept is adjusted by adding
the independent variable 1 − R x to the regression model, where R x is the response
indicator vector for the incomplete covariate x . See Appendix C – Multiple
Imputation – Predictive Model Based Method.
3.
If the Exclude option is chosen, all of those cases that are missing in the Covariate are
excluded, and no missing values will be imputed for these cases.
NOTE: Unless another Covariate is chosen, the Covariate with missing values discussed above
will be used in all subsequent steps of the imputation.
And:
4.
If a nominal variable(s) is chosen as the Covariate(s) you will be prompted to create
design variables and these will be used in the regression analysis.
Imputation User Manual 21