Predicted Mean Imputation

This example uses the data set MI_TRIAL.MDD (located in the SAMPLES subdirectory).

1. Open the datasheet MI_TRIAL.MDD, select Analyze > Single Imputation, and the Predicted Mean option to display the Specify Predicted Mean window.
2. Drag the variables to be imputed, the chosen Covariates, and the Grouping Variable between the Variable(s), the Variable(s) to Impute, and the Covariate(s) listboxes, and Grouping Variable datafield.
3. For this example we have chosen the Variables to be imputed as MeasA_1 and MeasA_2, the variable MeasA_0 as the Covariate.

NOTE: You cannot drag variables that do not contain missing values into the “Variable(s) to Impute” listbox.

4. When the required variables have been selected, press the OK button to display the Specify Predicted Mean window shown on the right.
5. After pressing the OK button, new datasheet window is displayed where the imputed values are displayed as green text, and an Imputation Report (shown in part below) can be selected from the View menu.

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_ Rx to the regression model, where Rx is the response indicator vector for the incomplete covariate x .
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.
5. If there are no groups in the variable chosen as a grouping variable, you will be prompted to group the variable.

NOTE: There are no missing values in the variable chosen as a grouping variable for this example, but if there were, the following window would be displayed.

Then:

6. If the Use the hot-deck option is chosen, you must select a variable in the dropdown listbox that will be used to impute the missing values in the grouping variable. The dropdown list will contain 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 of the used cases.
7. If the Exclude option is chosen, all of those cases that are missing in the grouping variable are excluded, and no missing values will be imputed in these cases.

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