Missing Values

The Missing Values tab controls the handling of nominal, user-missing, independent (predictor) variable values.

  • Handling of ordinal and scale user-missing independent variable values varies between growing methods.
  • Handling of nominal dependent variables is specified in the Categories dialog box. See the topic Selecting Categories for more information.
  • For ordinal and scale dependent variables, cases with system-missing or user-missing dependent variable values are always excluded.

Treat as missing values. User-missing values are treated like system-missing values. The handling of system-missing values varies between growing methods.

Treat as valid values. User-missing values of nominal independent variables are treated as ordinary values in tree growing and classification.

Method-Dependent Rules

If some, but not all, independent variable values are system- or user-missing:

  • For CHAID and Exhaustive CHAID, system- and user-missing independent variable values are included in the analysis as a single, combined category. For scale and ordinal independent variables, the algorithms first generate categories using valid values and then decide whether to merge the missing category with its most similar (valid) category or keep it as a separate category.
  • For CRT and QUEST, cases with missing independent variable values are excluded from the tree-growing process but are classified using surrogates if surrogates are included in the method. If nominal user-missing values are treated as missing, they are also handled in this manner. See the topic Surrogates for more information.

To Specify Nominal, Independent User-Missing Treatment

This feature requires the Decision Trees option.

  1. From the menus choose:

    Analyze > Classify > Tree...

  2. In the main Decision Tree dialog box, select at least one nominal independent variable.
  3. Click Options.
  4. Click the Missing Values tab.