# CHAID Criteria

For the CHAID and Exhaustive CHAID methods, you can control:

Significance Level. You can control the significance value for splitting nodes and merging categories. For both criteria, the default significance level is 0.05.

• For splitting nodes, the value must be greater than 0 and less than 1. Lower values tend to produce trees with fewer nodes.
• For merging categories, the value must be greater than 0 and less than or equal to 1. To prevent merging of categories, specify a value of 1. For a scale independent variable, this means that the number of categories for the variable in the final tree is the specified number of intervals (the default is 10). See the topic Scale Intervals for CHAID Analysis for more information.

Chi-Square Statistic. For ordinal dependent variables, chi-square for determining node splitting and category merging is calculated using the likelihood-ratio method. For nominal dependent variables, you can select the method:

• Pearson. This method provides faster calculations but should be used with caution on small samples. This is the default method.
• Likelihood ratio. This method is more robust that Pearson but takes longer to calculate. For small samples, this is the preferred method.

Model Estimation. For nominal and ordinal dependent variables, you can specify:

• Maximum number of iterations. The default is 100. If the tree stops growing because the maximum number of iterations has been reached, you may want to increase the maximum or change one or more of the other criteria that control tree growth.
• Minimum change in expected cell frequencies. The value must be greater than 0 and less than 1. The default is 0.05. Lower values tend to produce trees with fewer nodes.

Adjust significance values using Bonferroni method. For multiple comparisons, significance values for merging and splitting criteria are adjusted using the Bonferroni method. This is the default.

Allow resplitting of merged categories within a node. Unless you explicitly prevent category merging, the procedure will attempt to merge independent (predictor) variable categories together to produce the simplest tree that describes the model. This option allows the procedure to resplit merged categories if that provides a better solution.

To Specify CHAID Criteria

This feature requires the Decision Trees option.