General CHAID Models Quick Specs - Advanced Tab

Select the Advanced tab of the General CHAID Models Quick specs dialog box to access the options described here.

Element Name Description
Sigma-restricted parameterization for categorical predictors (coded ANOVA-like design) This check box is available only if you selected CHAID with coded designs in the Startup Panel. In that case, you can specify ANOVA/ANCOVA-like designs in the same manner as discussed in detail in the context of the ANOVA and General Linear Models modules. This option determines the coding that is used for the categorical predictor effects and their interactions. When this check box is selected, the sigma-restricted parameterization is used. If this check box is not selected, an over-parameterized design matrix is constructed. Refer also to Sigma Restricted and Overparameterized Model in General Linear Models for additional details concerning the different ways in which the categorical effects can be coded into vectors of a design matrix.
Splitting merged categories When a predictor is used in the splitting criterion, the GCHAID analysis merges the current categories into as small a number of categories as possible, in order to find a parsimonious split rule for the tree. If this check box is selected, the merged categories are split to optimally select the categories. See also Basic Tree-Building Algorithm: CHAID and Exhaustive CHAID for details.
Bonferroni adjustment As described in Basic Tree-Building Algorithm: CHAID and Exhaustive CHAID, at the point of selecting the best predictor for a split, the program finds the predictor with the smallest p-value (greatest statistical significance) for the set of categories for the respective predictor. This p-value can be computed after applying the Bonferroni adjustment.