Options for CHAID and Exhaustive CHAID

Element Name Description
Splitting merged categories When a predictor is used in the splitting criterion, the CHAID (or Exhaustive CHAID) 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.
Intervals The Interactive Trees (C&RT, CHAID) module gives you full control over the manner in which the range of values in continuous predictors in CHAID and Exhaustive CHAID analyses is divided into intervals (unlike the General CHAID Models module, which applies automatic algorithms for building trees; see Basic Tree-Building Algorithm: CHAID and Exhaustive CHAID for details; see also the Introductory Overview topic - Differences in Computational Procedures section). Use the options in the Intervals group box to determine how exactly the range of values in each continuous predictor variable is to be divided into intervals.
Automatic continuous predictor intervals in each node Select this check box if you want STATISTICA to recompute an optimal set of (approximately equal-N) intervals for each continuous predictor in the analysis (to evaluate potential splits) at each node. If this check box is not selected, the program will only determine an optimal number of intervals once, when the data is read for the first time; thereafter, these intervals remain unchanged unless manually modified by the user. Note that this (latter) procedure is more efficient since it requires fewer reading passes through all data and, hence, it may result in faster computations (of the final tree). However, there is the possibility that the final (terminal) nodes in the analysis are determined from very few (sparse) intervals for the continuous predictors, leading to less than optimal splits.
Continuous predictor intervals This option is available only if the Automatic continuous predictor intervals in each node check box is not selected. In this case, you can modify manually the intervals for each continuous predictor that are to be used for evaluating potential splits (for the CHAID tree). Select the desired continuous predictor variable from the drop-down list, and then click the Intervals button to display the Specify Boundaries dialog box, where you can modify the default intervals created for the respective (selected) continuous predictor.