CHAID trees utilize the chi squared significance test as a means of partitioning data. Independent variables are tested by looping through the values and merging categories that have the least significant difference from one another and also are still below the merging significance level parameter (default .05). Once all independent variables have been optimally merged the one with the highest significance is chosen for the split, the data is subdivided, and the process is repeated on the subsets of the data. The splitting stops when the significance goes above the splitting significance level (default .05).
For a detailed description of this type of tree, see [Kass].