Chi-square automatic interaction detection: Difference between revisions

Revision as of 20:56, 12 March 2010

Revision as of 20:59, 12 March 2010

Line 1:

'''CHAID''' is a type of [[Decision_tree_learning|decision tree]] technique, based upon adjusted significance testing ([[Bonferroni testing]]). The technique was developed in [[South Africa]] and was published in 1980 by Gordon V. Kass, who had completed a PhD thesis on this topic. CHAID can be used for prediction (in a similar fashion to [[regression analysis]], this version of CHAID being originally known as XAID) as well as classification, and for detection of interaction between variables. CHAID stands for '''CH'''i-squared '''A'''utomatic '''I'''nteraction '''D'''etector, based upon a formal extension of the US AID (Automatic Interaction Detector) and THAID (THeta Automatic Interaction Detector) procedures of the 1960's and 70's, which in turn were extended versions of an algorithm developed in the UK in the 1950's.

In practice, CHAID is often used in the context of [[direct marketing]] to select groups of consumers and predict how their responses to some variables affect other variables, although other early applications were in the field of medical and psychiatric research. The procedure is also being applied in the area of drug and genetic research.

Like other decision trees, CHAID's advantages are that its output is highly visual and easy to interpret. Because it uses multiway splits by default, it needs rather large sample sizes to work effectively as with small sample sizes the respondent groups can quickly become too small for reliable analysis.