Chi-square automatic interaction detection (CHAID)
is a
decision tree
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...
technique based on adjusted significance testing (
Bonferroni correction,
Holm-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 is based on a formal extension of AID (Automatic Interaction Detection) and THAID (THeta Automatic Interaction Detection) procedures of the 1960s and 1970s, which in turn were extensions of earlier research, including that performed by Belson in the UK in the 1950s.
A history of earlier supervised tree methods together with a detailed description of the original CHAID algorithm and the exhaustive CHAID extension by Biggs, De Ville, and Suen,
can be found in Ritschard.
In practice, CHAID is often used in the context of
direct marketing to select groups of consumers to predict how their responses to some variables affect other variables, although other early applications were in the fields of medical and psychiatric 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, since with small sample sizes the respondent groups can quickly become too small for reliable analysis.
One important advantage of CHAID over alternatives such as multiple regression is that it is non-parametric.
See also
*
Chi-squared distribution
*
Bonferroni correction
*
Latent class model
*
Structural equation modeling
Structural equation modeling (SEM) is a label for a diverse set of methods used by scientists in both experimental and observational research across the sciences, business, and other fields. It is used most in the social and behavioral scienc ...
*
Market segment
*
Decision tree learning
*
Multiple comparisons
References
{{reflist
Further reading
* Press, Laurence I.; Rogers, Miles S.; & Shure, Gerald H.; ''An interactive technique for the analysis of multivariate data'', Behavioral Science, Vol. 14 (1969), pp. 364–370
* Hawkins, Douglas M. ; and Kass, Gordon V.; ''Automatic Interaction Detection'', in Hawkins, Douglas M. (ed), ''Topics in Applied Multivariate Analysis'', Cambridge University Press, Cambridge, 1982, pp. 269–302
* Hooton, Thomas M.; Haley, Robert W.; Culver, David H.; White, John W.; Morgan, W. Meade; & Carroll, Raymond J.; ''The Joint Associations of Multiple Risk Factors with the Occurrence of Nosocomial Infections'', American Journal of Medicine, Vol. 70, (1981), pp. 960–970
* Brink, Susanne; & Van Schalkwyk, Dirk J.; ''Serum ferritin and mean corpuscular volume as predictors of bone marrow iron stores'', South African Medical Journal, Vol. 61, (1982), pp. 432–434
* McKenzie, Dean P.; McGorry, Patrick D.; Wallace, Chris S.; Low, Lee H.; Copolov, David L.; & Singh, Bruce S.; ''Constructing a Minimal Diagnostic Decision Tree'', Methods of Information in Medicine, Vol. 32 (1993), pp. 161–166
* Magidson, Jay; ''The CHAID approach to segmentation modeling: chi-squared automatic interaction detection'', in Bagozzi, Richard P. (ed); ''Advanced Methods of Marketing Research'', Blackwell, Oxford, GB, 1994, pp. 118–159
* Hawkins, Douglas M.; Young, S. S.; & Rosinko, A.; ''Analysis of a large structure-activity dataset using recursive partitioning'', Quantitative Structure-Activity Relationships, Vol. 16, (1997), pp. 296–302
Software
* Luchman, J.N.; ''CHAID: Stata module to conduct chi-square automated interaction detection'', Available for fre
download or type within Stata: ssc install chaid.
* Luchman, J.N.; ''CHAIDFOREST: Stata module to conduct random forest ensemble classification based on chi-square automated interaction detection (CHAID) as base learner'', Available for fre
or type within Stata: ssc install chaidforest.
IBM SPSS Decision Treesgrows exhaustive CHAID trees as well as a few other types of trees such as CART.
* An R package
CHAID' is available on R-Forge.
Market research
Market segmentation
Statistical algorithms
Statistical classification
Decision trees
Classification algorithms