Chi-square automatic interaction detection (CHAID)
is a
decision tree technique based on adjusted significance testing (
Bonferroni correction
In statistics, the Bonferroni correction is a method to counteract the multiple comparisons problem.
Background
The method is named for its use of the Bonferroni inequalities.
An extension of the method to confidence intervals was proposed by Ol ...
,
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
Direct marketing is a form of communicating an offer, where organizations communicate directly to a pre-selected customer and supply a method for a direct response. Among practitioners, it is also known as ''direct response marketing''. By ...
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
In probability theory and statistics, the chi-squared distribution (also chi-square or \chi^2-distribution) with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. The chi-square ...
*
Bonferroni correction
In statistics, the Bonferroni correction is a method to counteract the multiple comparisons problem.
Background
The method is named for its use of the Bonferroni inequalities.
An extension of the method to confidence intervals was proposed by Ol ...
*
Latent class model In statistics, a latent class model (LCM) relates a set of observed (usually discrete) multivariate variables to a set of latent variables. It is a type of latent variable model. It is called a latent class model because the latent variable is disc ...
*
Structural equation modeling
*
Market segment
*
Decision tree learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of obse ...
*
Multiple comparisons
In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.
The more inferences ...
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