Chi-square automatic interaction detection: Difference between revisions

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{{Short description|Decision tree learning technique}}

'''CHAID''' is a technique that detects interaction between variables. It is used to identify discrete groups of consumer and predict how their responses to some variables affect other variables.

'''Chi-square automatic interaction detection''' ('''CHAID''')<ref name=":1" /> is a [[Decision tree learning|decision tree]] technique based on adjusted significance testing ([[Bonferroni correction]], [[Holm-Bonferroni method|Holm-Bonferroni testing]]).<ref name="kass1980">{{Cite journal |last=Kass |first=G. V. |date=1980 |title=An Exploratory Technique for Investigating Large Quantities of Categorical Data |url=https://www.jstor.org/stable/2986296 |journal=Applied Statistics |volume=29 |issue=2 |pages=119–127 |doi=10.2307/2986296|jstor=2986296 |url-access=subscription }}</ref><ref name=":0">{{Cite journal |last1=Biggs |first1=David |last2=De Ville |first2=Barry |last3=Suen |first3=Ed |date=1991 |title=A method of choosing multiway partitions for classification and decision trees |url=https://www.tandfonline.com/doi/full/10.1080/02664769100000005 |journal=Journal of Applied Statistics |language=en |volume=18 |issue=1 |pages=49–62 |doi=10.1080/02664769100000005 |bibcode=1991JApSt..18...49B |issn=0266-4763|url-access=subscription }}</ref>

==History==

CHAID stands for CHi-squared Automatic Interaction Detector:

CHAID is based on a formal extension of AID (Automatic Interaction Detection)<ref name="morgan1963">{{Cite journal |last1=Morgan |first1=James N. |last2=Sonquist |first2=John A. |date=1963 |title=Problems in the Analysis of Survey Data, and a Proposal |url=http://www.tandfonline.com/doi/abs/10.1080/01621459.1963.10500855 |journal=Journal of the American Statistical Association |language=en |volume=58 |issue=302 |pages=415–434 |doi=10.1080/01621459.1963.10500855 |issn=0162-1459|url-access=subscription }}</ref> and THAID (THeta Automatic Interaction Detection)<ref name="messenger1972">{{Cite journal |last1=Messenger |first1=Robert |last2=Mandell |first2=Lewis |date=1972 |title=A Modal Search Technique for Predictive Nominal Scale Multivariate Analysis |url=http://www.tandfonline.com/doi/abs/10.1080/01621459.1972.10481290 |journal=Journal of the American Statistical Association |language=en |volume=67 |issue=340 |pages=768–772 |doi=10.1080/01621459.1972.10481290 |issn=0162-1459|url-access=subscription }}</ref><ref name="morgan1973">{{Cite book |last=Morgan |first=James N. |title=THAID, a sequential analysis program for the analysis of nominal scale dependent variables |date=1973 |others=Robert C. Messenger |isbn=0-87944-137-2 |___location=Ann Arbor, Mich. |oclc=666930}}</ref> 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.<ref>{{Cite journal |last=Belson |first=William A. |date=1959 |title=Matching and Prediction on the Principle of Biological Classification |url=https://www.jstor.org/stable/2985543 |journal=Applied Statistics |volume=8 |issue=2 |pages=65–75 |doi=10.2307/2985543|jstor=2985543 |url-access=subscription }}</ref>

*'''CH'''i-squared

*'''A'''utomatic

*'''I'''nteraction

*'''D'''etector

In 1975, the CHAID technique itself was developed in South Africa. It was published in 1980 by Gordon V. Kass, who had completed a PhD thesis on the topic.<ref name="kass1980"/>

Its advantages are that its output is highly visual and contains no equations. (It commonly takes the form of an organisation chart.)

A history of earlier supervised tree methods can be found in [[Gilbert Ritschard|Ritschard]], including a detailed description of the original CHAID algorithm and the exhaustive CHAID extension by Biggs, De Ville, and Suen.<ref name=":0" /><ref name=":1">{{Cite journal |last=Ritschard |first=Gilbert |title=CHAID and Earlier Supervised Tree Methods |url=https://www.researchgate.net/publication/315476407 |journal=Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences, McArdle, J.J. And G. Ritschard (Eds) |___location=New York |publisher=Routledge |publication-date=2013 |pages=48–74}}</ref>

But it needs large sample sizes to work effectively. CHAID does not work well with small sample sizes as respondent groups can quickly become too small for reliable analysis.

CHAID was used as the data mining technique. It is a technique based on multiway splitting to create discrete groups and understand their impact on the dependent variable. CHAID was preferred for analysis because of five major criteria:

CHAID detects interaction between variables in the data set. Using this technique we can establish relationships between a ‘dependent variable’ – for example readership of a certain newspaper – and other explanatory variables such as price, size, supplements etc. CHAID does this by identifying discrete groups of respondents and, by taking their responses to explanatory variables, seeks to predict what the impact will be on the dependent variable.

1. A good proportion of input data was categorical;

CHAID is often used as an exploratory technique and is an alternative to multiple regression, especially when the data set is not well-suited to regression analysis.

2. Its efficiency in large datasets;

3. Its highly visual and ease of interpretation;

4. Ease of implementation/integration of business rules generated from CHAID in business; and

5. Input data quality can be handled efficiently<ref>{{Cite web |last=Behera, Desik |first= |date=Nov 2012 |title=Acquiring Insurance Customer: The CHAID Way |url=https://www.researchgate.net/publication/256038754_Acquiring_Insurance_Customer_The_CHAID_Way |access-date=7 Aug 2025 |website=Research Gate}}</ref><ref>{{Cite web |last=Kotane |first=Inta |date=September 2024 |title=APPLICATION OF CHAID DECISION TREES AND NEURAL NETWORKS METHODS IN FORECASTING THE YIELD OF CEREAL INDUSTRY COMPANIES |url=https://www.researchgate.net/publication/383956028_APPLICATION_OF_CHAID_DECISION_TREES_AND_NEURAL_NETWORKS_METHODS_IN_FORECASTING_THE_YIELD_OF_CEREAL_INDUSTRY_COMPANIES |url-status=live |archive-url= |archive-date= |access-date=7 August 2025 |website=Research Gate |doi=10.17770/het2024.28.8264}}</ref>

==Properties==

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.<ref name="morgan1963"/><ref name="messenger1972"/><ref name="morgan1973"/>

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.{{fact|date=December 2024}}

ButLike 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. CHAID, does not work wellsince with small sample sizes asthe respondent groups can quickly become too small for reliable analysis.{{fact|date=December 2024}}

One important advantage of CHAID over alternatives such as multiple regression is that it is non-parametric.{{fact|date=December 2024}}

==See also==

*[[Chi-squareBonferroni distributioncorrection]]

*[[DecisionChi-squared treedistribution]]

*[[Decision tree learning]]

*[[Latent class model]]

*[[Structural equation modeling]]

*[[Market segment]]

*[[Multiple comparisons]]

*[[Structural equation modeling]]

==External linksReferences==

{{reflist|1}}

*[http://www.statsoft.com/textbook/stchaid.html Statsoft - CHAID Analysis]

*[http://www.spss.com/answertree/decisiontrees.htm SPSS - How decision tree results are different in AnswerTree]

*[http://www.smartdrill.com/About/process4.html SmartDrill - Analytic Techniques: CHAID]

==Bibliography==

[[Category:Statistical algorithms]]

* Press, Laurence I.; Rogers, Miles S.; & Shure, Gerald H.; ''An interactive technique for the analysis of multivariate data'', Behavioral Science, Vol. 14 (1969), pp.&nbsp;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.&nbsp;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.&nbsp;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.&nbsp;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.&nbsp;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.&nbsp;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.&nbsp;296–302

==External lkinks==

[[de:CHAID]]

* Luchman, J.N.; ''CHAID: Stata module to conduct chi-square automated interaction detection'', Available for free [https://ideas.repec.org/c/boc/bocode/s457752.html 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 free [https://ideas.repec.org/c/boc/bocode/s457932.html download], or type within Stata: ssc install chaidforest.

* [https://www.ibm.com/downloads/cas/Z6XD69WQ IBM SPSS Decision Trees] grows exhaustive CHAID trees as well as a few other types of trees such as CART.

* An R package ''[https://r-forge.r-project.org/R/?group_id=343 CHAID]'' is available on R-Forge.

[[Category:Market research]]

[[Category:Market segmentation]]

[[Category:Statistical algorithms]]

[[Category:Statistical classification]]

[[Category:Decision trees]]

[[Category:Classification algorithms]]