1980
DOI: 10.2307/2986296
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An Exploratory Technique for Investigating Large Quantities of Categorical Data

Abstract: Summary The technique set out in the paper, chaid, is an offshoot of aid (Automatic Interaction Detection) designed for a categorized dependent variable. Some important modifications which are relevant to standard aid include: built‐in significance testing with the consequence of using the most significant predictor (rather than the most explanatory), multi‐way splits (in contrast to binary) and a new type of predictor which is especially useful in handling missing information.

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Cited by 2,202 publications
(1,341 citation statements)
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“…CHAID method 25 was used to verify which variables better discriminated the groups in relation to activity restriction. The Bonferroni model was used to determine statistical significance level.…”
Section: Discussionmentioning
confidence: 99%
“…CHAID method 25 was used to verify which variables better discriminated the groups in relation to activity restriction. The Bonferroni model was used to determine statistical significance level.…”
Section: Discussionmentioning
confidence: 99%
“…CHAID is a type of decision tree technique. This technique can both be used as prediction (like logistic regression) and for detection of interaction between variables (Kass, 1980). The algorithm recursively splits up the dataset by detecting the best predictor of PTSD among the five sociodemographic characteristics, creating associated subgroups of high and low risk.…”
Section: Discussionmentioning
confidence: 99%
“…The best results were obtained with the 'chi-squared automatic interaction detector' (CHAID) growing method (Kass, 1980). The CHAID algorithm evalu- ates each one of the independent variables to find the categories that best split each node, starting from the root.…”
Section: Classification Treementioning
confidence: 99%