Chi-square automatic interaction detection: Difference between revisions

Revision as of 16:00, 23 August 2006

Revision as of 16:28, 23 August 2006

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Its advantages are that isits output is highly visual output,and withcontains no equations. (It is a highly visual means of data presentation that commonly takes the form of an organisation chart and does not entail any formulae or equations.)

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 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.

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.

==See also==