CHAID
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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.
CHAID stands for CHi-squared Automatic Interaction Detector:
- CHi-squared
- Automatic
- Interaction
- Detector
Its advantages are that its output is highly visual and contains no equations. (It commonly takes the form of an organisation chart.)
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.
[edit] See also
- Chi-square distribution
- Decision tree
- Latent class model
- Structural equation modeling
- Market segment
