Chaid Analysis

Chaid Analysis

CHAID, (Chi Square Automatic Interaction Detection) is a technique whose original intent was to detect interaction between variables (i.e., find "combination" variables), recursively partitions a population into separate and distinct groups, which are defined by a set of independent (predictor) variables, such that the CHAID Objective is met: the variance of the dependent (target) variable is minimized within the groups, and maximized across the groups.

The model can be employed for forecasting and understanding responses, in cases of market penetration, or a multitude of other research queries. This analysis is particularly beneficial for data stating classified values instead of continuous values, because in that case - statistical tools such as regression are not relevant and CHAID analysis is an appropriate tool to determine the correlation among variables. Its advantages are that its output is highly visual, and contains no equations. It commonly takes the form of an organization chart, more commonly referred to as a tree display.

CHAID creates all possible cross tabulations for each categorical predictor until the best outcome is achieved and no further splitting can be performed. The development of the decision, or classification tree, starts with identifying the target variable or dependent variable; which would be considered the root. CHAID analysis splits the target into two or more categories that are called the initial, or parent nodes, and then the nodes are split using statistical algorithms into child nodes. Unlike in regression analysis, the CHAID technique does not require the data to be normally distributed.