An Extension of the CHAID Tree-based Segmentation Algorithm to Multiple Dependent Variables
The CHAID algorithm has proven to be an effective approach for obtaining a quick but meaningful segmentation where segments are defined in terms of demographic or other variables that are predictive of a single categorical criterion (dependent) variable. However, response data may contain ratings or purchase history on several products, or, in discrete choice experiments, preferences among alternatives in each of several choice sets. We propose an efficient hybrid methodology combining features of CHAID and latent class modeling (LCM) to build a classification tree that is predictive of multiple criteria. The resulting method provides an alternative to the standard method of profiling latent classes in LCM through the inclusion of (active) covariates.
KeywordsLatent Class Latent Class Analysis Discrete Choice Experiment Latent Class Modeling National Election Study
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