Standard practice in the application of disaggregate choice models is to assume all members of a (sample) population use the same basic decision process, and thus to employ one disaggregate choice model to analyze an entire population. The author suggests that not all decision makers use the same basic decision process on a given choice and that there are variables by which a sample can be partitioned a priori into segments assumed to be using different choice processes. The use of multiple disaggregate choice models, each model fitting the choice process assumptions of a segment, should improve both the predictive accuracy and the quality of the diagnostic information provided. Empirical information from two separate data sets tends to support strongly the approach of using multiple disaggregate choice models on a given (sample) population.
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