Choice Based Conjoint Analysis: Discrete Choice Models vs. Direct Regression

Conjoint analysis is family of techniques that originated in psychology and later became popular in market research. The main objective of conjoint analysis is to measure an individual’s or a population’s preferences on a class options that can be described by parameters and their levels. We consider preference data obtained in choice based conjoint analysis studies, where one observes test persons’ choices on small subsets of the options. There are many ways to analyze choice based conjoint analysis data. Here we want to compare two approaches, one based on statistical assumptions (discrete choice models) and a direct regression approach. Our comparison on real and synthetic data indicates that the direct regression approach outperforms the discrete choice models.