Journal of Data Science

Abstract: Random Utility models have been shown useful in scaling choice options, as well as in providing a rich source of information about individual differences and perceived similarity relationships among choice alternatives. Modeling of preference data such as rankings was made easier by representing utilities as latent factors in a structural equation modeling framework. In this paper, we extend such an SEM approach to analyze ranking data and other types of ordinal data simultaneously. This combination of both absolute and relative judgment data can enrich our understanding of individual differences in multiple domains including preference and attitude.