Marginal Maximum Likelihood Analyses of Individual Differences in Additivity and Judgmental Criteria for Categorical Rating Data and Decision Making Data

A marginal maximum likelihood (MML) estimation method is developed for the analysis of both categorical rating data and choice data for decision making in the context of uncertain outcomes. The proposed method fits the weighted additive models with interaction terms to the data, allowing for individual differences in weights, category boundaries, and thresholds. The present study demonstrates that the MML approach will be useful for dealing with individual differences in those variables as well as the subject points previously dealt with within the framework of multidimensional scaling. Bock and Aitkin’s EM algorithm is used for the MML estimation of the proposed models.