Two-Group Classification in Latent Trait Theory: Scores with Monotone Likelihood Ratio

Abstract

This paper deals with two-group classification when a unidimensional latent trait, θ , is appropriate for explaining the data, X . It is shown that if X has monotone likelihood ratio then optimal allocation rules can be based on its magnitude when allocation must be made to one of two groups related to θ . These groups may relate to θ probabilistically via a non-decreasing function p ( θ ), or may be defined by all subjects above or below a selected value on θ . In the case where the data arise from dichotomous items, then only the assumption that the items have nondecreasing item characteristic functions is enough to ensure that the unweighted sum of responses (the number-right score or raw score) possesses this fundamental monotone likelihood ratio property.