On the Use of Factor-Analytic Multinomial Logit Item Response Models to Account for Individual Differences in Response Style

Abstract

Multidimensional item response models are usually implemented to model the relationship between item responses and two or more traits of interest. We show how multidimensional multinomial logit item response models can also be used to account for individual differences in response style. This is done by specifying a factor-analytic model for latent responses at the category level. This permits traits and response style to be separated into separate but possibly correlated factors when properly identified by the factor structure. Special cases of this model can be viewed as generalizations of some unidimensional multinomial logit item response models. In this article, we describe and demonstrate the specification and implementation of these models to account for individual differences in response style that would otherwise compromise the validity of the measurement model.