In this paper, we present a flexible full-information approach to modeling multiple userdefined response styles across multiple constructs of interest. The model is based on a novel parameterization of the multidimensional nominal response model that separates estimation of overall item slopes from the scoring functions (indicating the order of categories) for each item and latent trait. This feature allows for the definition of response styles to vary across items as well as overall item slopes that vary across items for both substantive and response style dimensions. The model is compared with similar approaches using examples from the smoking initiative of NIH's Patient Reported Outcomes Measurement Information System. A small set of simulations show that the estimation approach is able to recover model parameters, factor scores, and reasonable estimates of standard errors. Furthermore, these simulations suggest that failing to include response style factors (when present in the data generating model) has adverse consequences for substantive trait factor score recovery. [This paper was published in "Psychological Methods" v21 n3 p328-347 2016.]