Multidimensional forced-choice (MFC) items have been found to be useful to reduce response biases in personality assessments. However, conventional scoring methods for the MFC items result in ipsative data, hindering the wider applications of the MFC format. In the last decade, a number of item response theory (IRT) models have been developed, majority of which are for MFC items with binary responses. However, MFC items with polytomous responses are more informative and have many applications. This paper develops a polytomous Rasch ipsative model (pRIM) that can deal with ipsative data and yield estimates that measure construct differentiation--a latent trait that describes the degree to which the personality constructs (e.g., interests) distinguish between each other. The pRIM and its simpler form are applied to a career interests assessment containing four-category MFC items and the measures of interests differentiation are used for both intra- and interpersonal comparisons. Simulations are conducted to examine the recovery of the parameters under various conditions. The results show that the parameters of the pRIM can be well recovered, particularly when a complete linking design and a large sample are used. The implications and application of the pRIM in the personality assessment using MFC items are discussed.