Confirmatory Multidimensional IRT Unfolding Models for Graded-Response Items.

Most unfolding item response models for graded-response items are unidimensional. When there are multiple tests of graded-response items, unidimensional unfolding models become inefficient. To resolve this problem, the authors developed the confirmatory multidimensional generalized graded unfolding model, which is a multidimensional extension of the generalized graded unfolding model, and conducted a series of simulations to evaluate its parameter recovery. The simulation study on between-item multidimensionality demonstrated that the parameters of the new model can be recovered fairly well with the WinBUGS program. The Tattoo Attitude Questionnaire, with three subscales, was analyzed to demonstrate the advantages of the new model over the unidimensional model in obtaining a better model-data fit, a higher test reliability, and a stronger correlation between latent traits. Discussion on potential applications and suggestion for future studies are provided.