  Item response theory (IRT) models explain an observed item response as a
function of a respondent's latent trait and the item's property. IRT is one of
the most widely utilized tools for item response analysis; however, local item
and person independence, which is a critical assumption for IRT, is often
violated in real testing situations. In this article, we propose a new type of
analytical approach for item response data that does not require standard local
independence assumptions. By adapting a latent space joint modeling approach,
our proposed model can estimate pairwise distances to represent the item and
person dependence structures, from which item and person clusters in latent
spaces can be identified. We provide an empirical data analysis to illustrate
an application of the proposed method. A simulation study was also provided to
evaluate the performance of the proposed method in comparison to an existing
method.
