[size=16.6043px]A DOUBLY LATENT SPACE JOINT MODEL FOR LOCAL ITEM AND PERSON
[size=13.2835px]DEPENDENCE IN THE ANALYSIS OF ITEM RESPONSE DATA
[size=13.2835px]abstarct:

[size=13.2835px]Item response theory (IRT) is one of the most widely utilized tools for item response analysis; however,
[size=13.2835px]local item and person independence, which is a critical assumption for IRT, is often violated in real testing
[size=13.2835px]situations. In this article, we propose a new type of analytical approach for item response data that does not
[size=13.2835px]require standard local independence assumptions. By adapting a latent space joint modeling approach, our
[size=13.2835px]proposed model can estimate pairwise distances to represent the item and person dependence structures,
[size=13.2835px]from which item and person clusters in latent spaces can be identified. We provide an empirical data
[size=13.2835px]analysis to illustrate an application of the proposed method. A simulation study is provided to evaluate the
[size=13.2835px]performance of the proposed method in comparison with existing methods.
[size=13.2835px]Key words: latent space model, multilayer network, item response model, local dependence, cognitive