The effect of ignoring item interactions on the estimated discrimination parameters in item response theory.

Most item response theory models assume conditional independence, and it is known that interactions between items affect the estimated item discrimination. In this article, this effect is further investigated from a theoretical perspective and by means of simulation studies. To this end, a parametric model for item interactions is introduced. Next, it is shown that ignoring a positive interaction results in an overestimation of the discrimination parameter in the two-parameter logistic model (2PLM), whereas ignoring a negative interaction leads to an underestimation of the parameter. Furthermore, it is demonstrated that in some cases the item characteristic curves of the 2PLM and of an item involved in an interaction are quite similar, indicating that the 2PLM can provide a good fit to data with interactions.