Effects of Ignoring Item Interaction on Item Parameter Estimation and Detection of Interacting Items

Abstract

This study explores the effects of ignoring item interaction on item parameter estimation and the efficiency of using the local dependence index Q3 and the SAS NLMIXED procedure to detect item interaction under the three-parameter logistic model and the generalized partial credit model. Through simulations, it was found that ignoring positive item interaction led to overestimation for the discrimination parameters, underestimation for the difficulty parameters, and a Q3 much smaller than zero. As the guessing parameters approached zero, the overestimation for the discrimination parameters became more serious. In contrast, ignoring negative item interaction led to underestimation for the discrimination parameters, overestimation for the difficulty parameters, and a Q3 much larger than zero. As the guessing parameters approached zero, the underestimation for the discrimination parameters became less serious. A modification of posterior predictive p value for Q3 was proposed to detect item interaction and was found to work very well. Direct modeling of item interaction using NLMIXED was demonstrated.