Likelihood-Based Item-Fit Indices for Dichotomous Item Response Theory Models

Abstract

New goodness-of-fit indices are introduced for dichotomous item response theory (IRT) models. These indices are based on the likelihoods of number-correct scores derived from the IRT model, and they provide a direct comparison of the modeled and observed frequencies for correct and incorrect responses for each number-correct score. The behavior of Pearson’s X² ( S- X²) and the likelihood ratio G² ( S- G²) was assessed in a simulation study and compared with two fit indices similar to those currently in use ( Q1- X² and Q₁- G²). The simulations included three conditions in which the simulating and fitting models were identical and three conditions involving model misspecification. S- X² performed well, with Type I error rates close to the expected .05 and .01 levels. Performance of this index improved with increased test length. S- G² tended to reject the null hypothesis too often, as did Q₁- X² and Q₁- G². The power of S- X² appeared to be similar for all test lengths, but varied depending on the type of model misspecification.