Assessing item fit: A comparative study of frequentist and Bayesian frameworks

Goodness of fit for item response theory (IRT) models in a frequentist and Bayesian framework are evaluated. The assumptions that are targeted are differential item functioning (DIF), local independence (LI), and the form of the item characteristics curve (ICC) in the one-, two-, and three parameter logistic models. It is shown that a Lagrange multiplier (LM) test, which is a frequentist based approach, can be defined in such a way that the statistics are based on the residuals, that is, differences between observations and their expectations under the model. In a Bayesian framework, identical residuals are used in posterior predictive checks. In a Bayesian framework, it proves convenient to use normal ogive representation of IRT models. For comparability of the two frameworks, the LM statistics are adapted from the usual logistic representation to normal ogive representation. Power and Type I error rates are evaluated using a number of simulation studies. Results show that Type I error rates are conservative in the Bayesian framework and that there is more power for the fit indices in a frequentist framework. An empirical data example is presented to show how the frameworks compare in practic