The purpose of this paper is to present two new alternative methods to the current goodness of fit methodology. With the increase use of computerized adaptive test (CAT), the ability to determine the accuracy of calibrated item parameter estimates is paramount. The first method applies a normalizing transformation to the logistic residuals to make them more interpretable. The second method translates residuals directly into a loss of information statistic. Both methods require a CAT simulation to accurately assess the ability range over which an item would most likely be chosen. Results suggest that the lack of fit in the logistic regression should not be a major concern in developing a CAT item pool. Suggestions for further research are made. (Author)