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Studies investigating the power of person-fit statisticsoften assume that the item parameters that are usedto calculate the statistics are estimated in a sample withoutmisfitting item score patterns. However, in practicaltest applications calibration samples likely will containsuch patterns. In the present study, the influence of thetype and the number of misfitting patterns in the calibrationsample on the detection rate of the ZU3 statistic wasinvestigated by means of simulated data. An increase inthe number of misfitting simulees resulted in a decreasein the power of ZU3. Furthermore, the type of misfit andthe test length influenced the power of ZU3. The use ofan iterative procedure to remove the misfitting patternsfrom the dataset was investigated. Results suggested thatthis method can be used to improve the power of ZU3.Index terms: aberrance detection, appropriateness measurement,nonparametric item response theory, personfit, person-fit statistic ZU3

The Influence of the Presence of Deviant Item Score Patterns on the Power of a Person-Fit Statistic