The presence of response disturbances or aberrant response patterns is often assessed by the computation of person fit indexes. These indicate whether a pattern, as a whole, can be considered as abnormal with respect to the test characteristics. However, most often they require item parameters to be pre-calibrated and this calibration is performed upon the same data set. The presence of response disturbances might therefore impact the item calibration process and subsequently, the identification of person misfit. This paper presents a straightforward iterative process to reduce the risk of unfair item calibration due to the presence of response disturbances. The idea consists in iteratively removing the patterns flagged as aberrant from the item calibration process, and re-computing the person fit indexes with newly calibrated parameters. The process is illustrated by analyzing the data from an English skill assessment questionnaire in Quebec. applying the process to these data reveals: an increase in the number of response patterns flagged as aberrant, a somewhat weak impact on estimated item parameters, with a limited number of iterations required to reach convergence of the iterative process.