Person Misfit in Attitude Surveys: Influences, Impacts and Implications

This study of person fit in attitude surveys was undertaken in order to investigate the influence of the inclusion of misfitting persons on item parameter estimates in analyses using the Partial Credit extension of the Rasch measurement model. It was hypothesised that the inclusion of misfitting persons in data sets used for the calibration of attitude survey instruments might compromise the measurement properties of those instruments. Using both actual and simulated data sets, the inclusion of misfitting cases was found to reduce item variance. Several characteristics of both item and person samples were found to influence the proportion of cases identified as misfitting. These characteristics must be considered before removing cases that, according to customary practice, appear to misfit. The residual based misfit indicators that are commonly reported in Rasch analyses, the weighted and unweighted mean squares, appear not to have the generality over all instruments nor the precision required to make clear decisions on the retention or elimination of cases from samples, and there is a need to seek better misfit indicators.