Investigating the Classification Accuracy of Rasch and Nominal Weights Mean Equating with Very Small Samples

Abstract

ABSTRACTMaintaining equivalent performance standards across forms is a psychometric challenge exacerbated by small samples. In this study, the accuracy of two equating methods (Rasch anchored calibration and nominal weights mean) and four anchor item selection methods were investigated in the context of very small samples (N = 10). Overall, nominal weights mean equating slightly outperformed Rasch equating for three of the four anchor item selection methods, but Rasch equating slightly outperformed nominal weights mean equating when anchor items were selected to be near the cut score. The results largely confirmed previous research on the utility of nominal weights mean equating for very small samples. In addition, the results provide useful guidance for small volume programs who wish to consider using Rasch for building and equating new forms. Lastly, the results underscored the importance of being mindful about the method for selecting anchor items when building new forms.