Item reduction in a scale for screening.

This paper presents a non-parametric approach for the selection of items in a scale for screening, with the score defined as the sum of item response indicators. Without specifying parametric models for binary classification probabilities, the proposed item selection method evaluates the change in classification accuracy due to adding or deleting one item for a scale with k items. It first removes least useful items from the scale and then uses a forward stepwise selection procedure to the remaining items to identify a subset of items for a reduced scale. The reduced scale usually retains or improves classification accuracy compared to the full scale. The variation in items selected can be assessed with bootstrap samples. In a simulation study, the proposed procedure shows a fairly good finite sample performance. The method is illustrated with a data set on patients with and without high risk of developing Alzheimer's disease who were administered a 40-item test of olfactory function.