Methods for Item Reduction in a Scale for Screening

Zhezhen Jin
Division of Biostatistics
Columbia University

Wednesday, April 2nd
3:30pm
MoosT 5-125
Minneapolis Campus

Abstract:
In this talk, I will present a nonparametric 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, two item selection methods are proposed. The proposed item selection methods evaluate 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. The method will be illustrated with a dataset on patients with and without high risk of developing Alzheimer's disease who were administered a 40-item test of olfactory function.

This is joint work with Dr. Xinhua Liu.

A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html