Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm

Abstract

This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and traditionally used methods of item selection is also presented. It is shown that the ACO algorithm outperforms the largest factor loadings and maximum test information item selection methods. The results demonstrate the capabilities of using ACO for creating short-form scales.

ACKNOWLEDGMENTS

An earlier version of this article was presented at the annual meeting of the American Educational Research Association, Chicago, April 2007.

Notes

¹ The R program that implements the ACO algorithm alongside Mplus is provided at Walter L. Leite's Web site: http://education.ufl.edu/Faculty/Leite/publications.html

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