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.

Methods matter: Testing competing models for designing short-scale Big-Five assessments

“…ACO will not analyze all possible solutions, but instead uses a heuristic to converge to a high-quality solution over the course of several iterations. However, due to the heuristic nature of the search procedure, ACO is not guaranteed to find a single optimal solution and may result in different outcomes in separate runs (for more details, see Leite, Huang & Marcoulides, 2008).…”

“…The output generated by Mplus was analyzed in R in order to update pheromone levels and hence item selection probabilities. Our adaptation of ACO is based on the R-script used and provided by Leite, Huang and Marcoulides (2008). Our goal was to find the best fitting model concerning CFI and RMSEA.…”

Regularized Structural Equation Modeling

“…Model modification with the use of modification indexes might work well if the number of misspecifications is small, but when there is a higher degree of uncertainty regarding the model structure, global search strategies are needed. This has led to the proposal of heuristic search algorithms such as ant colony optimization (Leite, Huang, & Marcoulides, 2008; Marcoulides & Drezner, 2003), genetic algorithm (Marcoulides & Drezner, 2001), and tabu search (Marcoulides, Drezner, & Schumacker, 1998; for overview, see Marcoulides & Ing, 2012). …”

Regularized Structural Equation Modeling

“…Model modification with the use of modification indexes might work well if the number of misspecifications is small, but when there is a higher degree of uncertainty regarding the model structure, global search strategies are needed. This has led to the proposal of heuristic search algorithms such as ant colony optimization (Leite, Huang, & Marcoulides, 2008;Marcoulides & Drezner, 2003), genetic algorithm (Marcoulides & Drezner, 2001), and tabu search (Marcoulides, Drezner, & Schumacker, 1998; for overview, see Marcoulides & Ing, 2012).…”