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Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm
Walter L Leite1, I-Chan Huang1, George A Marcoulides2
1a University of Florida .
Multivariate Behavioral Research
|January 8, 2016
Summary
Ant colony optimization (ACO) effectively creates shorter health scales. This algorithm outperformed traditional methods in developing a Diabetes-39 quality-of-life scale, demonstrating its utility for scale reduction.
Area of Science:
- Health outcomes research
- Psychometrics
- Computational intelligence
Background:
- Developing shorter health-related quality-of-life (HRQoL) scales is crucial for efficient patient assessment.
- Traditional item selection methods may not always yield optimal short forms.
- Diabetes-39 scale is a widely used measure for diabetes patient quality of life.
Purpose of the Study:
- To introduce and evaluate the Ant Colony Optimization (ACO) algorithm for developing short-form scales.
- To compare the performance of ACO with traditional item selection techniques.
- To demonstrate the application of ACO in creating a short form of the Diabetes-39 scale.
Main Methods:
- Application of the Ant Colony Optimization (ACO) algorithm for item selection.
- Development of a 22-item short form of the Diabetes-39 scale using a sample of 265 diabetes patients.
- Simulation study comparing ACO with factor loading and maximum test information item selection methods.
Main Results:
- The ACO algorithm successfully developed a 22-item short form of the Diabetes-39 scale.
- ACO demonstrated superior performance compared to largest factor loadings and maximum test information methods.
- The simulation study confirmed the effectiveness of ACO in item selection for scale reduction.
Conclusions:
- Ant Colony Optimization (ACO) is a capable and effective algorithm for developing short-form scales.
- ACO offers an advantageous alternative to traditional methods for scale shortening in health research.
- The developed short form of the Diabetes-39 scale shows the practical utility of ACO.
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