The so-called Ant Colony Optimization (ACO) Algorithm is based on the foraging behavior of ants. It simulates their behavior to find the shortest route between a food source and their nest and form ant trails to the food. This can be transferred to various problem-solving situations. In the current project, the algorithm is used to optimize existing questionnaires.

Studies investigating population-based behavioral prevention often assess multiple health behaviors. The usage of extensive tests and questionnaires places a burden on the participants, which influences the applicability of these approaches in a population. In order to lighten this burden, psychometrically robust short scales are crucial. In the current project, we examine existing questionnaires and aim to develop short scales for the assessment of self-efficacy and decisional balance for different health-related behaviors. We focus on the following issues:

• To what extent is the ACO algorithm an adequate method of item selection in the domains of alcohol and tobacco consumption as well as physical activity?
• Are short scales that were optimized with the ACO algorithm comparable or even more reliable than short scales constructed with conventional methods?
• Are the scales invariant across different points of time and can the ACO algorithm be used to select measurement invariant item sets?

Project Informations

Principal Investigator: Dr. Anne Möhring
Funding: Deutsche Forschungsgemeinschaft (DFG)
Project duration: 05/2020´- 05/2022