Developing cross-cultural short scales using ant colony optimization

This article demonstrates how the metaheuristic item selection algorithm ant colony optimization (ACO) can be used to develop short scales for cross-cultural surveys. Traditional item selection approaches typically select items based on expert-guided assessment of item-level information in the full scale, such as factor loadings or item correlations with relevant outcomes. ACO is an optimization procedure that instead selects items based on the properties of the resulting short models, such as model fit and reliability. Using a sample of 5,567 respondents from five countries, we selected a 15-item short form of the Big Five Inventory–2 with the goal of optimizing model fit and measurement invariance in exploratory structural equation modeling, as well as reliability, construct coverage, and criterion-related validity of the scale. We compared the psychometric properties of the new short scale with the Big Five Inventory–2 extra-short form developed with a traditional approach. Whereas both short scales maintained the construct coverage and criterion-related validity of the full scale, the ACO short scale achieved better model fit and measurement invariance across countries than the Big Five Inventory–2 extra-short form. As such, ACO can be a useful tool to identify items for cross-cultural comparisons of personality.