Measure-shortening techniques: A how-to guide for within-person personality research

Experience sampling methods are valuable for studying within-person personality dynamics but can burden participants and thus compromise data quality. While shortened versions of established measures can mitigate this burden, selecting items that maintain psychometric properties and content representativeness remains challenging. The present study evaluated three statistical techniques for item selection: confirmatory factor analysis (CFA) factor loadings, Optimization App for Selecting Item Subsets (OASIS), and Ant Colony Optimization (ACO). Using a sample of 257 participants, we compared these techniques for shortening state personality measures of extraversion and neuroticism, examining psychometric properties, convergent and discriminant validity, and content representativeness. Results showed that specific techniques work better than others for different criteria depending on scale characteristics. We provide a decision tree to guide researchers in selecting appropriate techniques based on their needs, while recommending that combining multiple approaches may often be advisable.