Background Investigating personality is commonly performed using the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991; John, Naumann, & Soto, 2008). Five different traits can be distinguished using 44 multiple-choice questions, which can be convenient for preselecting participants; for instance for investigating individual differences in driving with automated vehicles. However, high scores on one trait are regularly accompanied with high scores on another. When aiming for unique participants per trait, this can be troublesome and time-consuming. This study provides a MATLAB calculation method solving this issue. Methods Participant selection is made through a selection algorithm. First, questionnaire answers are placed in an Excel file. Then, five lists (one for each category) are generated of the selected participants who have the highest results. Since it is possible that one participant acquires the highest score or the same percentage in different categories, two algorithms are used. The first normalizes the participants’ scores, and the second tracks the highest score of the five categories. Each participant was selected for (only) their best trait, making for the most profound traits for the entire selection. Results The resulting matrix presents five lists of unique participants with their corresponding score on their respective trait. The code works optimally at higher numbers of entries and has no upper boundary. When a participant scores equally high on two (or more) traits, it selects the trait most beneficial for the entire participant pool, so that each trait has the highest possible average. Conclusions Our MATLAB code, designed for selecting the most appropriate participant for each trait based on the BFI, is found to be successful in selecting unique participants for each trait, and accounting for equal scores on traits, preferring the entire participant pool over the individual scores. This code can be used by other researchers aiming to use the BFI as a means of selection criterion. Our code is found to be robust for higher numbers of entries, and quick and easy to use.@en