Big Five Inventory-based participant selection method

Daniël Heikoop, Alba Rodríguez Sayrol, Marjan Hagenzieker

Research output: Contribution to conferenceAbstractScientific



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.


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.


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.


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.
Original languageEnglish
Number of pages1
Publication statusPublished - 2022
EventICTTP 2022: International Conference on Traffic and Transport Psychology - Gothenburg, Sweden
Duration: 23 Aug 202225 Aug 2022


ConferenceICTTP 2022


  • Big Five Inventory
  • personality traits
  • programming
  • participant selection


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