What if there were no personality factors? Comparing the predictability of behavioral act frequencies from a big-five and a maximal-dimensional item set

Personality inventories are predominantly curated using factor analytic approaches. Indicators capturing common and thus redundant variance are preferentially selected, whereas indicators capturing a large proportion of unique variance outside the broad trait domains are omitted from further research. Even recent research dealing with lower-level personality traits such as facets or nuances has invariably relied on inventories founded on this factor analytic approach. However, items can also be selected to ensure low instead of high communality amongst them. The expected predictive power of such item sets is higher compared to those compiled to capitalize on the indicators' redundancy. To investigate this, we applied Ant Colony Optimization (ACO) to select personality-descriptive adjectives with minimal inter-item correlations. When used to predict the frequency of everyday life behaviors, this 'crude-grit' set outperformed a traditional big-five item set and sets of randomly selected adjectives. The size of the predictive advantage of the crude-grit set was generally higher for those behaviors that could also be predicted better by the big-five item set. This study provides a proof-of-concept for an alternative procedure for compiling personality scales, and serves as a starting point for future studies using broader item sets.