PUB - Publikationen an der Universität Bielefeld

Gender differences in personality are typically summarised using broad personality domains. To acknowledge meaningful variation on lower levels of the personality hierarchy, we studied gender differences in facets and single-item nuances. We used machine learning methods to predict gender from aggregate traits of domains and facets, and unaggregated items in previously collected data from four different inventories (IPIP-NEO, NEO-PI-R, HEXACO and BFI-2). In addition, we trained models on IPIP-NEO data from the United States and validated these on data from 74 countries. By different degrees across inventories and countries, items outpredicted facets which outpredicted domains. We present nuances with the smallest and largest gender differences, both those that were the most and least specific to countries. Our results are consistent with research on the multidimensionality of personality traits and suggest that many gender differences are specific to narrow traits and geographical contexts. While gender is predictable from trait combinations, we show that predicting personality traits from gender is virtually impossible and conclude that sweeping statements and generalisations about gender differences are inconsistent with data.