In principle, neural networks and other algorithms of machine learning can perfectly map unique relations of any complexity between input and output variables. We investigate whether using multi-layer neural networks allows improving personality assessments by constructing short-tests that are more efficient. Personality data for N = 3,498 participants from Germany, the US and the UK was collected using the International Personality Item Pool-300-item-version (IPIP-300 or IPIP-NEO), the Big Five Inventory (BFI-10) and the HEXACO Personality Inventory-60 (HEXACO-PI-R). We trained 40 multi-layer neural networks on this data to predict individuals’ scores on the Big-5-personality dimensions as well as facet scores from a 30-item version of the IPIP. A neural network based short-test version, IPIP30-NNet, predicted Big-5 dimensions from IPIP-300 as well as its facets with high accuracy. The correlations with the long-test scores (IPIP-300) were significantly higher compared to short-tests using a standard averaging algorithm and a multiple regression. Particularly for the facet scores, IPIP30-NNet lead to substantial improvements in predictive validity (Δr = .04 - .17). Additionally, as a syntheses of all three personality tests we calculated Big-5-“superscores”, which could be predicted from IPIP30-NNet with high accuracy as well. Our results demonstrate that neural network based diagnostic can be used to receive a very detailed individual personality profile based on very few information. We discuss challenges, potentials and future directions for using machine learning to improve standard psychological assessment.
Make this part of your paper trail
Save this paper to a shelf, write a review, and keep your own notes.