Corresponding Author Information: Cristina Mazza
Session Abstract: Deliberate attempts to portray oneself in an unrealistic manner are commonly encountered in the administration of personality questionnaires. The main aim of the present study was to explore whether mouse tracking temporal indicators and machine learning models could improve the detection of subjects implementing a faking-good response style when answering personality inventories with four choice alternatives, with and without time pressure. A total of 120 volunteers were randomly assigned to one of four experimental groups and asked to respond to the Virtuous Responding (VR) validity scale of the PPI-R and the Positive Impression Management (PIM) validity scale of the PAI via a computer mouse. A mixed design was implemented, and predictive models were calculated. The results showed that, on the PIM scale, faking-good participants were significantly slower in responding than honest respondents. Relative to VR items, PIM items are shorter in length and feature no negations. Accordingly, the PIM scale was found to be more sensitive in distinguishing between honest and faking-good respondents, demonstrating high classification accuracy (80-83%).
Cristina Mazza | Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
Merylin Monaro | Department of General Psychology University of Padova, Padua, Italy
Marco Colasanti | Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
Eleonora Ricci | Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
Alberto Di Domenico | Department of Psychological, Health, and Territorial Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
Paolo Roma | Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy