Malingering Scraper

Abstract

Malingered responses to psychological testing are frequent when monetary incentives or other forms of rewards are at stake. Psychological symptoms are usually identified through clinical questionnaires which, however, may be easily inflated by malingered responses (fake-bad). A fake-bad response style is usually identified through specialized scales embedded in the personality questionnaires, but no procedure is currently available that reconstructs honest responses from malingered responses. In this paper, we present a technique for the Millon (MCMI-III) questionnaire a widely used test for investigating psychopathology. This technique detects malingered MCMI-III profiles (malingering detector) and removes the intentionally inflated test results (malingering remover). We demonstrate that by applying machine learning to the validity scales of MCMI-III we can discriminate between malingerer and honest profiles with 90% accuracy. Moreover, our results show that by applying regression models to malingerer tests, we are able to well reconstruct the original honest profile. Our models decrease the RMSE (Root Mean Square Error) of the reconstruction up to 19% compared to base correction procedures. Finally, applying the malingering detector to the reconstructed scales, we show that only 9% were classified as malingerers, demonstrating the validity of the proposed approach.