Using Timer Data to Conjunct Self-Reported Measures in Quantifying Deception

This paper proposes the reduction of self-reported measures in deception quantifying assessment psychometric devices through the integration of digital trace data to limit bias and cognitive laziness. This paper then proceeded to test this proposal through a simulation involving a user study where a previous work's predictive model was recreated to incorporate such changes. The results highly suggest that the conjunction of digital trace data yields lower unaccounted variance (i.e., noise) and stronger forecasting prowess of the predictive models. The intended target audiences of this paper are information scientists, digital forensic professionals, communication experts, and policymakers possibly seeking references in this application area.