A Step Forward in Identifying Socially Desirable Respondents: An Integrated Machine Learning Model Considering T-Scores, Response Time, Kinematic Indicators, and Eye Movements

Abstract

Context: In high-stakes assessments, such as court cases or managerial evaluations, decision-makers heavily rely on psychological testing. These assessments often play a crucial role in determining important decisions that affect a person’s life and have a significant impact on society.

Problem Statement: Research indicates that many psychological assessments are compromised by respondents’ deliberate distortions and inaccurate self-presentations. Among these sources of bias, socially desirable responding (SDR) describes the tendency to provide overly positive self-descriptions. This positive response bias can invalidate test results and lead to inaccurate assessments.

Objectives: The present study is aimed at investigating the utility of mouse- and eye-tracking technologies for detecting SDR in psychological assessments. By integrating these technologies, the study sought to develop more effective methods for identifying when respondents are presenting themselves in a favorable light.

Methods: Eighty-five participants completed the Lie (L) and Correction (K) scales of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) twice: once answering honestly and once presenting themselves in a favorable light, with the order of conditions balanced. Repeated measures univariate analyses were conducted on L and K scale T-scores, as well as on mouse- and eye-tracking features, to compare the honest and instructed SDR conditions. Additionally, machine learning models were developed to integrate T-scores, kinematic indicators, and eye movements for predicting SDR.

Results: The results showed that participants in the SDR condition recorded significantly higher T-scores, longer response times, wider mouse trajectories, and avoided looking at the answers they intended to fake, compared to participants in the honest condition. Machine learning algorithms predicted SDR with 70%–78% accuracy.

Conclusion: New assessment strategies using mouse- and eye-tracking can help practitioners identify whether data is genuine or fabricated, potentially enhancing decision-making accuracy.

Implications: Combining self-report measures with implicit data can improve SDR detection, particularly in managerial, organizational, and forensic contexts where precise assessments are crucial.