| Title: |
A Step Forward in Identifying Socially Desirable Respondents: An Integrated Machine Learning Model Considering T-Scores, Response Time, Kinematic Indicators, and Eye Movements |
| Authors: |
Mazza C.; Ceccato I.; Cannito L.; Monaro M.; Ricci E.; Bartolini E.; Cardinale A.; Di Crosta A.; Cardaioli M.; La Malva P.; Colasanti M.; Tambelli R.; Giromini L.; Palumbo R.; Di Domenico A.; Roma P. |
| Contributors: |
Mazza, C.; Ceccato, I.; Cannito, L.; Monaro, M.; Ricci, E.; Bartolini, E.; Cardinale, A.; Di Crosta, A.; Cardaioli, M.; La Malva, P.; Colasanti, M.; Tambelli, R.; Giromini, L.; Palumbo, R.; Di Domenico, A.; Roma, P. |
| Publisher Information: |
John Wiley and Sons Inc |
| Publication Year: |
2024 |
| Collection: |
Padua Research Archive (IRIS - Università degli Studi di Padova) |
| Description: |
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 ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001336280900001; volume:2024; issue:1; journal:HUMAN BEHAVIOR AND EMERGING TECHNOLOGIES; https://hdl.handle.net/11577/3540851 |
| DOI: |
10.1155/2024/7267030 |
| Availability: |
https://hdl.handle.net/11577/3540851; https://doi.org/10.1155/2024/7267030 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.A23D1BA7 |
| Database: |
BASE |