Introducing Machine Learning to Detect Personality Faking-Good in a Male Sample: A New Model Based on Minnesota Multiphasic Personality Inventory-2 Restructured Form Scales and Reaction Times

Abstract

Background and Purpose. The use of machine learning (ML) models in the detection of malingering has yielded encouraging results, showing promising accuracy levels. We investigated the possible application of this methodology when trained on behavioral features, such as response time (RT) and time pressure, to identify faking behavior in self-report personality questionnaires. To do so, we reintroduced the article of Roma et al. (2018), which highlighted that RTs and time pressure are useful variables in the detection of faking; we then extended the number of participants and applied an ML analysis. Materials and Methods. The sample was composed of 175 subjects, of whom all were graduates (having completed at least 17 years of instruction), male, and Caucasian. Subjects were randomly assigned to four groups: honest speeded, faking-good speeded, honest unspeeded, and faking-good unspeeded. A software version of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) was administered. Results. Results indicated that ML algorithms reached very high accuracies (around 95%) in detecting malingerers when subjects are instructed to respond under time pressure. The classifiers’ performance was lower when the subjects responded with no time restriction to the MMPI-2-RF items, with accuracies ranging from 75% to 85%. Further analysis demonstrated that T -scores of validity scales are ineffective to detect fakers when participants were not under temporal pressure (accuracies 55–65%), whereas temporal features resulted to be more useful (accuracies 70–75%). By contrast, temporal features and T -scores of validity scales are equally effective in detecting fakers when subjects are under time pressure (accuracies higher than 90%). Discussion. To conclude, results demonstrated that ML techniques are extremely valuable and reach high performance in detecting fakers in self-report personality questionnaires over more the traditional psychometric techniques. Validity scales MMPI-2-RF manual criteria are very poor in identifying under-reported profiles. Moreover, temporal measures are useful tools in distinguishing honest from dishonest responders, especially in a no time pressure condition. Indeed, time pressure brings out malingerers in clearer way than does no time pressure condition.

Use of mouse-tracking software to detect faking-good behavior on personality questionnaires: an explorative study

“…On the one hand, honest respondents remained honest under the speeded condition, indicating that they were not affected by time pressure; on the other hand, faking-good respondents in the speeded condition did not show the expected significant increase in T-scores. This result does not exactly agree with the findings of previous studies 17,22,23 , demonstrating that a speeded condition induces fakers to significantly improve their self-presentation (as demonstrated by increased T-scores) relative to an unspeeded condition, on both the MMPI-2-RF L-r and K-r scales 17 and the MMPI-2 L and K scales 23 . The authors of these studies suggested that time pressure may limit respondents' ability to consider the appropriateness of endorsing particularly virtuous items, and this may lead them to enhance their positive self-presentation and subsequently present less believable profiles.…”

“…To conclude, this exploratory study suggests that some parameters of mouse dynamics-especially velocity on the x-axis-could be useful for detecting subjects who fake good when completing the validity scales of the MMPI-2 and PPI-R personality questionnaires, independent of whether a time pressure condition is imposed. However, upon comparing the accuracy performance obtained in this study (72-80%) with the accuracies reported in previous studies, it seems that mouse parameters may be less accurate than simple RT analysis (with reported accuracy ranging from 75-95%) 22 for this task. The present findings are still preliminary and confirmatory studies are needed.…”

“…To explain these findings, Shalvi et al 20 suggested that subjects with limited time to reflect lie more frequently, whereas those with more time available choose their answers more cautiously in an attempt to moderate their faking behaviour. This idea was confirmed in a recent study 17,22 , in which time pressure led fakers to significantly improve their self-presentation on the L-r and K-r scales of the MMPI-2-RF (η 2 p = 0.243). Roma et al 23 also pointed out that time pressure can be useful in distinguishing between honest respondents and fakers, even when items are rejected: in their research, no difference in RTs between honest respondents and fakers was registered in the unspeeded condition, while a statistically significant difference was observed in the speeded condition (MMPI-2 L scale η 2 = 0.481; MMPI-2 K scale η 2 = 0.457; MMPI-2 S scale η 2 = 0.011).…”

Machine Learning Increases Diagnosticity in Psychometric Evaluation of Alexithymia in Fibromyalgia

“…One such method is supervised machine learning (ML), an area of artificial intelligence concerned with the development of algorithms and techniques able to automatically extract information from the available data. In recent years, it has been shown that psychometric testing may be augmented using ML techniques (20) in different fields application, amongst others in neuroimaging (21), malingering (22,23), genetics (24), and clinical medicine (25). As far as we know, this is the first time that such methods have been applied to fibromyalgia and related psychological domains of interest.…”

An Item-Level Analysis for Detecting Faking on Personality Tests: Appropriateness of Ideal Point Item Response Theory Models

“…Many methodologies and techniques have been developed for detecting response distortion over the years, for example, machine learning models, reaction times, regression analysis, etc. (Dunn et al, 1972;Sellbom and Bagby, 2010;Jiménez Gómez et al, 2013;Monaro et al, 2018;Roma et al, 2018;Mazza et al, 2019). Still, there is a concern about the perceptions and interpretations of the change on items due to intentional dissimulation.…”