Abstract
In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives.
Highlights
Malingering is the dishonest and intentional production or exaggeration of physical or psychological symptoms in order to obtain external gain (Tracy & Rix, 2017)
The results indicated that the Structured Inventory of Malingered Symptomatology (SIMS) Total Score, scores for the Neurologic Impairment and Low Intelligence subscales, and scores for the MMPI-2-RF Infrequent Responses and Response Bias subscales successfully discriminated between symptom accentuators, symptom producers, and consistent participants
Third—and —we report on the classification of participants into groups of consistent versus inconsistent participants
Summary
Malingering is the dishonest and intentional production or exaggeration of physical or psychological symptoms in order to obtain external gain (Tracy & Rix, 2017). The SIMS is a multi-axial self-report questionnaire that has been validated with clinical-forensic, psychiatric, and non-clinical populations. It is composed of a list of 75 implausible symptoms or statements that subjects must endorse or reject. Its items index atypical depression, improbable memory problems, unlikely pseudo-neurological symptoms, doubtful claims of psychotic experiences, and hyperbolic signs of mental retardation. Each of these five categories (relating to neurologic impairment, affective disorders, psychosis, low intelligence, and amnestic disorders) is represented by a subscale composed of 15 items. The scale’s sensitivity for the commonly employed cutoff scores has been found to be adequate, ranging from 0.75 to 1.00, with corresponding specificity rates that are highly divergent (range 0.37–0.93), yet often alarmingly low
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