The Development of the Inventory of Problems–29: A Brief Self-Administered Measure for Discriminating Bona Fide From Feigned Psychiatric and Cognitive Complaints

Abstract

This article describes the development of the Inventory of Problems-29 (IOP-29), a new, short, paper-and-pencil, self-administered measure of feigned mental and cognitive disorders.Four clinical comparison, simulation studies were conducted. Study 1 (n = 451) selected the items and produced an index of potential feigning. Study 2 (n = 331) scaled such index to produce a probability score, and examined its psychometric properties. Study 3 tested the generalizability of Study 2's findings with two additional samples (ns = 128 and 90). Results supported the utility of the IOP-29 for discriminating bona fide from feigned psychiatric and cognitive complaints. Validity was demonstrated with mild traumatic brain injury, psychosis, PTSD, and depression. Within the independent samples of studies 2 and 3, the brief IOP-29 performed similarly to the MMPI-2 and PAI, and perhaps better than the TOMM.Classifications within these samples with base rates of .5 produced sensitivity, specificity, positive predictive power, and negative predictive power statistics of about .80. Further research is needed testing the IOP-29 in ecologically valid field studies.

The Self-Report Symptom Inventory (SRSI) is sensitive to instructed feigning, but not to genuine psychopathology in male forensic inpatients: An initial study

“…Third, unlike the SIMS, the SRSI contains symptom scales that are relevant to symptomatology typically prominent in civil litigation cases, such as pain, fatigue, and PTSD. With the exception of the Inventory of Problems-29 (IOP-29;Viglione, Giromini, & Landis, 2016) and the Modified Somatic Perception Questionnaire and the Pain Disability Index (MSPQ and PDI;Crighton, Wygant, Applegate, Umlauf, & Granacher, 2014), no other SVTs address these types of problems. It would be informative if future studies would compare psychometric performance of the SRSI, IOP-29, MSPQ, and PDI across various clinical samples.…”

An Inventory of Problems–29 Study on Random Responding Using Experimental Feigners, Honest Controls, and Computer-Generated Data

“…In terms of demographics, the combined sample encompassing 891 adult volunteers was highly heterogeneous regarding gender (men = 44.4%), age (M ≈ 42, SD ≈ 14), 1 education (high school or less = 61.9%), racial characterization (about 70% came from Italy, and about 30% -of which slightly more than half defined themselves as "White" -from the US), and marital status (in a relationship = 58.3%) (for details, see Giromini et al, 2018;Giromini et al, 2019a;Viglione et al, 2017). The great majority of this sample, i.e., n = 791, was used to develop the Hon-RRS (Honest Responders -Developmental Sample); 100 were randomly extracted to be used for cross-validation purposes (Honest Responders -Validation Sample).…”

“…As for the sample of experimental simulators, an archival data set of 910 IOP-29s, 274 from the US (Viglione et al, 2017) and 636 from Italy (236 from Giromini et al, 2018; 400 from Giromini et al, 2019a), contributed to this research. As for the 400 IOP-29s from Giromini et al (2019a), they were taken from the second condition described above, in which participants were asked to take the test as if they wanted to convince the examiner that they were mentally ill.…”

“…In all cases, simulators were provided with a brief scenario or vignette from real-life situations, and were warned not to be too dramatic in their symptom presentations, so to improve their ability to feign in a more credible way (Rogers & Bender, 2013). In terms of conditions to be feigned, each participant was instructed to pretend to suffer Also the combined, experimental simulators sample was highly heterogeneous regarding (men = 43.2%), age (M ≈ 38, SD ≈ 13), education (high school or less = 54.0%), racial characterization (about 70% came from Italy, and about 30% -of which slightly more than half defined themselves as "White" -from the US), and marital status (in a relationship = 40.6%) (for details, see Giromini et al, 2018;Giromini et al, 2019a;Viglione et al, 2017). These data served to develop and validate an indicator of infrequency and an indicator of inconsistency based on responding patterns of experimental simulators.…”

The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment

“…However, some studies have estimated a much higher prevalence, especially in forensic and non-forensic neuropsychological settings, with approximate rates ranging from 30 to 50% (Ardolf et al, 2007;Chafetz, 2008;Larrabee et al, 2009;Martin & Schroeder, 2020;Mittenberg et al, 2002). Given the cost and implications of malingering to the healthcare system, it is not surprising that several instruments have been introduced to assess the credibility of symptom presentations (see, e.g., the Structured Interview of Reported Symptoms-Second edition [SIRS-2;Rogers et al, 2010], the Test of Memory Malingering [TOMM;Tombaugh, 1996], the Self-Report Symptom Inventory [SRSI;Merten et al, 2016], and the Inventory of Problems-29 [IOP-29;Viglione et al, 2017; see also Roma et al, 2019a]). Among these instruments, the most widely used standalone symptom validity test in Europe and North America is the Structured Inventory of Malingered Symptomatology (SIMS; Dandachi-FitzGerald et al, 2013;Martin et al, 2015;Smith & Burger, 1997).…”