A comparison of forensic and nonforensic malingerers: A prototypical analysis of explanatory models.

Abstract

Explanatory models of malingering strive to understand the primary motivation underlying attempts to feign. Roger~~ Sewell, and Goldstein (1994) provided empirical support for the concepiualization of pathogenic, criminological, and adaptational models. In the cllrrent slUdy, !l prototypical analysis of 221 forensic experts results in a slightly refined formulation: the adaptational model was decomposed into its two broad dimensions (cost-benefit analysis and adversarial setting). An important finding is that the factor structure for rhe explanatory models remained stable when applied to both forensic and nonforensic cases. As a first investigation, significant differences were observed in prototypical cases of malingering by the category of referral (forensic or nonforensic) and by type of feigning (mental disorders, cognitive impairment, and medical syndromes). Surprisingl>; the feigning of medical syndromes appeared to play a relatively prominent role in both forensic alld nonforensic cases and to be influenced by the apparent adversarial context of the assessment. Finally, important gender differences were observed, especially with nonforensic prototypical cases of malingering.

The Detection of Feigned Psychiatric Disorders Using the MMPI-2-RF Overreporting Validity Scales: An Analog Investigation

“…Predictive powers are more directly meaningful to clinicians and are therefore emphasized here, but they are heavily influenced by base rates. For that reason, we provide estimates of predictive powers across three different base rates, (1) base rate of 0.15, because it represents the lower end of empirically identified overreporting rates (Rogers et al , 1998, (2) base rate of 0.30, which represents the higher end of empirically derived overreporting rates (e.g., Mittenberg et al 2002) and (3) base rate of 0.50, which yields the fewest classification errors (i.e., sensitivity and specificity are maximized). In terms of selecting optimal cut scores, we chose those that would be associated with the best balance of overall classification and predictive powers across hypothetical base rates, with an aim at reducing falsepositive predictions to the extent feasible (i.e., not at too much cost of actual identification of over-reporting as such cut scores would be meaningless in classification).…”

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

“…In a review of literature, Rogers (1997) cited two large surveys estimating malingering prevalence rates to range between 15.7% and 17.4% among forensic settings. Nevertheless, these rates are in fact highly variable in forensic practice, with a standard deviation of 14.4% (Rogers, Salekin, Sewell, Goldstein, & Leonard, 1998). More recently, Larrabee (2003) grossly estimated the average rate of neuropsychological malingering from research publications to be 41%, although estimates in the individual studies differed by as much as 50%.…”

Do tests of malingering concur? Concordance among malingering measures

“…Although little controlled research has investigated the utility of the clinical interview to detect malingering (Ziskin, 1984), psychological testing has been extensively studied in this context. Indeed, in most cases, decisions about the accuracy of a somewhat higher, at approximately 15-17% (Rogers et al, , 1998. This figure may represent an underestimation, since ''successful'' malingerers, by definition, are not detected by clinicians and thus would not be included in their subjective base rate estimates (Berry, Baer, Rinaldo, & Wetter, 2002).…”