The Detection of Feigned Disabilities: The Effectiveness of the Personality Assessment Inventory in a Traumatized Inpatient Sample

Research on feigned mental disorders indicates that severe psychopathology coupled with significant trauma histories often complicate feigning determinations, resulting in inaccuracies on otherwise effective measures. As part of malingering assessments, the Personality Assessment Inventory (PAI) is...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Assessment (Odessa, Fla.) Fla.), 2012-03, Vol.19 (1), p.77-88
Hauptverfasser: Rogers, Richard, Gillard, Nathan D., Wooley, Chelsea N., Ross, Colin A.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Research on feigned mental disorders indicates that severe psychopathology coupled with significant trauma histories often complicate feigning determinations, resulting in inaccuracies on otherwise effective measures. As part of malingering assessments, the Personality Assessment Inventory (PAI) is often used because of its excellent validation and the availability of three feigning indicators (Negative Impression, Malingering Index, and Rogers Discriminant Function), which have evidenced large effect sizes and clinically useful cut scores. The current study examined the effectiveness of the PAI in a traumatized inpatient sample using a between-subjects simulation design. Although Negative Impression appeared affected by trauma—especially in conjunction with dissociative symptoms—very positive results were found for Malingering Index and Rogers Discriminant Function. They remained relatively unelevated under honest conditions, despite posttraumatic stress disorder and extensive comorbidity. Using single-point cut scores provided moderately good classification of feigned and genuine PAI profiles. For purposes of classification, the authors operationally defined small indeterminate groups that were considered too close to classify (i.e., ±5T of the cut scores). With indeterminate cases removed, the overall classification rates improved modestly. However, the more important finding involved the error rates for the indeterminate group, which exceeded 50%. Directions for further research are discussed.
ISSN:1073-1911
1552-3489
DOI:10.1177/1073191111422031