What to do with Contradictory Data? Approaches to the Integration of Multiple Malingering Measures

UMMS Affiliation

Department of Psychiatry; Systems and Psychosocial Advances Research Center

Publication Date


Document Type



Health Services Research | Law and Psychology | Mental and Social Health | Psychiatric and Mental Health | Psychiatry | Psychiatry and Psychology


This study describes the potential problems and possible solutions to the integration of multiple malingering measures. Multivariate prediction models, using both discriminant function analyses and regression tree approaches, are compared. Study measures, including an abbreviated version of the SIRS (SIRS-A), the MMPI-2, the TOMM and the VIP Verbal subtest, were administered to 29 community members instructed to malinger and 87 psychiatric patients instructed to respond honestly. Predictive accuracy varied substantially across measures and the correlations between tests ranged from .19 to .79. Further, 48% of the psychiatric sample were misclassified as malingering by at least one test and 46% of the malingering sample were classified as honest by at least one test; “unanimous” findings occurred in only half of the cases. Multivariate models identified the SIRS-A as the strongest predictor of malingering, but the MMPI-2, TOMM, and VIP provided significant contributions to these models. The implications of these findings for the problem of multiple, contradictory indicators in general, and the specific problems associated with clinical assessments of malingering in particular, are discussed.

DOI of Published Version



Rosenfeld, B., Green, D., Pivovarova, E., Dole, T., & Zapf, P. A. (2010). What to do with contradictory data? Approaches to the integration of multiple malingering measures. International Journal of Forensic Mental Health, 9, 63-73. doi: 10.1080/14999013.2010.499559

Journal/Book/Conference Title

International Journal of Forensic Mental Health


At the time of publication, Ekaterina Pivovarova was not yet affiliated with the University of Massachusetts Medical School.