Title
Identification of insufficient cognitive effort using the MMPI-2: An optimal classification tree analysis
Department/School
Psychology, Professional
Date of this version
2008
Document Type
Article
Keywords
malingering, neuropsychological assessment, optimal discriminant analysis, personality assessment, response validity, response bias scale
Abstract
Neuropsychologists routinely rely on response validity measures to evaluate the authenticity of test performances. However, the relationship between cognitive and psychological response validity measures is not clearly understood. It remains to be seen whether psychological test results can predict the outcome of response validity testing in clinical and civil forensic samples. The present analysis applied a unique statistical approach, classification tree methodology (Optimal Data Analysis: ODA), in a sample of 307 individuals who had completed the MMPI-2 and a variety of cognitive effort measures. One hundred ninety-eight participants were evaluated in a secondary gain context, and 109 had no identifiable secondary gain. Through recurrent dichotomous discriminations, ODA provided optimized linear decision trees to classify either sufficient effort (SE) or insufficient effort (IE) according to various MMPI-2 scale cutoffs. After “pruning” of an initial, complex classification tree, the Response Bias Scale (RBS) took precedence in classifying cognitive effort. After removing RBS from the model, Hy took precedence in classifying IE. The present findings provide MMPI-2 scores that may be associated with SE and IE among civil litigants and claimants, in addition to illustrating the complexity with which MMPI-2 scores and effort test results are associated in the litigation context.
DOI
https://doi.org/10.1017/S1355617708081034
Volume
14
Issue
5
Published in
Journal of the International Neuropsychological Society
Citation/Other Information
Smart, C., Nelson, N. W., Sweet, J. J., Bryant, F., & Heilbronner, R. L. (2008). Use of MMPI-2 to predict cognitive effort: A hierarchically optimal classification tree analysis. Journal of the International Neuropsychological Society, 14(5), 842-852. https://doi.org/10.1017/S1355617708081034