Abstract
The current investigation identified characteristics that discriminated authentic dyslexia from its simulation using measures common to postsecondary learning disability evaluations. Analyses revealed accurate simulation on most achievement measures but inaccurate feigning on neurolinguistic processing measures, speed on timed tasks, and error quantity. The largest group separations were on rapid naming, speeded orthographic, and reading fluency tasks. Simulators accurately feigned dyslexia profiles on cut-score and discrepancy diagnostic models but not on the more complex aspects of the clinical judgment model. Regarding simulation detection, a multivariate rule exhibited the greatest classification accuracy, followed by univariate indices developed from rapid naming tasks. The findings of the current study suggest that aspects of a comprehensive evaluation may aid in the detection of simulated dyslexia.
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