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Logistic regression (LR) is recognized as a promising method for making decisions about neuropsychological performance validity by integrating information across multiple measures. However, this method has yet to be widely adopted in clinical practice, likely because several open questions remain about its utility relative to simpler methods, its effectiveness across different clinical contexts, and its feasibility at sample sizes common in the field. The current study addresses these questions by assessing classification performance of logistic regression and alternative methods across an array of simulated data sets. We simulated scores of valid and invalid performers on 6 tests designed to mimic the psychometric and distributional properties of real performance validity measures. Out-of-sample predictive performance of LR and a commonly used alternative (“vote counting”) was assessed across different base rates, validity measure properties, and sample sizes. LR improved classification accuracy by 2%–12% across simulation conditions, primarily by improving sensitivity. False positives and negatives can be further reduced when LR predictions are interpreted as continuous, rather than binary. LR made robust predictions at sample sizes feasible for neuropsychology research ( N  = 307) and when as few as 2 tests with good psychometric properties were used. Although training and test data sets of at least several hundred individuals may be required to develop and evaluate LR models for use in clinical practice, LR promises to be an efficient and powerful tool for improving judgements about performance validity. We offer several recommendations for model development and LR interpretation in a clinical setting.

Benefits and challenges of using logistic regression to assess neuropsychological performance validity: Evidence from a simulation study