Testing for suspected impairments and dissociations in single-case studies in neuropsychology: Evaluation of alternatives using Monte Carlo simulations and revised tests for dissociations

Crawford, J. R. and Garthwaite, P. H. (2005). Testing for suspected impairments and dissociations in single-case studies in neuropsychology: Evaluation of alternatives using Monte Carlo simulations and revised tests for dissociations. Neuropsychology, 19(3) pp. 318–331.

DOI: https://doi.org/10.1037/0894-4105.19.3.318

URL: http://psycnet.apa.org/journals/neu/19/3/318/

Abstract

In neuropsychological single-case studies, a patient is compared with a small control sample. Methods of testing for a deficit on Task X, or a significant difference between Tasks X and Y, either treat the control sample statistics as parameters (using z and z(D)) or use modified t tests. Monte Carlo simulations demonstrated that if z is used to test for a deficit, the Type I error rate is high for small control samples, whereas control of the error rate is essentially perfect for a modified t test. Simulations on tests for differences revealed that error rates were very high for z(D). A new method of testing for a difference (the revised standardized difference test) achieved good control of the error rate, even with very small sample sizes. A computer program that implements this new test (and applies criteria to test for classical and strong dissociations) is made available.

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