This paper examines the conceptual and statistical difficulties created when neuropsychological research uses attribute variables in traditional orthogonal experimental designs. It is argued that attribute variables, as a result of their statistical and theoretical nonindependence, break the underlying assumptions of these traditional designs, and may lead to incorrect inferences being drawn. These difficulties are illustrated in a consideration of the typical use of analysis of variance designs, matched groups designs, and the analysis of covariance. Finally, a plea is made for the explicit consideration of the assumptions underlying the design models used in neuropsychological research, and a suggestion is made regarding the more appropriate use of correlational techniques in neuropsychology.
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