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Wanted: fully operational definitions of dissociations in single case studies

Crawford, Joh R.; Garthwaite, Paul H. and Gray, Colin D. (2003). Wanted: fully operational definitions of dissociations in single case studies. Cortex, 39(2) pp. 357–370.

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In contrast to the careful consideration given to the issue of what we can infer from dissociations in single-case studies, the more basic question of how we decide whether a dissociation is present has been relatively neglected. Proposals are made for fully operational definitions of a deficit, classical and strong dissociations, and double dissociations. In developing these definitions it was assumed that they should be based on the use of inferential rather than descriptive statistical methods. The scope of these definitions is limited to typical single-case studies in which patients are compared to control samples of a modest size. The operational definition of a classical dissociation incorporates a requirement that a patient's performance on Task X is significantly different from Task Y, in addition to the “standard” requirement that the patient has a deficit on Task X and is within normal limits on Task Y. We ran a simulation to estimate the Type I error rates when the criteria for dissociations are applied and found these to be low (Type I errors were defined as identifying an individual from the control population as having a dissociation). The inferential methods for testing whether the various criteria are met make use of t-distributions. These methods are contrasted with the widespread use of z to test for a deficit or a difference between tasks. In the latter approach the statistics of the control sample are treated as parameters; this is not appropriate when, as is normally the case, the control sample size is modest in size.

Item Type: Journal Item
Copyright Holders: 2003 Elsevier Masson Srl
ISSN: 0010-9452
Keywords: double dissociation; single case studies; inferential statistics
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 22628
Depositing User: Sarah Frain
Date Deposited: 26 Aug 2010 10:24
Last Modified: 07 Dec 2018 09:38
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