title: Comparing a single case to a control sample: testing for neuropsychological deficits and dissociations in the presence of covariates.
abstract: Existing inferential methods of testing for a deficit or dissociation in the single case are extended to allow researchers to control for the effects of covariates. The new (bayesian) methods provide a significance test, point and interval estimates of the effect size for the difference between the case and controls, and point and interval estimates of the abnormality of a case's score, or standardized score difference. The methods have a wide range of potential applications, e.g., they can provide a means of increasing the statistical power to detect deficits or dissociations, or can be used to test whether differences between a case and controls survive partialling out the effects of potential confounding variables. The methods are implemented in a series of computer programs for PCs (these can be downloaded from www.abdn.ac.uk/∼psy086/dept/Single_Case_Covariates.htm). Illustrative examples of the methods are provided.
authors: School of Psychology, College of Life Sciences and Medicine, King’s College, University of Aberdeen, Aberdeen AB24 2UB, UK. j.crawford@abdn.ac.uk
authors: John R
authors: JR
authors: Crawford
authors: Paul H
authors: PH
authors: Garthwaite
authors: Kevin
authors: K
authors: Ryan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D004213:Dissociative Disorders / Q000175:diagnosis* / Q000523:psychology; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D009422:Nervous System Diseases / Q000175:diagnosis* / Q000523:psychology; D009484:Neuropsychology / Q000379:methods*; D012984:Software