Comparing a single case to a control sample: testing for neuropsychological deficits and dissociations in the presence of covariates

John R. Crawford, Paul H. Garthwaite, Kevin Ryan

Research output: Contribution to journalArticle

57 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1166-1178
Number of pages13
JournalCortex
Volume47
Issue number10
Early online date5 Mar 2011
DOIs
Publication statusPublished - Nov 2011

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Confounding Factors (Epidemiology)
Bayes Theorem
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Keywords

  • single case
  • covariates
  • statistical methods

Cite this

Comparing a single case to a control sample : testing for neuropsychological deficits and dissociations in the presence of covariates. / Crawford, John R.; Garthwaite, Paul H.; Ryan, Kevin.

In: Cortex, Vol. 47, No. 10, 11.2011, p. 1166-1178.

Research output: Contribution to journalArticle

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