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

Cortex. 2011 Nov-Dec;47(10):1166-78. doi: 10.1016/j.cortex.2011.02.017. Epub 2011 Mar 5.


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∼psy086/dept/Single_Case_Covariates.htm). Illustrative examples of the methods are provided.

MeSH terms

  • Algorithms
  • Bayes Theorem*
  • Case-Control Studies*
  • Data Interpretation, Statistical
  • Dissociative Disorders / diagnosis*
  • Dissociative Disorders / psychology
  • Humans
  • Models, Statistical
  • Multivariate Analysis
  • Nervous System Diseases / diagnosis*
  • Nervous System Diseases / psychology
  • Neuropsychology / methods*
  • Software