Inferential methods for comparing two single cases

In neuropsychological single-case studies, it is not uncommon for researchers to compare the scores of two single cases. Classical (and Bayesian) statistical methods are developed for such problems, which, unlike existing methods, refer the scores of the two single cases to a control sample. These methods allow researchers to compare two cases' scores, with or without allowing for the effects of covariates. The methods provide a hypothesis test (one- or two-tailed), point and interval estimates of the effect size of the difference, and point and interval estimates of the percentage of pairs of controls that will exhibit larger differences than the cases. Monte Carlo simulations demonstrate that the statistical theory underlying the methods is sound and that the methods are robust in the face of departures from normality. The methods have been implemented in computer programs, and these are described and made available (to download, go to http://www.abdn.ac.uk/~psy086/dept/Compare_Two_Cases.htm).

[1]  Paul H Garthwaite,et al.  Testing for suspected impairments and dissociations in single-case studies in neuropsychology: evaluation of alternatives using monte carlo simulations and revised tests for dissociations. , 2005, Neuropsychology.

[2]  Richard J. Brown Neuropsychology Mental Structure , 1989 .

[3]  D. C. Howell,et al.  Comparing an Individual's Test Score Against Norms Derived from Small Samples , 1998 .

[4]  A. Azzalini,et al.  Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution , 2003, 0911.2342.

[5]  Denise McCall,et al.  The role of processing support in the remediation of aphasic language production disorders , 2004, Cognitive neuropsychology.

[6]  P. Garthwaite,et al.  Point and interval estimates of effect sizes for the case-controls design in neuropsychology: Rationale, methods, implementations, and proposed reporting standards , 2010, Cognitive neuropsychology.

[7]  L. Joseph,et al.  Bayesian Statistics: An Introduction , 1989 .

[8]  K Willmes,et al.  An approach to analyzing a single subject's scores obtained in a standardized test with application to the Aachen Aphasia Test (AAT). , 1985, Journal of clinical and experimental neuropsychology.

[9]  A. Azzalini,et al.  Statistical applications of the multivariate skew normal distribution , 2009, 0911.2093.

[10]  G. Humphreys,et al.  Is oral spelling recognition dependent on reading or spelling systems? dissociative evidence from two single case studies , 2005, Cognitive neuropsychology.

[11]  J. Barth,et al.  The Halstead-Reitan Neuropsychological Test Battery. , 2000 .

[12]  D. Wedding Halstead‐Reitan Neuropsychological Test Battery , 2010 .

[13]  Jeremy MG Taylor,et al.  Robust Statistical Modeling Using the t Distribution , 1989 .

[14]  T. Shallice,et al.  Error analysis at the level of single moves in block design , 2004, Cognitive neuropsychology.

[15]  D. C. Howell Statistical Methods for Psychology , 1987 .

[16]  I. Gilchrist,et al.  Within-object and between-object coding deficits in drawing production , 2005, Cognitive neuropsychology.

[17]  P. Garthwaite,et al.  Investigation of the single case in neuropsychology: confidence limits on the abnormality of test scores and test score differences , 2002, Neuropsychologia.

[18]  H. Kashima,et al.  A deficit in discriminating gaze direction in a case with right superior temporal gyrus lesion , 2006, Neuropsychologia.

[19]  Paul H. Garthwaite,et al.  On comparing a single case with a control sample: An alternative perspective , 2009, Neuropsychologia.

[20]  A. Lamba,et al.  Differential deficits in expression recognition in gene-carriers and patients with Huntington ’ s disease , 2003 .

[21]  Paul H. Garthwaite,et al.  Methods of testing for a deficit in single-case studies: Evaluation of statistical power by Monte Carlo simulation , 2006, Cognitive neuropsychology.

[22]  Paul H Garthwaite,et al.  Comparison of a single case to a control or normative sample in neuropsychology: Development of a Bayesian approach , 2007, Cognitive neuropsychology.

[23]  H. Tagaya,et al.  Deficits in long-term retention of learned motor skills in patients with cortical or subcortical degeneration , 2004, Neuropsychologia.

[24]  Arjun K. Gupta,et al.  A multivariate skew normal distribution , 2004 .

[25]  Randi C. Martin,et al.  Lexical‐semantic retention and speech production:further evidence from normal and brain‐damaged participants for a phrasal scope of planning , 2004, Cognitive neuropsychology.

[26]  Tim Shallice,et al.  Dissociable distal and proximal motor components: Evidence from perseverative errors in three apraxic patients , 2005, Cognitive neuropsychology.

[27]  P. Garthwaite,et al.  Comparing a single case to a control sample: Testing for neuropsychological deficits and dissociations in the presence of covariates , 2011, Cortex.

[28]  Keith R. Laws,et al.  Testing for a deficit in single-case studies: Effects of departures from normality , 2006, Neuropsychologia.

[29]  Max Coltheart,et al.  Cognitive Neuropsychology , 2014, Scholarpedia.

[30]  Robert E. McCulloch,et al.  Elementary Bayesian Statistics , 1998 .

[31]  Leland Wilkinson,et al.  Statistical Methods in Psychology Journals Guidelines and Explanations , 2005 .

[32]  Glyn W Humphreys,et al.  Naming a giraffe but not an animal: Base-level but not superordinate naming in a patient with impaired semantics , 2005, Cognitive neuropsychology.

[33]  B. J. Casey,et al.  Semantic impairment with and without surface dyslexia: Implications for models of reading , 2005, Cognitive neuropsychology.

[34]  G. Humphreys,et al.  Object identification in simultanagnosia: When wholes are not the sum of their parts , 2004, Cognitive Neuropsychology.

[35]  Tim M. Gale,et al.  When is Category Specific in Alzheimer's Disease? , 2005, Cortex.