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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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On comparing a single case with a control sample: an alternative perspective.

John R Crawford1, Paul H Garthwaite, David C Howell

  • 1School of Psychology, King's College, University of Aberdeen, Aberdeen AB24 2UB, UK. j.crawford@abdn.ac.uk

Neuropsychologia
|April 23, 2009
PubMed
Summary
This summary is machine-generated.

Statistical methods for single-case studies are evaluated. The Crawford and Howell method is valid for comparing individual scores to control groups, unlike Corballis's approach, which has limitations.

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Area of Science:

  • Neuropsychology
  • Statistical Inference
  • Single-Case Research

Background:

  • Critically examines Corballis's position on statistical inference in single-case studies.
  • Addresses the validity of testing null hypotheses for single-case research.
  • Compares Corballis's method with the Crawford and Howell method.

Discussion:

  • Corballis's method is found to be illegitimate due to failure to account for control mean uncertainty, risking Type I errors.
  • Crawford and Howell's method is validated for testing if a patient's score deviates from a control population.
  • Discusses the applicability of Crawford and Howell's method for comparing notional patient population means to control means.

Key Insights:

  • The Crawford and Howell method correctly tests the null hypothesis for single-case research.
  • Corballis's proposed method is statistically flawed for comparing single cases to control samples.
  • Mixed ANOVA designs for case-control comparisons present issues that can be resolved with alternative statistical approaches.

Outlook:

  • Recommends the use of validated methods like Crawford and Howell's for robust single-case statistical inference.
  • Highlights the importance of controlling Type I errors in single-case research.
  • Suggests exploring alternative statistical designs to overcome limitations of mixed ANOVA in case-control comparisons.