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Useful effect size interpretations for single case research.

Richard I Parker1, Shanna Hagan-Burke

  • 1Department of Educational Psychology, Texas A&M University, College Station 77843, USA. rparker@tamu.edu

Behavior Therapy
|February 13, 2007
PubMed
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Interpreting effect sizes in single case research (SCR) is challenging. This study offers five alternative interpretations for Cohen's d and R2, aiming for more intuitive understanding and broader acceptance in SCR. Three methods show promise.

Area of Science:

  • Behavioral Sciences
  • Psychology
  • Educational Research

Background:

  • Effect sizes in single case research (SCR) lack intuitive interpretations, hindering broader acceptance.
  • Current interpretations of Cohen's d and R2 are not well-suited for visual analysts in SCR.
  • Percent of nonoverlapping data is the only widely supported comparative analysis in SCR.

Purpose of the Study:

  • To explore five alternative interpretations of Cohen's d and R2 effect sizes for the SCR field.
  • To evaluate these interpretations based on intuitive appeal, relevance to visual analysis, ease of calculation, and technical adequacy.
  • To identify potential improvements over prevailing effect size interpretation practices in SCR.

Main Methods:

  • Five alternative interpretation schemes for effect sizes were examined: Cohen's Percent of Nonoverlapping Data (CPND), Parker et al.'s Percent of All Nonoverlapping Data (PAND), Binomial Effect Size Display (BESD), Percentile Rank in Control Group (PR), and Common Language Effect Size (CLES).

Related Experiment Videos

  • Each interpretation was applied to a published single case research data set.
  • Evaluations focused on intuitive appeal, relevance to visual analysis, computational simplicity, and technical soundness.

    • Main Results:

    • The study applied and evaluated five distinct effect size interpretation methods for single case research.
    • Three of the five explored interpretation schemes were found to be improvements over current practices.
    • The evaluated methods offer potentially more accessible and relevant ways to understand effect sizes for visual analysts.

    Conclusions:

    • Several alternative effect size interpretations show promise for enhancing the utility and acceptance of quantitative findings in single case research.
    • These methods may bridge the gap between statistical effect sizes and the visual analysis commonly employed in SCR.
    • Further adoption of these intuitive effect size measures could strengthen the evidence base and communication within the SCR community.