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Interpreting treatment effect size from single case experimental design data: a preliminary analysis of differential

D M Richman1, L Barnard-Brak2, L Watkins2

  • 1Special Education, Texas Tech University, Lubbock, TX, USA.

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Summary
This summary is machine-generated.

Effect sizes for single case experimental designs (SCED) differ based on treatment goals. Treatments increasing behavior show larger effect sizes than those decreasing behavior, necessitating distinct interpretation benchmarks.

Keywords:
SCEDadvanced quantificationeffect sizesingle case experimental designs

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Area of Science:

  • Behavioral Science
  • Research Methodology
  • Psychometrics

Background:

  • Treatment effect size estimation in single case experimental designs (SCED) can be influenced by the direction of the dependent variable's slope.
  • Effect sizes for interventions decreasing behavior are constrained by a zero baseline, while those increasing behavior have a less restricted ceiling.

Purpose of the Study:

  • To conduct a preliminary analysis of the mean and range of SCED effect sizes for treatments aimed at increasing or decreasing target behaviors.
  • To investigate the impact of treatment direction on effect size estimation in SCED.

Main Methods:

  • Utilized a within-case Cohen's d measure specifically adapted for SCED data.
  • Analyzed effect sizes for interventions designed to either increase or decrease specific behaviors.

Main Results:

  • The mean and range of effect sizes were significantly greater for treatments that increased behavior compared to those that decreased behavior.
  • Demonstrated a notable difference in effect size distributions based on the direction of behavioral change targeted by the intervention.

Conclusions:

  • Preliminary findings suggest the need for distinct benchmarks for interpreting low, medium, and high effect sizes in SCED, differentiated by whether the treatment aims to increase or decrease behavior.
  • Emphasizes the importance of developing specific standards for effect size interpretation in SCED meta-analyses to ensure accurate quality control and data inclusion.