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Related Experiment Video

Updated: Mar 22, 2026

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Comparing Single Case Design Overlap-Based Effect Size Metrics From Studies Examining Speech Generating Device

Mo Chen1, Jolene K Hyppa-Martin2, Joe E Reichle3

  • 1Mo Chen, University of Minnesota, Minneapolis, MN;

American Journal on Intellectual and Developmental Disabilities
|April 28, 2016
PubMed
Summary
This summary is machine-generated.

Synthesizing single case experimental data for individuals with intellectual and developmental disabilities (IDD) is challenging. Percent of All Nonoverlapping Data (PAND), Phi, Improvement Rate Difference (IRD), and Percent of Nonoverlapping Data (PND) effectively quantified intervention effects.

Keywords:
effect size metricevidence-based practicesintellectual and developmental disabilitiessingle case designspeech generating devices

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Area of Science:

  • Behavioral Science
  • Special Education
  • Rehabilitation Medicine

Background:

  • Synthesizing single case experimental (SCE) data for individuals with intellectual and developmental disabilities (IDD) presents unique challenges.
  • Quantifying effect sizes in SCE designs is crucial for evidence-based practice and intervention research.
  • Individuals with moderate to profound IDD often have low-incidence conditions, complicating data synthesis.

Purpose of the Study:

  • To compare the effectiveness of seven effect size metrics for SCE data in IDD research.
  • To identify the most suitable metrics for quantifying intervention effects in this population.
  • To inform best practices for analyzing SCE data in IDD intervention studies.

Main Methods:

  • Compared seven effect size metrics: PEM, PND, IRD, PAND, Phi, NAP, and Tau(novlap).
  • Utilized SCE data focused on teaching speech-generating device use to individuals with IDD.
  • Analyzed a dataset comprising 285 phase or condition contrasts.

Main Results:

  • PAND, Phi, IRD, and PND demonstrated greater effectiveness in quantifying intervention effects.
  • These metrics provided more reliable estimates for the analyzed SCE data.
  • Visual analysis and effect size calculation issues were noted.

Conclusions:

  • PAND, Phi, IRD, and PND are recommended for effect size estimation in IDD intervention research using SCE designs.
  • Further research is needed to refine effect size calculation and interpretation for SCE data in IDD.
  • Improved methods will enhance the synthesis of evidence for individuals with IDD.