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We developed a Bayesian hierarchical ordinal model to accurately estimate non-overlap indices for single-case designs (SCDs). This statistical advancement improves intervention effect size analysis and supports evidence-based decision-making.

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Area of Science:

  • Behavioral Science
  • Psychometrics
  • Statistical Modeling

Background:

  • Single-case designs (SCDs) are crucial for evaluating interventions.
  • Visual analysis is common, but quantitative effect sizes like non-overlap indices are increasingly used.
  • Statistical underpinnings for non-overlap indices in SCDs require further development.

Purpose of the Study:

  • To introduce a Bayesian hierarchical ordinal model for analyzing SCDs, specifically treatment-reversal designs.
  • To enable accurate estimation of case-specific non-overlap indices.
  • To provide a user-friendly R package (ssrhom) for model implementation.

Main Methods:

  • Developed a Bayesian hierarchical ordinal model.
  • Conducted simulation studies to compare model performance against standard methods.
  • Utilized treatment-reversal designs as a primary focus for analysis.

Main Results:

  • The proposed model yields more accurate case-specific non-overlap indices compared to standard approaches.
  • Parametric indices generated by the model demonstrate superior accuracy.
  • Simulation studies confirm the enhanced precision of the developed statistical indices.

Conclusions:

  • The Bayesian hierarchical ordinal model offers a statistically robust framework for SCD analysis.
  • This approach enhances the interpretation of intervention effectiveness in SCDs.
  • The ssrhom R package facilitates the adoption of advanced quantitative methods in SCD research.