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Summary

The study found that improving the convergence of the gAB effect size, particularly by increasing the number of cases (m), enhances its accuracy in single-case designs. Optimal case size depends on data distribution and reliability.

Keywords:
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Area of Science:

  • Single-case experimental designs
  • Effect size estimation
  • Intervention research methodology

Background:

  • The gAB effect size is recommended for single-case studies and meta-analyses.
  • Limited research exists on gAB's non-convergence and how to improve it.

Purpose of the Study:

  • Investigate the impact of case size (m) and measurement size (N) on gAB non-convergence and performance.
  • Examine factors like data distribution, autocorrelation, reliability, and variance components.

Main Methods:

  • Expanded on previous work by Pustejovsky et al. and Chen et al.
  • Simulated a wide range of m and N values.
  • Assessed gAB performance using relative bias, variance bias, and CI coverage rates.

Main Results:

  • gAB performance improved with convergence, especially for non-normal data.
  • Convergence increased with larger m, higher within-case reliability, and greater variance component ratios.
  • Optimal m varied based on data distribution and reliability; N had minimal impact.

Conclusions:

  • gAB convergence is crucial for accurate effect size estimation in single-case designs.
  • Selecting an optimal number of cases (m) is vital for improving gAB application.
  • Findings highlight the importance of methodological considerations for robust intervention effect assessment.