gAB is a design-comparable effect size that has been recommended by the What Works Clearinghouse since 2020 to assess an intervention effect in single-case studies and for meta-analyses. Yet, no research has systematically studied how gAB’s performance could be impacted by its non-convergence, and how gAB’s non-convergence and performance could be improved by increasing the case size (m) and measurement size (N). This study expanded on the work of Pustejovsky et al. (Journal of Educational and Behavioral Statistics, 39(5), 368–393, 2014) and Chen et al. (Behavioral Research Methods, 56, 379–405, 2024) to investigate the impact of a wide range of m and N, data distribution, autocorrelation, within-case reliability, and ratio of variance components on gAB’s non-convergence rate and performance in multiple-baseline designs. gAB’s performance was assessed by relative bias, relative bias of variance, and coverage rate of 95% symmetric CIs. Findings revealed that gAB’s performance was improved by convergence, especially when data were non-normal. In addition, gAB’s convergence improved by increasing m, within-case reliability, and ratio of variance components. When data distribution was normal, converged gAB improved with large m and large within-case reliability. When data distribution was mildly non-normal, converged gAB improved with medium to large m and small within-case reliability. When data distribution was moderately non-normal, converged gAB improved with small to medium m and small within-case reliability. Optimal m depended on data distribution and within-case reliability. N had a trivial impact on converged gAB. In sum, our findings demonstrated the importance of gAB’s convergence and optimal m to improve its application.
