Visual analysis and nonoverlap-based effect sizes are predominantly used in analyzing single case experimental designs (SCEDs). Although they are popular analytical methods for SCEDs, they have certain limitations. In this study, a new effect size calculation model for SCEDs, named performance criteria-based effect size (PCES), is proposed considering the limitations of 4 nonoverlap-based effect size measures, widely accepted in the literature and that blend well with visual analysis. In the field test of PCES, actual data from published studies were utilized, and the relations between PCES, visual analysis, and the 4 nonoverlap-based methods were examined. In determining the data to be used in the field test, 1,052 tiers (AB phases) were identified from 6 journals. The results revealed a weak or moderate relation between PCES and nonoverlap-based methods due to its focus on performance criteria. Although PCES has some weaknesses, it promises to eliminate the causes that may create issues in nonoverlap-based methods, using quantitative data to determine socially important changes in behavior and to complement visual analysis.

中文翻译：

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单例实验设计的基于性能标准的效应量 (PCES) 测量：真实世界数据研究

视觉分析和基于非重叠的效应量主要用于分析单一案例实验设计 (SCED)。虽然它们是 SCED 的流行分析方法，但它们有一定的局限性。在本研究中，考虑到 4 种基于非重叠的效应量测量的局限性，提出了一种新的 SCED 效应量计算模型，称为基于性能标准的效应量 (PCES)，在文献中被广泛接受并且与视觉分析很好地融合. 在 PCES 的现场测试中，利用了已发表研究的实际数据，并检查了 PCES、视觉分析和 4 种基于非重叠的方法之间的关系。在确定用于现场测试的数据时，从 6 种期刊中确定了 1,052 层（AB 阶段）。结果表明，PCES 与基于非重叠的方法之间存在弱或中等的关系，因为它侧重于性能标准。尽管 PCES 有一些弱点，但它有望消除可能在基于非重叠的方法中产生问题的原因，使用定量数据来确定行为的社会重要变化并补充视觉分析。