Behavior analysts commonly use visual inspection to analyze single-case graphs, but studies on its reliability have produced mixed results. To examine this issue, we compared the Type I error rate and power of visual inspection with a novel approach—machine learning. Five expert visual raters analyzed 1,024 simulated AB graphs, which differed on number of points per phase, autocorrelation, trend, variability, and effect size. The ratings were compared to those obtained by the conservative dual-criteria method and two models derived from machine learning. On average, visual raters agreed with each other on only 75% of graphs. In contrast, both models derived from machine learning showed the best balance between Type I error rate and power while producing more consistent results across different graph characteristics. The results suggest that machine learning may support researchers and practitioners in making fewer errors when analyzing single-case graphs, but replications remain necessary.

中文翻译：

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机器学习分析单例图：与目视检查的比较

行为分析师通常使用目视检查来分析单个案例图，但对其可靠性的研究产生了不同的结果。为了研究这个问题，我们将第一类错误率和视觉检查的能力与一种新颖的方法——机器学习进行了比较。五位视觉评估专家分析了 1,024 个模拟 AB 图形，这些图形在每个阶段的点数、自相关、趋势、可变性和效应大小方面有所不同。将评级与通过保守的双重标准方法和机器学习衍生的两个模型获得的评级进行比较。平均而言，视觉评估员仅在 75% 的图表上达成一致。相比之下，源自机器学习的两种模型都显示了 I 类错误率和功率之间的最佳平衡，同时在不同的图形特征上产生了更一致的结果。