The purpose of this study was to compare 12 commonly-used nonoverlap methods with each other and with the results of visual analysis. Data were obtained from 25 studies focused on embedded instruction and schema-based instruction and included a total of 101 graphs. Treatment effect estimates using 12 nonoverlap methods were calculated for each graph by hand or using an online calculator. Five experts conducted visual analysis of each graph. Results showed that strong agreements existed between visual analysis and PND, TauNOVLAP and Tau-U when raw data were analysed, and PND, PNCD and PEM-T when categorised data were analysed. Of the 12 methods investigated, PND had the highest agreement rate with visual analysis, followed by PEM-T, PAND, PNCD, IRD, NAP and TauNOVLAP. Overall, visual analysis appeared to be more conservative, as most nonoverlap methods overestimated the treatment effect. Additional research is needed to replicate and potentially validate the findings of this study.

中文翻译：

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在单例实验研究中评估治疗效果估计值：十二种重叠方法的比较和视觉分析

这项研究的目的是将12种常用的非重叠方法彼此之间以及视觉分析的结果进行比较。数据从25个研究中获得，这些研究集中在嵌入式指令和基于模式的指令上，总共包括101张图。手动或使用在线计算器为每张图计算使用12种非重叠方法的治疗效果估计值。五位专家对每个图形进行了视觉分析。结果表明，在分析原始数据时，视觉分析与PND，TauNOVLAP和Tau-U之间存在强烈的一致性，而在对分类数据进行分析时，PND，PNCD和PEM-T之间存在强烈的一致性。在所研究的12种方法中，PND的视觉分析一致性最高，其次是PEM-T，PAND，PNCD，IRD，NAP和TauNOVLAP。总体而言，视觉分析似乎更为保守，因为大多数非重叠方法都高估了治疗效果。需要进行其他研究来复制并可能验证本研究的结果。