We examined the degree of autocorrelation among single-case design data with six measures used to estimate treatment effect size. The most commonly used measures of effect size for single-case data over the last 5 years published in peer-reviewed journals were selected for comparison. The overall mean degree of autocorrelation was 0.46 (SD = 0.33) across the 304 data paths, which represents a moderate degree of autocorrelation. Overall, it appears that non-parametric measures of effect size (i.e., percent of non-overlapping data [PND], non-overlap of all pairs [NAP], and improvement rate difference [IRD] values) were substantially and significantly more influenced by the degree of autocorrelation. Tau-U effect size estimate was the non-parametric exception as it was not significantly influenced by the degree of autocorrelation. Parametric measures of effect sizes such as standardized mean difference (SMD) and log response ratio (LRR) values did not appear to be significantly influenced by the degree of autocorrelation. For SMD, LRR, and Tau-U values, the correlation between the effect size value and the degree of autocorrelation was minimal. For NAP, IRD, and PND values, the correlation between the effect size value and the degree of autocorrelation was moderate, indicating that these estimates of effect size should be avoided as the degree of autocorrelation between data points increases.

中文翻译：

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单例实验设计数据的自相关和治疗效果大小的估计

我们使用用于估计治疗效果大小的六项措施检查了单案例设计数据之间的自相关程度。选择了过去 5 年发表在同行评审期刊上的单例数据最常用的效应量指标进行比较。在 304 个数据路径中，自相关的总体平均程度为 0.46 (SD = 0.33)，这表示自相关程度适中。总体而言，效果大小的非参数测量（即，非重叠数据的百分比 [PND]、所有对的非重叠 [NAP] 和改善率差异 [IRD] 值）似乎受到了更大的影响通过自相关程度。Tau-U 效应大小估计是非参数例外，因为它不受自相关程度的显着影响。效应大小的参数测量，例如标准化平均差 (SMD) 和对数响应比 (LRR) 值似乎不受自相关程度的显着影响。对于 SMD、LRR 和 Tau-U 值，效应量值与自相关程度之间的相关性最小。对于 NAP、IRD 和 PND 值，效应量值与自相关程度之间的相关性适中，表明随着数据点之间自相关程度的增加，应避免这些效应量估计值。效应量值与自相关程度之间的相关性最小。对于 NAP、IRD 和 PND 值，效应量值与自相关程度之间的相关性适中，表明随着数据点之间自相关程度的增加，应避免这些效应量估计值。效应量值与自相关程度之间的相关性最小。对于 NAP、IRD 和 PND 值，效应量值与自相关程度之间的相关性适中，表明随着数据点之间自相关程度的增加，应避免这些效应量估计值。