BACKGROUND Although average-based effect size (ES) and percentage of individual changes (PIC) are quite different, they are not independent: larger ESs lead to higher PICs. However, this association has not been sufficiently explored. METHOD We analyzed this association based on data simulated in the context of a pre-post design, with and without control groups. We simulated various distributions, sample sizes, degrees of test-retest reliability, effect sizes, and different variances in pre- and post-test. RESULTS The PIC is closely associated with the ES across a wide variety of empirically frequent scenarios. In the "single group pre-post designs", the linear regression model shows R2 values above 0.90. In the "control group pre-post designs", the linear regression model shows R2 values above 0.80. These results were found even when the post-test variability differed from that of the pre-test, replicating, extending and generalizing the findings in previous studies. CONCLUSIONS (1) In the absence of information about the PIC, the ES may be used to estimate this percentage. (2) The PIC is useful in interpreting the meaning of ES measures.

中文翻译：

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分布中心和个体分数的变化：与异方差的前测和后测分布的关系。

背景虽然基于平均值的效应量 (ES) 和个体变化百分比 (PIC) 有很大不同，但它们并不是独立的：更大的 ES 导致更高的 PIC。然而，这种关联尚未得到充分探索。方法我们根据在前后设计背景下模拟的数据分析了这种关联，有对照组和没有对照组。我们模拟了各种分布、样本大小、重测可靠性程度、效果大小以及前后测试中的不同方差。结果 在各种经验频繁的场景中，PIC 与 ES 密切相关。在“单组前后设计”中，线性回归模型显示 R2 值高于 0.90。在“对照组前后设计”中，线性回归模型显示 R2 值高于 0.80。即使后测变异性与前测变异性不同，这些结果也被发现，复制、扩展和概括以前研究中的发现。结论 (1) 在没有关于 PIC 的信息的情况下，可以使用 ES 来估计这个百分比。(2) PIC 有助于解释 ES 措施的含义。