During an experimental session, participants adapt and change due to learning, fatigue, fluctuations in attention, or other physiological or environmental changes. This temporal variation affects measurement, potentially reducing statistical power. We introduce a restricted randomization algorithm, permuted-subblock randomization (PSR), that boosts power by balancing experimental conditions over the course of an experimental session. We used Monte Carlo simulations to explore the performance of PSR across four scenarios of time-dependent error: exponential decay (learning effect), Gaussian random walk, pink noise, and a mixture of the previous three. PSR boosted power by about 13% on average, with a range from 4% to 45% across a representative set of study designs, while simultaneously controlling the false positive rate when time-dependent variation was absent. An R package, explan, provides functions to implement PSR during experiment planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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更好的设计功效：置换子区组随机化提高了重复测量实验的功效。


在实验过程中，参与者会因学习、疲劳、注意力波动或其他生理或环境变化而适应和变化。这种时间变化会影响测量，可能会降低统计功效。我们引入了一种受限随机化算法，即排列子块随机化 （PSR），它通过在实验过程中平衡实验条件来提高功效。我们使用蒙特卡洛模拟来探索 PSR 在四种时间相关误差场景中的性能：指数衰减（学习效应）、高斯随机游走、粉红噪声以及前三种情况的混合。PSR 平均将功效提高了约 13%，在一组代表性的研究设计中，功效范围为 4% 至 45%，同时在没有时间依赖性变化时控制假阳性率。R 软件包 exexplain 提供了在实验规划期间实现 PSR 的函数。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。