Better power by design: Permuted-subblock randomization boosts power in repeated-measures experiments.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Psychological methods Pub Date : 2024-12-12 DOI:10.1037/met0000717
Jinghui Liang, Dale J Barr
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Abstract

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).

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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改变。这种时间上的变化会影响测量结果,从而降低统计能力。我们引入了一种限制性随机化算法--置换子块随机化(PSR),通过平衡实验过程中的实验条件来提高统计能力。我们使用蒙特卡罗模拟来探索 PSR 在四种随时间变化的误差情况下的性能:指数衰减(学习效应)、高斯随机漫步、粉红噪声以及前三种误差的混合。在一组具有代表性的研究设计中,PSR 平均提高了约 13% 的功率,范围在 4% 到 45% 之间,同时在不存在随时间变化的情况下控制了假阳性率。R软件包 "解释 "提供了在实验计划中实施PSR的功能。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
期刊最新文献
A guided tutorial on linear mixed-effects models for the analysis of accuracies and response times in experiments with fully crossed design. Bayes factors for logistic (mixed-effect) models. Better power by design: Permuted-subblock randomization boosts power in repeated-measures experiments. Building a simpler moderated nonlinear factor analysis model with Markov Chain Monte Carlo estimation. Definition and identification of causal ratio effects.
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