Despite increased attention to the role of statistical power in psychological studies, navigating the process of sample size planning for linear regression designs can be challenging. In particular, it can be difficult to decide upon an appropriate value for the effect size, owing to a variety of factors, including the influence of the correlations among the predictors and between the other predictors and the outcome, in addition to the correlation between the particular predictor(s) in question and the outcome, on statistical power. One approach that addresses these concerns is to use available prior sample information but adjust the sample effect size appropriately for publication bias and/or uncertainty. This article motivates a procedure that accomplishes this, Bias Uncertainty Corrected Sample Size (BUCSS), as a valid approach for linear regression, carefully illustrating how BUCSS may be used in practice. To demonstrate the relevant factors influencing BUCSS performance and ensure it performs well in plausible regression contexts, a Monte Carlo simulation is reported. Importantly, the present difficulties in sample size planning for regression are explained, followed by clear illustrations using BUCSS software for a variety of common practical scenarios in regression studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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使用先验信息来规划适当的回归研究：使用 BUCSS 的教程。

尽管越来越关注统计功效在心理学研究中的作用，但导航线性回归设计的样本量规划过程可能具有挑战性。特别是，由于多种因素，包括预测变量之间以及其他预测变量与结果之间的相关性的影响，以及预测变量之间的相关性，可能难以确定效应大小的适当值。有问题的特定预测因子和结果，关于统计功效。解决这些问题的一种方法是使用可用的先前样本信息，但根据发表偏倚和/或不确定性适当调整样本效应量。本文提出了一个实现这一目标的程序，即偏差不确定性校正样本大小 (BUCSS)，作为线性回归的有效方法，仔细说明如何在实践中使用 BUCSS。为了证明影响 BUCSS 性能的相关因素并确保其在合理的回归环境中表现良好，报告了蒙特卡罗模拟。重要的是，解释了回归样本量规划目前的困难，然后使用 BUCSS 软件对回归研究中的各种常见实际场景进行了清晰的说明。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。解释了目前回归样本量规划的困难，然后使用 BUCSS 软件对回归研究中的各种常见实际场景进行了清晰的说明。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。解释了目前回归样本量规划的困难，然后使用 BUCSS 软件对回归研究中的各种常见实际场景进行了清晰的说明。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。