Sample Size Justification
Sample Size Justification
复制标题
DOI：
10.1525/collabra.33267
复制
发表时间：
2022-03-22
影响因子：
2.5
通讯作者：
Lakens, Daniel
中科院分区：
文献类型：
--
作者：
Lakens, Daniel
设计实证研究时的一个重要步骤是证明将收集的样本量。此类研究样本量合理性的主要目的是解释在给定研究者的推理目标的情况下，期望收集的数据如何提供有价值的信息。在这篇综述文章中，讨论了六种方法来证明定量实证研究中的样本量：1）从（几乎）整个人群中收集数据，2）根据资源限制选择样本量，3）进行先验功效分析，4）规划所需的准确性，5）使用统计学，或6）明确承认缺乏理由。在证明样本量时要考虑的一个重要问题是哪些效应量被认为是有趣的，以及收集的数据在多大程度上为关于这些效应量的推断提供了信息。根据选择的样本量理由，研究人员可以考虑1）感兴趣的最小效应量是什么，2）最小效应量将具有统计学显著性，3）他们期望的效应量（以及他们基于什么来进行这些预期），4）基于效应量周围的置信区间，哪些效应量将被拒绝，5）基于敏感性功效分析，研究具有足够功效检测的效应范围，以及6）在特定研究领域中预期的效应大小。研究人员可以使用本文中提供的指南，例如通过使用随附的在线Shiny应用程序中的交互式表单，来改进他们的样本量合理性，并希望将研究的信息价值与他们的推理目标相一致。
An important step when designing an empirical study is to justify the sample size that will be collected. The key aim of a sample size justification for such studies is to explain how the collected data is expected to provide valuable information given the inferential goals of the researcher. In this overview article six approaches are discussed to justify the sample size in a quantitative empirical study: 1) collecting data from (almost) the entire population, 2) choosing a sample size based on resource constraints, 3) performing an a-priori power analysis, 4) planning for a desired accuracy, 5) using heuristics, or 6) explicitly acknowledging the absence of a justification. An important question to consider when justifying sample sizes is which effect sizes are deemed interesting, and the extent to which the data that is collected informs inferences about these effect sizes. Depending on the sample size justification chosen, researchers could consider 1) what the smallest effect size of interest is, 2) which minimal effect size will be statistically significant, 3) which effect sizes they expect (and what they base these expectations on), 4) which effect sizes would be rejected based on a confidence interval around the effect size, 5) which ranges of effects a study has sufficient power to detect based on a sensitivity power analysis, and 6) which effect sizes are expected in a specific research area. Researchers can use the guidelines presented in this article, for example by using the interactive form in the accompanying online Shiny app, to improve their sample size justification, and hopefully, align the informational value of a study with their inferential goals.