Tibbe Tristan D, Montoya Amanda K
Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
Front Psychol. 2022 May 27;13:810258. doi: 10.3389/fpsyg.2022.810258. eCollection 2022.
The bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for conducting inference on the indirect effect in mediation analysis due to its high power in small samples, but now it is criticized by methodologists for its inflated type I error rates. In its place, the percentile bootstrap confidence interval (PBCI), which does not adjust for bias, is currently the recommended inferential method for indirect effects. This study proposes two alternative bias-corrected bootstrap methods for creating confidence intervals around the indirect effect: one originally used by Stine (1989) with the correlation coefficient, and a novel method that implements a reduced version of the BCBCI's bias correction. Using a Monte Carlo simulation, these methods were compared to the BCBCI, PBCI, and Chen and Fritz (2021)'s 30% Winsorized BCBCI. The results showed that the methods perform on a continuum, where the BCBCI has the best balance (i.e., having closest to an equal proportion of CIs falling above and below the true effect), highest power, and highest type I error rate; the PBCI has the worst balance, lowest power, and lowest type I error rate; and the alternative bias-corrected methods fall between these two methods on all three performance criteria. An extension of the original simulation that compared the bias-corrected methods to the PBCI after controlling for type I error rate inflation suggests that the increased power of these methods might only be due to their higher type I error rates. Thus, if control over the type I error rate is desired, the PBCI is still the recommended method for use with the indirect effect. Future research should examine the performance of these methods in the presence of missing data, confounding variables, and other real-world complications to enhance the generalizability of these results.
偏差校正自助置信区间（BCBCI）曾是中介分析中对间接效应进行推断的首选方法，因为它在小样本中具有较高的功效，但现在受到方法论者的批评，因为其I型错误率过高。取而代之的是，不进行偏差校正的百分位数自助置信区间（PBCI）目前是间接效应的推荐推断方法。本研究提出了两种替代的偏差校正自助方法，用于围绕间接效应创建置信区间：一种是Stine（1989）最初使用的相关系数法，以及一种实现BCBCI偏差校正简化版本的新方法。通过蒙特卡罗模拟，将这些方法与BCBCI、PBCI以及Chen和Fritz（2021）的30% Winsorized BCBCI进行了比较。结果表明，这些方法在一个连续体上表现，其中BCBCI具有最佳平衡（即落在真实效应之上和之下的置信区间比例最接近相等）、最高功效和最高I型错误率；PBCI具有最差平衡、最低功效和最低I型错误率；替代的偏差校正方法在所有三个性能标准上介于这两种方法之间。在控制I型错误率膨胀后，将偏差校正方法与PBCI进行比较的原始模拟的扩展表明，这些方法功效的提高可能仅归因于其较高的I型错误率。因此，如果希望控制I型错误率，PBCI仍然是用于间接效应的推荐方法。未来的研究应该考察这些方法在存在缺失数据、混杂变量和其他现实世界复杂性情况下的性能，以提高这些结果的可推广性。
