Mediation analysis is central to theory building and testing in organizational sciences. Scholars often use linear regression analysis based on normal-theory maximum likelihood estimators to test mediation. However, these estimators are very sensitive to deviations from normality assumptions, such as outliers, heavy tails, or skewness of the observed distribution. This sensitivity seriously threatens the empirical testing of theory about mediation mechanisms. To overcome this threat, we develop a robust mediation method that yields reliable results even when the data deviate from normality assumptions. We demonstrate the mechanics of our proposed method in an illustrative case, while simulation studies show that our method is both superior in estimating the effect size and more reliable in assessing its significance than the existing methods. Furthermore, we provide freely available software in R and SPSS to enhance its accessibility and adoption by empirical researchers.

中介分析对于组织科学中的理论构建和测试至关重要。学者们经常使用基于正态理论最大似然估计量的线性回归分析来检验调解。但是，这些估计量对于与正态性假设的偏差非常敏感，例如离群值，粗尾或观察分布的偏斜度。这种敏感性严重威胁着有关调解机制理论的实证检验。为了克服这种威胁，我们开发了一种鲁棒的调解方法，即使数据偏离正常性假设，该调解方法也可以产生可靠的结果。我们在一个示例性案例中演示了我们提出的方法的机制，而仿真研究表明，与现有方法相比，我们的方法在估计效果大小方面更为出色，并且在评估其重要性方面更可靠。