Department of Educational, School, and Counseling Psychology, University of Missouri, Columbia, MO, USA.
Behav Res Methods. 2020 Feb;52(1):342-359. doi: 10.3758/s13428-019-01230-4.
It is well-known that the identification of direct and indirect effects in mediation analysis requires strong unconfoundedness assumptions. Even when the predictor is under experimental control, unconfoundedness assumptions must be imposed on the mediator-outcome relation in order to guarantee valid indirect-effect identification. Researchers are therefore advised to test for unconfoundedness when estimating mediation effects. Significance tests to evaluate unconfoundedness usually rely on an instrumental variable (IV)-that is, a variable that is nonindependent of the explanatory variable and, at the same time, independent of all exogenous factors that affect the outcome when the explanatory variable is held constant. Because IVs may be hard to come by, the present study shows that confounders of the mediator-outcome relation can be detected without making use of IVs when variables are nonnormal. We show that kernel-based tests of independence are able to detect confounding under nonnormality. Results from a simulation study are presented that suggest that these tests perform well in terms of Type I error protection and statistical power, independent of the distribution or measurement level of the confounder. A real-world data example from the Job Search Intervention Study (JOBS II) illustrates how the presented approach can be used to minimize the risk of obtaining biased indirect-effect estimates. The data requirements and role of unconfoundedness tests as diagnostic tools are discussed. A Monte Carlo-based power analysis tool for sample size planning is also provided.
众所周知，中介分析中直接效应和间接效应的识别需要严格的无混杂假设。即使预测变量受到实验控制，为了保证有效识别间接效应，也必须对中介-结果关系施加无混杂假设。因此，研究人员在估计中介效应时应进行无混杂性检验。评估无混杂性的显著性检验通常依赖于工具变量（IV），即与解释变量不独立但同时与当解释变量保持恒定时影响结果的所有外生因素独立的变量。由于 IV 可能难以获得，本研究表明，当变量是非正态分布时，可以在不使用 IV 的情况下检测到中介-结果关系的混杂因素。我们表明，基于核的独立性检验能够在非正态性下检测到混杂。模拟研究的结果表明，这些检验在保护Ⅰ类错误和统计功效方面表现良好，与混杂因素的分布或测量水平无关。来自求职干预研究（JOBS II）的一个真实世界数据示例说明了如何使用所提出的方法来最小化获得有偏差的间接效应估计的风险。讨论了数据要求和无混杂性检验作为诊断工具的作用。还提供了一个基于蒙特卡罗的样本量规划功效分析工具。
