This paper intends to remind communication scientists that the indirect effect as estimated in mediation analyses is a statistical synonym for omitted variable bias (i.e. confounding or suppression). This simple fact questions the interpretability of statistically significant ‘indirect effects’ when using observational data: in social reality, all variables correlate with each other to some extent – the so-called ‘crud factor’ – which means that omitted variable bias and ‘indirect effects’ at the population level are virtually guaranteed regardless of the actual variables involved in the statistical mediation model. As a result, there can be no inferential link between the observation of a significant indirect effect and a theoretical claim of mediation. Through this argument, the paper hopes to add to the existing warnings on mediation analyses and cultivate a more critical interpretation of ‘indirect effects’ in communication science.

本文旨在提醒传播科学家，在中介分析中估计的间接影响是遗漏变量偏差（即混杂或抑制）的统计同义词。这个简单的事实质疑使用观察数据时具有统计意义的“间接影响”的可解释性：在社会现实中，所有变量在某种程度上都相互关联——所谓的“粗略因素”——这意味着忽略变量偏差和“间接影响”无论统计中介模型中涉及的实际变量如何，实际上都可以保证总体水平的影响。因此，在对显着间接影响的观察与中介的理论主张之间不可能存在推论联系。通过这个论证，