Mediation analysis revisited again. Issue 1 (February 2019)

• Record Type: Journal Article
• Authors: Thoemmes, Felix; Lemmer, Gunnar
• Is Part Of: Australasian marketing journal. Volume 27:Issue 1(2019)
• Page Start: 52
• Page End: 56
• Publication Date: 2019-02
• Subjects: Mediation -- Causality -- Confounding -- Equivalent models; Marketing -- Australasia -- Periodicals; Marketing -- Periodicals; 658.8480905
• DOI: 10.1016/j.ausmj.2018.10.011
• Languages: English
• ISSNs: 1441-3582

Abstract

Highlights: Mediation models are inherently causal models that posit structural relationships among variables. Necessary causal assumptions are often not met, and sensitivity analysis is warranted. Reversing arrows to inspect equivalent models is not a sound strategy for model selection. Abstract: In a mediation model the effect of a dependent variable (DV) on an outcome is (partially) due to the DV's effect on one or multiple mediator(s) that consequently have an effect on the outcome. The use of such models as the theoretical background guiding empirical studies is widespread. Mediation models are fundamentally causal models that specify causal sequences. Unfortunately, the necessary causal assumptions are in practice often violated. In the current paper, we discuss possible improvements of causal mediation analyses, and highlight some potential pitfalls. We discuss the benefits gained by analyzing indirect effect between latent variables specified with measurement models. The validity of statistical findings can also be improved by using experimental designs. We discuss the cross-over design and the cross-over encouragement design, and how they can help improve causal conclusions. We also discuss recent advances on sensitivity analyses in the context of mediation models. Specifically, we explain how this analysis can be used to argue for the severity of unobserved confounding. Lastly, we discuss the practice of reversing the direction of the arrow between variables in a mediation model. We argue that if such reversals result in equivalent models, this practice cannot be recommended. Chinese Abstract: 在中介模型中, 因变量对结果的影响是(部分)由于因变量对一个或多个中介变量的影响, 从而对结果产生影响. 这类模型作为指导实证研究的理论背景被广泛使用. 中介模型根本上是具体指定因果顺序的因果模型. 不幸的是, 在实际操作中必要的因果假设经常被违反. 在这篇文章中, 我们讨论了针对因果中介模型合理的改善, 并强调了一些潜在的问题. 我们讨论了通过分析用测量模型指定的潜在变量中的间接影响而获得的益处. 统计结果的有效性也可以通过实验设计得以提高. 我们讨论了交叉设计和交叉激励设计, 以及它们如何有助于改善因果结论. 我们也讨论了在中介模型环境中关于敏感性分析法的最新进展. 具体地说, 我们解释了该分析如何论证了未观察到的混淆的严重性. 最后, 我们讨论了在中介模型中颠倒变量之间的箭头方向的实践操作. 我们认为, 如果这种逆转导致等效模型, 则不推荐这种做法.