The "true" indirect effect won't (always) stand up: When and why reverse mediation testing fails
The "true" indirect effect won't (always) stand up: When and why reverse mediation testing fails
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DOI：
10.1016/j.jesp.2016.05.002
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发表时间：
2017-03-01
影响因子：
3.5
通讯作者：
Gollwitzer, Mario
中科院分区：
文献类型：
--
作者：
Lemmer, Gunnar;Gollwitzer, Mario
许多社会心理学研究旨在测试自变量（X）是否通过一个（或多个）中介变量（M）影响因变量（Y）。测试这种中介模型（X -> M -> Y）的一种方法是操纵X，测量M和Y，并在统计上测试X通过M对Y的间接影响是否与“零”有显著差异。然而，由于M和Y之间的因果顺序不清楚，替代模型（如X -> Y -> M）也与数据兼容。学者们认为，从统计学上比较这些模型可以帮助确定哪个模型是“正确的”。在本文中，我们通过蒙特卡罗模拟来仔细检查这种“反向中介测试”方法的可行性。我们的研究结果表明，反向中介测试往往失败，特别是当中介测量不可靠的因变量。(C)2016 Elsevier Inc. All rights reserved.
Many social psychological studies aim to test whether an independent variable (X) affects a dependent variable (Y) via one (or more) intervening variable(s) or "mediator(s)" (M). One way to test such a mediation model (X -> M -> Y) is to manipulate X, measure both M and Y, and test statistically whether the indirect effect of X on Y via M is significantly different from' zero. However, since the causal order between M and Y is unclear, alternative models (such as X -> Y -> M) are also compatible with the data. Scholars have argued that comparing such models statistically against each other can help decide which model is "correct." In the present article, we scrutinize the tenability of this "reverse mediation testing" approach via Monte Carlo simulations. Our findings show that reverse mediation testing often fails-especially when the mediator is measured less reliably than the dependent variable. (C) 2016 Elsevier Inc. All rights reserved.