What mediation analysis can (not) do
What mediation analysis can (not) do
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DOI：
10.1016/j.jesp.2011.05.007
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发表时间：
2011-11-01
影响因子：
3.5
通讯作者：
Meiser, Thorsten
中科院分区：
文献类型：
--
作者：
Fiedler, Klaus;Schott, Malte;Meiser, Thorsten
本文关注的是统计中介分析解释中的一个常见误解。这些程序可以合理地用来检验第三个变量（Z）在多大程度上解释了自变量（X）对因变量（Y）的影响，条件是假设Z实际上是中介变量。然而，反过来说，一个显著的中介分析结果并不能证明Z是一个中介。这一显而易见但常常被忽视的观点在一项模拟研究中得到了证实。使用不同的因果模型产生Z（真正的调解人，虚假的调解人，相关的依赖措施，操纵检查），它表明，显着的调解测试不允许研究人员确定独特的调解人，或区分替代的因果模型。这一基本的见解，虽然很好地理解了统计学专家，是持续忽视的实证文献和审查过程中，即使是最有选择性的期刊。(C)2011年由Elsevier Inc.出版
The present article is concerned with a common misunderstanding in the interpretation of statistical mediation analyses. These procedures can be sensibly used to examine the degree to which a third variable (Z) accounts for the influence of an independent (X) on a dependent variable (Y) conditional on the assumption that Z actually is a mediator. However, conversely, a significant mediation analysis result does not prove that Z is a mediator. This obvious but often neglected insight is substantiated in a simulation study. Using different causal models for generating Z (genuine mediator, spurious mediator, correlate of the dependent measure, manipulation check) it is shown that significant mediation tests do not allow researchers to identify unique mediators, or to distinguish between alternative causal models. This basic insight, although well understood by experts in statistics, is persistently ignored in the empirical literature and in the reviewing process of even the most selective journals. (C) 2011 Published by Elsevier Inc.