In psychology, the causal process between 2 variables can be studied with statistical mediation analysis. To make a causal interpretation about the relation between variables, researchers who use the statistical mediation model make many assumptions about the variables in the model, among which are measurement assumptions about the mediator. For example, researchers often assume that the measure of the mediator yields scores that are reliable and that have a valid interpretation. In this article, we address how several measurement challenges affect the conclusions of statistical mediation analysis, and how researchers can use different psychometric models to study theoretically different causal processes. We use simulated data sets to illustrate how 10 well-fitting and theoretically sound statistical mediation models could significantly detect the indirect effect or miss it entirely depending on how the mediator is represented in the model. In the example, power to detect the indirect effect varied by the amount of true mediator variance that the psychometric model of the mediator was able to isolate. Different strategies to incorporate psychometric methods into mediation research are discussed and future directions are considered. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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中介变量的测量及其对统计中介结论的影响。

在心理学中，两个变量之间的因果过程可以通过统计中介分析来研究。为了对变量之间的关系做出因果解释，使用统计中介模型的研究者对模型中的变量做出了许多假设，其中包括对中介变量的测量假设。例如，研究人员经常假设中介变量的测量产生的分数是可靠的并且具有有效的解释。在本文中，我们讨论了一些测量挑战如何影响统计中介分析的结论，以及研究人员如何使用不同的心理测量模型来研究理论上不同的因果过程。我们使用模拟数据集来说明 10 个拟合良好且理论上合理的统计中介模型如何能够显着检测间接效应或完全忽略间接效应，具体取决于中介在模型中的表示方式。在该示例中，检测间接效应的能力随中介者的心理测量模型能够分离的真实中介者方差的量而变化。讨论了将心理测量方法纳入调解研究的不同策略，并考虑了未来的方向。（PsycINFO 数据库记录 (c) 2020 APA，保留所有权利）。