Department of Psychology, The Ohio State University.
Psychol Methods. 2017 Mar;22(1):6-27. doi: 10.1037/met0000086. Epub 2016 Jun 30.
Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of 2 different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this article we recast Judd et al.'s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path-analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al.'s method requires, because it relies only on an inference about the product of paths-the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path-analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses. (PsycINFO Database Record
研究人员在测试中介作用时，通常使用设计，让参与者在两种不同情况下测量因变量 Y 和中介 M。在这种设计中，评估中介作用的主流方法是由 Judd、Kenny 和 McClelland（2001）提出的，依赖于一系列关于中介模型组成部分的假设检验，而不是基于对间接效应的估计或正式推断。在本文中，我们将 Judd 等人的方法重新构建为现在在参与者间中介分析中常用的路径分析框架。通过这样做，很明显如何通过影响路径的乘积来估计参与者内操纵对某些结果的中介的间接效应。这种路径分析方法消除了像 Judd 等人的方法那样需要对模型的组成部分进行离散假设检验来支持中介作用的主张，因为它只依赖于对路径乘积的推断——间接效应。我们将在参与者间设计中广泛使用的间接效应推断方法推广到这种参与者内中介分析版本中，包括自举置信区间和蒙特卡罗置信区间。我们使用这种路径分析方法，将该方法扩展到具有多个并行和串行操作的中介的模型中，并讨论在这些更复杂的模型中比较间接效应。我们为 SPSS、SAS 和 Mplus 提供了执行这些分析的宏和代码。
