适度
路径分析(统计学)
调解
结构方程建模
心理学
调解
计量经济学
回归分析
口译(哲学)
路径(计算)
潜变量
多级模型
社会心理学
认知心理学
计算机科学
数学
人工智能
机器学习
程序设计语言
法学
政治学
作者
Jeffrey R. Edwards,Lisa Schurer Lambert
标识
DOI:10.1037/1082-989x.12.1.1
摘要
Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables.
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