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Quantifying and Testing Indirect Effects in Simple Mediation Models When the Constituent Paths Are Nonlinear

2010/08/06 by Andrew F. Hayes, Kristopher J. Preacher · 1,134 citations
Mathematics · Psychology · Social Sciences · #Advanced Causal Inference Techniques #Algorithm #Artificial intelligence #Bootstrapping (finance) #Causal inference #Causal model #Computation #Computer science #Cultural Differences and Values #Econometrics #Inference #Linear model #Machine learning #Macro #Mathematics #Mediation #Nonlinear system #Simple (philosophy) #Social and Intergroup Psychology #Statistical hypothesis testing #Statistical inference #Statistics #Structural equation modeling #Theoretical computer science

paper · doi:10.1080/00273171.2010.498290

published in Multivariate Behavioral Research 45(4), 627-660 (Taylor & Francis)

openalex publication_date 2010/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) Stolzenberg, R. M. 1980. The measurement and decomposition of causal effects in nonlinear and nonadditive models.. Sociological Methodology, 11: 459–488. [Crossref] , [Google Scholar] for estimating indirect effects in models of mediators and outcomes that are nonlinear functions but linear in their parameters. We introduce the concept of the instantaneous indirect effect of X on Y through M and illustrate its computation and describe a bootstrapping procedure for inference. Mplus code as well as SPSS and SAS macros are provided to facilitate the adoption of this approach and ease the computational burden on the researcher.

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