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

Andrew F Hayes¹, Kristopher J Preacher²

¹a School of Communication, The Ohio State University.

Multivariate Behavioral Research

|January 7, 2016

Summary

This study extends statistical methods for indirect effects and mediation analysis, moving beyond linear assumptions. It introduces a new approach for nonlinear models, offering computational tools for researchers.

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Area of Science:

Statistics
Behavioral Science
Causal Inference

Background:

Traditional mediation and indirect effect analyses often assume linear relationships.
Nonlinear relationships are common in behavioral science but challenging to model statistically.

Purpose of the Study:

To extend methods for estimating indirect effects in the presence of nonlinear relationships.
To introduce the concept of the instantaneous indirect effect for nonlinear mediation models.

Main Methods:

Extends Stolzenberg's (1980) method for nonlinear models.
Introduces computation of the instantaneous indirect effect.
Employs a bootstrapping procedure for statistical inference.

Main Results:

Provides a method for estimating indirect effects in models with nonlinear functions (linear in parameters).
Demonstrates the computation and inference for the instantaneous indirect effect.
Offers practical computational tools (Mplus, SPSS, SAS macros).

Conclusions:

The proposed method enhances the analysis of indirect effects in nonlinear mediation models.
Facilitates broader adoption of advanced statistical techniques in behavioral research.
Reduces the computational burden for researchers analyzing complex causal systems.