Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation

TLDR

Mediation of X’s effect on Y through a mediator M is moderated when the indirect effect depends on a fourth variable, and an index-based test for linear moderated mediation was introduced to evaluate such hypotheses. The study extends this index-based approach to models involving more than one moderator. The authors describe how to test whether X’s indirect effect on Y is moderated by one variable while holding a second moderator constant (partial moderated mediation), conditioned on it (conditional moderated mediation), or dependent on it (moderated moderated mediation). Examples, visualizations of indirect effects, and an implementation illustration in the PROCESS macro for SPSS and SAS are provided. Hayes (2015) introduced the approach in *Multivariate Behavioral Research*, 50, 1–22.

Abstract

Mediation of X's effect on Y through a mediator M is moderated if the indirect effect of X depends on a fourth variable. Hayes [(2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50, 1–22. doi:10.1080/00273171.2014.962683] introduced an approach to testing a moderated mediation hypothesis based on an index of moderated mediation. Here, I extend this approach to models with more than one moderator. I describe how to test if X's indirect effect on Y is moderated by one variable when a second moderator is held constant (partial moderated mediation), conditioned on (conditional moderated mediation), or dependent on a second moderator (moderated moderated mediation). Examples are provided, as is a discussion of the visualization of indirect effects and an illustration of implementation in the PROCESS macro for SPSS and SAS.