How to define and test an Indirect Moderation model: the missing link in regression-based path models

TL;DR

This paper introduces a novel 'indirect moderation' model that combines moderation and mediation effects, providing a structured regression-based approach to test whether a moderating effect is mediated by another variable, with demonstrated simulation and empirical results.

Contribution

It presents the first formal definition and testing procedure for indirect moderation, filling a gap in regression-based path models.

Findings

The decision tree effectively identifies indirect moderation effects.

Simulation shows reliable performance under various scenarios.

Empirical analysis confirms the model's practical utility.

Abstract

Two of the most important extensions of the basic regression model are moderated effects (due to interactions) and mediated effects (i.e. indirect effects). Combinations of these effects may also be present. In this work, an important, yet missing combination is presented that can determine whether a moderating effect itself is mediated by another variable. This 'indirect moderation' model can be assessed by a four-step decision tree which guides the user through the necessary regression analyses in order to infer or refute indirect moderation. A simple simulation experiment shows how the method performs under several basic scenarios. Analysis of an empirical data set shows that the indirect moderation model is extremely valuable in applied research.