I have understood the theoretical concepts of moderating and mediating variables. However, the research papers that have tested the roles of such variables are using interaction terms for both moderation and moderation effects.
What is the difference between the statistical models for testing moderating and mediating roles?
A mediation model consists of multiple regression equations (because there are multiple dependent variables) that can be analyzed simultaneously via path analysis. A mediated effect is an indirect variable effect that is formally represented by the product of two or more regression coefficients.
A moderator effect is an interaction effect that is represented by a product term (product of two or more variables) for which a separate regression slope coefficient is estimated. A moderator effect can be analyzed with a single regression equation (unless there are multiple outcome variables).
A source of confusion with mediators and moderators is that mediators cannot be expressed in linear models, where every variable is either a predictor, or a response, but not something in-between. The proper way is to use path analysis (e.g. SEM), where difference is crystal-clear.
Nivaj Gogoi wrote:
However, the research papers that have tested the roles of such variables are using interaction terms for both moderation and moderation effects.
First, I think you meant to say "for both moderation and mediation effects." Is that right? If so, it might help if you showed an example of such a model. Is it, by any chance, a model to show moderated mediation?
A mediator variable explains the relationship between two variables,
while a moderator variable affects the strength or direction of the relationship between two variables. In other words, a mediator variable is part of the causal pathway between two variables, while a moderator variable is not.
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