I conducted a path analysis including moderated mediation. The model has 2 independent variables, 1 moderator (leading to several interaction effects), 1 mediator and 1 DV:
Dashed lines: effects coming from control variables
Usually, in a mediation model with 1 independent variable, the indirect effect is reported by a*b (parameters in accordance to the example above).
But given that this model contains 2 independent variables and a moderator, I wonder how I can correctly specify the indirect effect.
I have for instance tried the specifications below:
indX1 := b*(a1)
indX2 := b*(a2)
indX1_mod := b*(a1+e1)
indX2_mod := b*(a2+e2)
1a. Could you advise me on how to specify the indirect effects in the aforementioned example please?
1b. In the output that I obtained in R (cfr bottom of this post), there is a significant effect from Me to Y (b), and a significant effect from X1 to Me (a1), and a significant interaction effect e1.
Yet, the indirect effect specified as b*(a1) or b*(a1+e1) is not significant. Assuming that either b*(a1) or b*(a1+e1) is correct to represent the indirect effect, what could be the reason for the insignificance of the indirect effect? And does this undermine the "mediating story", or can I base myself on the regression coefficients and their corresponding p-values to justify my story?
Plotting interaction effect
When researchers execute a linear regression model with an interaction effect, it seems common practice to plot that interaction effect.
It would be interesting if I could visualise the model's interaction effects as below (graphs are based on linear model investigating the effect of X1 and X2 on Me, conditional on Mo):
2a. Is it appropriate to plot an interaction effect coming from a sem model?
2b. If yes, how could I retrieve the necessary information from the model to establish these graphs?
I tried filling in the regression Me ~ r1*C1 + s1*C2 + t1*C3 + u1*C4 + a1*X1 + a2*X2 + d*Mo + e1*X1xMo + e2*X2xMo, but I'm not convinced that this was correct.
For example: for low X1 and Mo=1, I calculated Me ~ r1*0 + s1*0 + t1*0 + u1*0 + a1*(averageX1-2*SD) + a2*0 + d*1 + e1*(averageX1-2*SD)*1 + e2*0
But the resulting score for Me seems of a weird magnitude to me. I notice that the regression does not include an intercept (no idea if there is one and if so, where to find it), which is different from a "normal" linear regression. Not sure if this would clarify the strange outcome, and how to solve this issue?
Thanks in advance for the help,
Note: I used estimator = "MLR" to account for multivariate non-normality