Dear all,
I need some advice for my mediation analysis. In my research model, there are 2 IVs, 2DVs, and 1 mediator.
To conduct the analysis, I perform the following steps:
1. Build the inner model by putting all the variables into the diagram.
2. Build the outer model by putting all indicators of each variable.
3. Connect all variables by drawing the arrows from 2 IVs --> mediator, and from mediator --> 2DVs. No arrow from IVs --> DVs.
4. Run the path-modeling estimation by selecting “PLS Algorithm".
5. Check outer model loadings and significance, indicator reliability, internal consistency reliability, convergent validity, and discriminant validity. The results show that everything is OK.
6. Check structural path significance in bootstrapping. The results show that all paths are significant.
7. I conclude that the model is correct, the mediator mediates the effects of all IVs on DVs.
The total indirect effect between IVs and DVs is significant in results, but when I connect IVs with DVs to check the association between them, the path coefficients between IV and DV becomes insignificant.
My questions are:
Q1: I want to know the concept behind these results, why there is difference in the total indirect effect results (without connecting IV to DV) and path coefficient results (with connecting IV to DV), as both represents same relationship.
Q2: Is it appropriate to consider IV to DV (total indirect effect results) without connecting IV to DV for exploring relationship between IV to DV in a mediation model.
Your advice and suggestions are greatly appreciated.
Thank you very much.
Mediation analysis (Total indirect effects)
Re: Mediation analysis (Total indirect effects)
1. Concept Behind Total Indirect Effect vs. Path Coefficient:
In mediation analysis, we often encounter three paths:
Path c: This represents the total effect of the independent variable (IV) on the dependent variable (DV). It includes both the direct effect (c’) and the indirect effect (ab).
Path c’: This is the direct effect of the IV on the DV, controlling for the mediator (M).
Path ab: This is the indirect effect, which operates through the mediator M.
The equation c = c’ + ab holds true when:
a) We use multiple regression or structural equation modeling without latent variables.
b) The same cases are used in all analyses.
c) The same covariates are included in all equations.
However, this equality is only approximate for multilevel models, logistic analysis, and structural equation modeling with latent variables.
smash karts
2. Exploring IV to DV Relationship:
The total indirect effect (IV to DV via the mediator) is essential for understanding the overall impact of the IV on the DV, considering both direct and indirect pathways.
However, when you connect the IV directly to the DV (i.e., examine the path coefficient), you’re specifically assessing the direct relationship between them.
If the path coefficients become insignificant when connecting IV to DV, it suggests that the direct effect (c’) is not significant after accounting for the mediator.
In some cases, the mediator may fully explain the relationship between IV and DV, rendering the direct path non-significant.
In mediation analysis, we often encounter three paths:
Path c: This represents the total effect of the independent variable (IV) on the dependent variable (DV). It includes both the direct effect (c’) and the indirect effect (ab).
Path c’: This is the direct effect of the IV on the DV, controlling for the mediator (M).
Path ab: This is the indirect effect, which operates through the mediator M.
The equation c = c’ + ab holds true when:
a) We use multiple regression or structural equation modeling without latent variables.
b) The same cases are used in all analyses.
c) The same covariates are included in all equations.
However, this equality is only approximate for multilevel models, logistic analysis, and structural equation modeling with latent variables.
smash karts
2. Exploring IV to DV Relationship:
The total indirect effect (IV to DV via the mediator) is essential for understanding the overall impact of the IV on the DV, considering both direct and indirect pathways.
However, when you connect the IV directly to the DV (i.e., examine the path coefficient), you’re specifically assessing the direct relationship between them.
If the path coefficients become insignificant when connecting IV to DV, it suggests that the direct effect (c’) is not significant after accounting for the mediator.
In some cases, the mediator may fully explain the relationship between IV and DV, rendering the direct path non-significant.