25. SEMinR Lecture Series - Mediation Analysis with Multiple Mediators
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Both mediator-specific indirect paths from Vision to organizational performance—via collaborative culture and via commitment—are significant based on T statistics exceeding 1.96.
Briefing
A mediation model with two parallel mediators shows that Vision’s impact on organizational performance runs through both collaborative culture and organizational commitment—and the indirect paths are statistically significant. In the SEMinR setup, Vision is treated as the independent variable, organizational performance as the dependent variable, and the two mediators are collaborative culture and commitment. The analysis first estimates the measurement and structural models, then uses bootstrapping to test the significance of indirect effects for each mediator-specific pathway.
After confirming the model estimates cleanly (all 341 observations valid and bootstrap iterations within range), the total indirect effect from Vision to organizational performance is reported as 0.270. But significance is assessed at the mediator-specific level rather than relying on the total indirect effect alone. Using the specific_effect_significance function with the bootstrapped model, the pathway from Vision → collaborative culture → organizational performance is found significant (T statistic exceeds the 1.96 threshold). The pathway from Vision → commitment → organizational performance is also significant by the same criterion. Taken together, both mediators carry a meaningful portion of Vision’s effect on organizational performance.
The analysis then determines the mediation type by checking whether the direct effect remains significant once mediators are included. The direct effect of Vision on organizational performance is 0.373, and its T statistic is again above 1.96, meaning the direct relationship does not disappear. With significant indirect effects for both mediators and a still-significant direct effect, the model indicates partial mediation.
Finally, the model classifies whether the mediation is complementary or competitive by examining the sign of the combined mediated effects. The workflow multiplies the relevant path coefficients—Vision → collaborative culture → organizational performance and Vision → commitment → organizational performance—using the paths stored inside the summary_simple object. The resulting mediated products are positive, leading to a complementary mediation conclusion for the two-mediator structure.
A path plot and a bootstrap output are generated to visualize the model and the significance results, and the overall procedure is presented as a repeatable SEMinR workflow for multiple mediators: specify formative measurement blocks, define the structural paths, estimate and bootstrap, test mediator-specific indirect effects, evaluate direct effects for partial vs. full mediation, and compute mediated-effect products to label complementary vs. competitive mediation. The practical takeaway is that SEMinR can test not only whether mediation exists, but also which mediator-specific channels matter and how the mediators interact in shaping the dependent variable.
Cornell Notes
The two-mediator SEMinR model tests how Vision affects organizational performance through collaborative culture and organizational commitment. Bootstrapping is used to assess mediator-specific indirect effects: both Vision → collaborative culture → organizational performance and Vision → commitment → organizational performance are significant (T > 1.96). The direct effect of Vision on organizational performance remains significant (0.373 with T > 1.96), so the mediation is partial rather than full. To determine whether the mediators act in a complementary or competitive way, the workflow multiplies the relevant path coefficients; the mediated products are positive, indicating complementary mediation for the two-mediator setup. This matters because it identifies not just mediation presence, but which mediator channels carry the effect and how they combine.
How does the workflow test whether each mediator-specific indirect effect is significant (not just the total indirect effect)?
What evidence supports the claim of partial mediation rather than full mediation?
How does the analysis decide between complementary and competitive mediation?
Why does the model focus on mediator-specific indirect effects instead of relying only on the total indirect effect?
What structural relationships are specified in this two-mediator mediation model?
Review Questions
- What function and inputs are used to test mediator-specific indirect effects in SEMinR, and how is significance determined?
- How would you distinguish partial mediation from full mediation using the direct effect and indirect effects results?
- What sign-based rule does the workflow use to label complementary versus competitive mediation?
Key Points
- 1
Both mediator-specific indirect paths from Vision to organizational performance—via collaborative culture and via commitment—are significant based on T statistics exceeding 1.96.
- 2
The total indirect effect from Vision to organizational performance is 0.270, but significance is confirmed separately for each mediator-specific pathway.
- 3
The direct effect of Vision on organizational performance remains significant (0.373 with T > 1.96), indicating partial mediation.
- 4
Complementary vs. competitive mediation is determined by multiplying the relevant path coefficients; positive products indicate complementary mediation.
- 5
The SEMinR workflow uses bootstrapping to generate the distribution needed for indirect-effect significance testing.
- 6
Model interpretation depends on three layers: mediator-specific indirect effects, the direct effect, and the sign of mediated-effect products.