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Because the sampling distribution of mediation effect is skewed, bootstrap approach has been proved to be more powerful than traditional methods (e.g., Sobel test) and has been widely used in mediation analysis in recent years41,42.
Significant covariance between path a and b coefficients, on the other hand, indicates that the second-level mediation effect is driven by first-level mediation effects which are significant, but differ in their strength and/or direction.
The presentation for the mediation effect is shown in Fig. 5.
According to Kenny [99], a mediation effect is worth considering when ACME > 0.30.
To further gauge this mediation effect, we then ran a bootstrapping analysis (5000 bootstrap samples) and results show that the proposed mediation effect is significant (B = 0.11, SE = 0.06, 95% CI: [0.02 0.24]), thus Hypothesis 2 is supported.
The result shows the moderated mediation effect is significant (95% confidence interval: [−42.13, −1.89]), which supports Hypothesis 3. We then conducted a simple slope test and the results show it is significant (t = 852.7, p < .01).01
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For example, Tan and Perfetti (1997), using a phonologically-mediated priming paradigm in Chinese, demonstrated that the mediation effect was determined by homophone density: the more homophones a Chinese character had, the smaller the mediation priming.
Mediation effect was worked out by structural equation model (SEM).
LIMITATIONS Although the reverse mediation effect was also examined, the present sample was cross-sectional and predominantly male.
The moderated mediation effect was also tested with the same PROCESS program, which can test the conditional indirect effect, including the recommended bootstrapping.
Therefore this mediation effect was examined next.
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