How to read the results of bootstrap mediation effect test

Interpretation of Bootstrap mediation effect test results

In the Bootstrap mediation effect test, three important result indicators are usually obtained:

1. Mediation Effect size

Mediating effect size indicates the degree of influence of the mediating variable on the relationship between the independent variable and the dependent variable. It is usually expressed as a point estimate (for example, 0.12) and a confidence interval (for example, 0.05-0.19).

2. The significance of the mediating effect

The significance of the mediating effect refers to whether the influence of the mediating variable on the mediating effect is statistically significant. This can be determined by observing whether the confidence interval contains zero. If the confidence interval does not include zero, the mediation effect is considered significant.

3. Mediation proportion

Mediation proportion represents the proportion of the mediating effect size relative to the original relationship between the independent variable and the dependent variable. It is usually expressed as a percentage (for example, 15%).

Interpretation of results

1. Mediating effect size: The size of the mediating effect size indicates that the mediating variable regulates the relationship between the independent variable and the dependent variable role in the relationship. The larger the effect size, the greater the role of the mediating variable.
2. Significance of the mediating effect: A significant mediating effect indicates that the mediating variable has a statistically significant impact on the mediating effect size. If it is not significant, it indicates that the mediating variable may not be an important moderator of the relationship between the independent variable and the dependent variable.
3. Mediation ratio: The mediation ratio provides a quantitative index that can measure the importance of the mediating variable in moderating the relationship. A higher mediation ratio indicates that the mediating variable plays an important role in explaining the relationship between the independent variable and the dependent variable.

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