How to read the results of bootstrap mediation test

Interpretation of Bootstrap mediation test results

Bootstrap mediation test is a statistical method used to evaluate the significance of the mediation effect in the mediation model. By resampling the original data multiple times and calculating mediation effects, this method can provide a more powerful test of significance.

Interpreting the results

The output of the Bootstrap mediation test contains the following key information:

1. Confidence interval for the indirect effect:

This is the range of estimates of the mediation effect, represented by the upper and lower bounds of the confidence interval. If the confidence interval does not include zero, the mediation effect is statistically significant.

2. p-value:

This is the probability that the confidence interval does not contain zero. Generally, a p-value less than 0.05 is considered statistically significant.

3. Sample size:

This is the number of data samples used for bootstrap analysis.

4. Bootstrap subsampling times:

This is the number of repeated samplings, usually between 500 and 2000 times.

Conclusion

If the confidence interval of the mediation effect does not include zero and the p-value is less than 0.05, the mediation effect is statistically significant. This means that the mediating variable plays a role in explaining the relationship between the independent variable and the dependent variable.

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