Simple mediation
Purpose
Decompose the X–Y association into total, direct and mediator-specific indirect effects.
Data and assumptions
This release uses OLS for continuous observed variables. All equations use common complete cases and categorical coding. Bootstrap resamples entire cases. Covariates require substantive justification.
Configure this method
Specify one X, M and Y. The indirect effect is a times b. Analysis settings optionally show path regressions and allow 200–5000 bootstrap samples with a fixed seed.
Variables
| Control | Input |
|---|---|
| Outcome Y | Select one variable |
| Predictors X | Select variables |
| Mediator Variable | Select variables |
| Control Variables | Select variables |
| Treat as categorical | Select variables |
| Reference categories | Choose categorical reference levels |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Bootstrap replications | Set as needed | 1000 |
| Random Seed | Set as needed | 20260926 |
| Include path regression tables | Set as needed | Disabled |
Analysis parameters remain visible in the main configuration area. Advanced formatting contains only the table title and numeric decimals. Methods that calculate intervals also show the significance level; 0.05 corresponds to 95% intervals.
Read the results
Focus on percentile bootstrap intervals for each indirect effect and whether they include zero. A significant total effect is not a prerequisite. Cross-sectional associations alone cannot establish temporal order or a causal mechanism.
Workflow and exports
- Upload the dataset and choose the method from the sidebar or search.
- Select the variables above, set the design-specific options, then run the analysis.
- Inspect the paper-style result. Reconfigure from the result area, or open raw output for diagnostics and sample details.
- Rename and keep result tabs as needed. Export Word, Excel, CSV or TXT; batch export can include the selected AI interpretation. Word retains figures. AI text should be checked against the estimates.