Partial correlation
Purpose
Quantify the direction and strength of association between variables.
Data and assumptions
Check outliers and relationship shape. Complete-case analysis uses one sample throughout the matrix; pairwise deletion can use different samples for different entries.
Configure this method
Add numerical control variables to the target variables. This is Pearson partial correlation of linear residuals using complete cases across targets and controls, not rank partial correlation.
Variables
| Control | Input |
|---|---|
| Analysis variables | Select variables |
| Control Variables | Select variables |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Include descriptives in the correlation matrix | Set as needed | Enabled |
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
A coefficient near zero does not rule out every relationship. Stars denote test thresholds, not effect size. Correlation alone does not establish causation. Pair-specific N and p are retained in raw output.
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.