Spearman 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
Compute association after ranking, for monotonic relationships and ordinal measurements. Ties receive average ranks. A significant Spearman coefficient does not establish linearity in original units.
Variables
| Control | Input |
|---|---|
| Analysis variables | Select variables |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Missing data in correlations | Complete cases, Pairwise deletion | complete |
| 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.