Principal component analysis
Purpose
Summarize shared structure among correlated measures or reduce their dimensionality.
Data and assumptions
Use complete numerical cases, without constant or linearly dependent items. Set the number manually, or use 0 for the eigenvalue-greater-than-one aid and review it against theory and the scree plot.
Configure this method
PCA decomposes total variance to summarize the original measures with fewer components. It is not common-factor extraction, and components do not automatically represent latent constructs. Rotation and component-count settings remain available.
Variables
| Control | Input |
|---|---|
| Analysis variables | Select variables |
Analysis settings
| Parameter | Choices | Default |
|---|---|---|
| Factors or components (0 for suggested count) | Set as needed | 0 |
| Rotation | Varimax orthogonal rotation, Promax oblique rotation, No rotation | varimax |
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
The main table shows loadings and communalities. Raw output includes KMO, Bartlett testing, explained variance and rotation details. Under Promax, pattern and structure loadings differ; variance contributions of correlated factors are not simply additive.
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.