Analysis Summary
ReadyComponent Loading Heatmap
Loading Matrix Comparison
Diagnostic Charts
Loading Matrix
Component and Feature Details
Component Statistics
Feature Statistics
Interpretation
Strongest features by component
Report Summary
Formula Used
A PCA loading combines an eigenvector coefficient with its component eigenvalue. The value measures a feature’s association with one component. Larger magnitudes indicate stronger relationships within the selected solution.
Squared loadings estimate each component’s contribution to a feature. Their row sum provides a communality measure. Factor uniqueness is commonly estimated as one minus communality.
How to Use
- Paste observations or upload a CSV dataset.
- Exclude identifiers, labels, targets, or unsuitable columns.
- Select preprocessing, retention, rotation, and display settings.
- Choose thresholds, sorting, colors, and optional comparisons.
- Select Calculate Heatmap to generate results and diagnostics.
- Inspect cells, review tables, then export desired formats.
Example Data
| Math | Science | Writing | Attendance | Study Hours | Projects |
|---|---|---|---|---|---|
| 78 | 82 | 69 | 91 | 7.2 | 6 |
| 92 | 95 | 88 | 97 | 10.1 | 9 |
| 64 | 61 | 73 | 82 | 4.5 | 5 |
| 85 | 89 | 80 | 94 | 8.4 | 8 |
Interpretation Guide
Positive loadings move with the component’s positive direction. Negative loadings move in the opposite direction. Component signs can reverse without changing the underlying solution.
Absolute values below 0.30 are often considered weak. Values from 0.30 to 0.49 are moderate. Values above 0.50 usually deserve closer interpretation.
Rotation can produce simpler and more interpretable loading patterns. Orthogonal rotations retain uncorrelated component axes. Promax allows correlated latent dimensions when appropriate.
Frequently Asked Questions
What is a component loading?
A loading measures the relationship between an original feature and a derived component. Its sign gives direction, while magnitude indicates relationship strength.
Should I standardize my features?
Standardization is usually helpful when feature units or scales differ. It prevents large-scale variables from dominating the component solution.
What does a negative loading mean?
A negative loading indicates an inverse relationship with the component direction. Component signs are arbitrary and may be reversed safely.
How many components should I retain?
Use explained variance, eigenvalues, scree patterns, and interpretability together. No single retention rule is universally best.
What does Varimax rotation do?
Varimax encourages loadings near zero or large magnitudes. This often makes each component easier to describe.
What is communality?
Communality is the sum of squared retained loadings for a feature. It estimates variance represented by the retained solution.
Why do loading signs change after rotation?
Rotations can reorient component axes while preserving represented information. Sign changes alone do not indicate an analytical error.
Can I paste an existing loading matrix?
Yes. Select the loading-matrix input mode and provide feature names plus component columns.
What is the EFA approximation?
This calculator uses an eigen-based exploratory approximation for educational analysis. Dedicated statistical software may offer fuller extraction and fit testing.
Why were some features removed?
Constant, near-constant, excluded, nonnumeric, or unmatched features may be removed. Review warnings and preprocessing details for reasons.