Matrix rotations
Orthogonal and oblique matrix rotations.
p = number of variables, m = number of factors.
| Method | Parameters | Purpose |
|---|---|---|
| Varimax | Orthogonal only. A computational faster equivalent to CF-Varimax. | |
| Promax | power 0 ... 4 | Oblique only. |
| Crawford-Ferguson | kappa 0 ... 1 | Smaller kappa minimizes variables complexity and larger kappa minimizes factor complexity. |
| CF-Varimax | Crawford-Ferguson kappa = 1/p. | Spread variances across factors. Each factor tends to have either large or small loadings on a particular variable making it easy to identify each variable with a single factor. |
| CF-Quartimax | Crawford-Ferguson kappa = 0. | Minimizes variable complexity. Works well with distinct clusters without cross-loadings. |
| CF-Equamax | Crawford-Ferguson kappa = m/(2p). | |
| CF-Parsimax | Crawford-Ferguson kappa = (m-1) / (p+m-2). | |
| CF-Factor Parsimony | Crawford-Ferguson kappa = 1. | Minimizes factor complexity. Primarily of theoretical interest. |
| Oblimin | gamma 0 ... 1 | |
| O-Quartimin | Oblimin gamma = 0. | Equivalent to quartimax. |
| O-Biquartimin | Oblimin gamma = 0.5. | |
| O-Covarimin | Oblimin gamma = 1. | Equivalent to varimax. |
| Geomin | delta > 0 | Minimizes variable complexity. |
Related concepts
Available in Analyse-it Editions
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition
Standard edition
Method Validation edition
Quality Control & Improvement edition
Ultimate edition
