Factor analysis
- works on datasets or correlation matrices
- principal-components factor
- principal factor
- interated principal factor
- ML factors
- rotations
- anti-image correlation matrices
- Kaiser–Meyer–Olkin measure of sampling adequacy
- squared multiple correlations
- Bartlett scoring
- regression scoring
Principal components
- works with datasets or correlation or covariance matrices
- standard errors of eigenvalues and vectors
- rotations
- anti-image correlation matrices
- Kaiser–Meyer–Olkin measure of sampling adequacy
- loading plots, score plots, scree plots
- squared multiple correlations
Rotations after factor and principal components analyses
- orthogonal and oblique rotations
- Horst normalization
- varimax, quartimax, oblimax, parsimax, equamax, promax rotation
- minimum entropy rotation
- Comrey's tandem
- rotate toward a target matrix
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Multidimensional scaling
- classic metric multidimensional scaling
- works on datasets or matrices of distances
- 33 similarity/dissimilarity measures
- coordinates of approximating configuration
- correlations between dissimilarities and distances
- Kruskal stress measure
- Shepard diagram
- Plots of approximation Euclidian configuration
Procrustes analysis
- orthogonal, oblique, and unrestricted transformations
- overlayed graphs comparing target variables and fitted
values of source variables
Two-way correspondence analysis
- works with cross-tabulation of two categorical variables
or a matrix of counts
- coordinates in row and column space
- chi-squared distances
- inertia contributions
- row and column profiles (conditional distributions)
- Fitted, observed, and expected correspondence tables
- biplots
- projection plots
Biplots
Canonical correlations
Tetrachoric correlations
Zellner's seemingly unrelated regression
Multivariate linear regression
Hotelling's T-squared
Tests for identifying multivariate outliers
Cluster analysis
MANOVA
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