Multivariate methods

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, and scree plots
• Squared multiple correlations
• Orthogonal and oblique rotations
• Kaiser normalization
• Varimax, quartimax, oblimax, parsimax, equamax, and promax rotation
• Minimum entropy rotation
• Comrey’s tandem
• Rotate toward a target matrix

Discriminant analysis

• Linear
• Quadratic
• Logistic
• kth nearest neighbor
• Classification tables
• Error rates

Zellner’s seemingly unrelated regression

• Two-step or maximum likelihood estimates
• Linear constraints
• Breusch-Pagan test for independent equations

Multivariate linear regression

• Breusch-Pagan test for independent equations
• Use with multiple imputation for missing data

Canonical correlations

• Correlation matrices
• Loading matrices
• Rotate raw coefficients, standard coefficients, or loading matrices
• Compare rotated and unrotated coefficients or loadings
• Plot canonical correlations

Tetrachoric correlations

• Maximum likelihood or noniterative Edwards and Edwards estimator
• Tetrachoric correlation coefficient and standard error
• Exact two-sided significance level

Structural equation modeling (SEM)

• Complete implementation
• Read more here

Cluster analysis

• Complete implementation
• Read more here

MANOVA

• Complete implementation
• Read more here

Multivariate tests

• One- and multisample
• Means, covariances, and correlations
• Tests of normality
• Doornik–Hansen
• Henze–Zirkler
• Two by Mardia

Multidimensional scaling

• Modern metric and nonmetric 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 approximating Euclidean configuration

Correspondence analysis

• Two-way correspondence analysis
• Multiple correspondence analysis (MCA)
• Joint correspondence analysis (JCA)
• Work with cross-tabulations of categorical variables or matrices of counts
• Coordinates in column space
• Coordinates in row space (with two-way CA)
• Stacked (crossed) variables
• Chi-squared distances
• Inertia contributions
• Row and column profiles (conditional distributions)
• Fitted, observed, and expected correspondence tables
• Matrix of inertias (after JCA)
• Biplots
• Projection plots
• Correlations of profiles and loadings

Procrustes analysis

• Orthogonal, oblique, and unrestricted transformations
• Overlayed graphs comparing target variables and fitted values of source variables

Biplots

• Display your choice of any two biplot dimensions
• Distinguish groups of data within the biplot
• Display table of biplot coordinates
• Generate new variables containing biplot coordinates

Hotelling’s T-squared