Factor analysis
Works on datasets or correlation matrices
Principal-components factor
Principal factor
Interated principal factor
ML factors
Rotations
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
Anti-image correlation matrices
Kaiser–Meyer–Olkin measure of sampling adequacy
Loading plots, score plots, and scree plots
Squared multiple correlations
Bartlett scoring
Regression scoring
Principal components
Works with datasets or correlation or covariance matrices
Standard errors of eigenvalues and vectors
Anti-image correlation matrices
Kaiser–Meyer–Olkin measure of sampling adequacy
Loading plots, score plots, and scree plots
Squared multiple correlations
Rotations
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
Bayesian multivariate regression
Procrustes analysis
Orthogonal, oblique, and unrestricted transformations
Overlayed graphs comparing target variables and fitted values of source variables
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
Latent class analysis
Including latent profile analysis
Including finite mixture models
Marginal probabilities and marginal means
Evaluate goodness of fit
Predict probabilities of class membership and values of observed outcome variables
Watch Latent class analysis (LCA).
Cluster analysis
Complete implementation
MANOVA
Complete implementation
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 with two-way data, proximity data in long format, and proximity data in a matrix
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
Work with cross-tabulations of categorical variables or matrices of counts
Stacked (crossed) variables
Fitted, observed, and expected correspondence tables
Coordinates in column space
Coordinates in row space (with two-way CA)
Row and column profiles (conditional distributions)
Chi-squared distances
Correlations of profiles and axes
Inertia contributions
Biplots
Projection plots
Multiple and joint correspondence analysis (MCA and JCA)
Work with cross-tabulations of categorical variables
Stacked (crossed) variables
Coordinates in column space
Projection plots
Matrix of inertias (after JCA)
Postestimation Selector
View and run all postestimation features for your command
Automatically updated as estimation commands are run
Watch Postestimation Selector.
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
Cronbach’s alpha
Interitem correlations or covariances
Generate summative scale
Automatically reverse sense of variables
