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

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

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

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