User Guide

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Chapter
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GLM Multivariate Analysis
The GLM Multivariate procedure provides regression analysis and analysis of
variance for multiple dependent variables by one or more factor variables or
covariates. The factor variables divide the population into groups. Using this general
linear model procedure, you can test null hypotheses about the effects of factor
variables on the means of various groupings of a joint distribution of dependent
variables. You can investigate interactions between factors as well as the effects of
individual factors. In addition, the effects of covariates and covariate interactions with
factors can be included. For regression analysis, the independent (predictor) variables
are specified as covariates.
Both balanced and unbalanced models can be tested. A design is balanced if each
cell in the model contains the same number of cases. In a multivariate model, the sums
of squares due to the effects in the model and error sums of squares are in matrix form
rather than the scalar form found in univariate analysis. These matrices are called
SSCP (sums-of-squares and cross-products) matrices. If more than one dependent
variable is specified, the multivariate analysis of variance using Pillai’s trace, Wilks’
lambda, Hotelling’s trace, and Roy’s largest root criterion with approximate F
statistic are provided as well as the univariate analysis of variance for each dependent
variable. In addition to testing hypotheses, GLM Multivariate produces estimates of
parameters.
Commonly used a priori contrasts are available to perform hypothesis testing.
Additionally, after an overall F test has shown significance, you can use post hoc tests
to evaluate differences among specific means. Estimated marginal means give
estimates of predicted mean values for the cells in the model, and profile plots