User Guide

15
Chapter
2
GLM Repeated Measures
The GLM Repeated Measures procedure provides analysis of variance when the same
measurement is made several times on each subject or case. If between-subjects
factors are specified, they divide the population into groups. Using this general linear
model procedure, you can test null hypotheses about the effects of both the between-
subjects factors and the within-subjects factors. You can investigate interactions
between factors as well as the effects of individual factors. In addition, the effects of
constant covariates and covariate interactions with the between-subjects factors can
be included.
In a doubly multivariate repeated measures design, the dependent variables
represent measurements of more than one variable for the different levels of the
within-subjects factors. For example, you could have measured both pulse and
respiration at three different times on each subject.
The GLM Repeated Measures procedure provides both univariate and multivariate
analyses for the repeated measures data. 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. In addition to testing hypotheses, GLM Repeated Measures
produces estimates of parameters.
Commonly used a priori contrasts are available to perform hypothesis testing on
between-subjects factors. 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 (interaction plots) of these means allow you to visualize some of the
relationships easily.