The GLM Repeated Measures Model

The GLM Repeated Measures procedure is based on the general linear model, in which factors and covariates are assumed to have linear relationships to the dependent variables.

Factors. Categorical predictors should be selected as factors in the model.

Levels of a between-subjects factor separate the cases into groups.
Levels of a within-subjects factor define the number of observations recorded for each subject for each measure.

Covariates. Scale predictors should be selected as covariates in the model. Within combinations of factor levels (or cells), values of covariates are assumed to be linearly correlated with values of the dependent variables.

Interactions. By default, the GLM Repeated Measures procedure produces a model with all factorial interactions, which means that each combination of factor levels can have a different linear effect on the dependent variable. Additionally, you may specify factor-covariate interactions, if you believe that the linear relationship between a covariate and the dependent variables changes for different levels of a factor.

For the purposes of testing hypotheses concerning parameter estimates, the GLM Repeated Measures procedure assumes:

The dependent variables have a multivariate normal distribution, and the covariance matrix of the dependent variables is constant across cells formed by the between-subjects effects. This assumption is necessary for the multivariate tests.
For the univariate tests, it is further assumed that the covariance matrix of the dependent variables is "circular/spherical" in form; that is, the covariance between any two elements is equal to the average of their variances minus a constant.