SPSS® 12.0 Command Syntax Reference

650 GLM: Overview
For a repeated measures model, you can specify one, two, or all three of the L, M, and K
matrices. If only one or two types are specified, the unspecified matrices use the defaults
shown in Table 2 (read across the rows).
Table 2 Default matrices for repeated measures models if only one matrix is specified
L matrix M matrix K matrix
If LMATRIX is used to specify
the L matrix
Default = average matrix *
Default = zero matrix
Default = intercept matrix †
If MMATRIX is used to specify
the M matrix
Default = zero matrix
Default = intercept matrix † Default = average matrix * If KMATRIX is used to
specify the K matrix
* The average matrix is the transformation matrix that corresponds to the transformation for the
between-subjects test. The dimension is the number of measures.
† The intercept matrix is the matrix corresponding to the estimable function for the intercept term in the
model, provided that the intercept term is included in the model. If the intercept term is not included in
the model, the L matrix is not defined and this custom hypothesis test cannot be performed.
Example
GLM Y1 Y2 BY A B
/WSFACTOR TIME (2)
/MMATRIX Y1 1 Y2 1; Y1 1 Y2 -1
/DESIGN A B.
• Since WSFACTOR is specified, this is a repeated measures model with two betweensubjects
factors A and B, and a within-subjects factor, TIME.
• A custom hypothesis is requested by the MMATRIX subcommand. The M matrix is a
matrix:
1 1
1 –1
• Since the L matrix and the K matrix are not specified, their defaults are used. The default
for the L matrix is the matrix corresponding to the estimable function for the intercept
term in the between-subjects model, and the default for the K matrix is a zero-row vector
(0, 0).
CONTRAST Subcommand
2× 2
When the CONTRAST subcommand is used, an L matrix, which is used in custom hypothesis
testing, is generated according to the contrast chosen. The K matrix is always taken to be the
zero matrix. If the model is univariate or multivariate, the M matrix is always the identity matrix
and its dimension is equal to the number of dependent variables. For a repeated measures
model, the M matrix is always the average matrix that corresponds to the average transformation
for the dependent variable.