SPSS® 12.0 Command Syntax Reference

GLM: Overview 645
• Display expected mean squares, automatically detecting and using the appropriate error
term for testing each effect in mixed- and random-effects models.
• Select commonly used contrasts or specify custom contrasts to perform hypothesis tests.
• Customize hypothesis testing, based on the null hypothesis LBM = K, where B is the
parameter vector or matrix.
• Display a variety of post hoc tests for multiple comparisons.
• Display estimates of population marginal cell means for both between-subjects factors
and within-subjects factors, adjusted for covariates.
• Perform multivariate analysis of variance and covariance.
• Estimate parameters using the method of weighted least squares and a generalized inverse
technique.
• Compare graphically the levels in a model by displaying plots of estimated marginal cell
means for each level of a factor, with separate lines for each level of another factor in the
model.
• Display a variety of estimates and measures useful for diagnostic checking. All of these
estimates and measures can be saved in a data file for use by another SPSS procedure.
• Perform repeated measures analysis of variance.
• Display homogeneity tests for testing underlying assumptions in multivariate and univariate
analyses.
General Linear Model (GLM) and MANOVA
MANOVA, the other generalized procedure for analysis of variance and covariance in SPSS,
is available only in syntax. The major distinction between GLM and MANOVA in terms of
statistical design and functionality is that GLM uses a non-full-rank, or overparameterized,
indicator variable approach to parameterization of linear models, instead of the full-rank
reparameterization approach used in MANOVA. The generalized inverse approach and the
aliasing of redundant parameters to zero employed by GLM allow greater flexibility in handling
a variety of messy data situations, particularly those involving empty cells. GLM offers
a variety of features unavailable in MANOVA:
• Identification of the general forms of estimable functions.
• Identification of forms of estimable functions specific to four types of sums of squares
(Types I–IV).
• Tests using the four types of sums of squares, including Type IV, specifically designed
for situations involving empty cells.
• Flexible specification of general comparisons among parameters, using the syntax
subcommands LMATRIX, MMATRIX and KMATRIX; sets of contrasts can be specified that
involve any number of orthogonal or nonorthogonal linear combinations.
• Nonorthogonal contrasts for within-subjects factors (using the syntax subcommand
WSFACTORS).
• Tests against nonzero null hypotheses can be specified using the syntax subcommand
KMATRIX.