SPSS® 12.0 Command Syntax Reference

ANOVA 131
Some restrictions apply to the use of the regression approach:
• The lowest specified categories of all the independent variables must have a marginal frequency
of at least 1, since the lowest specified category is used as the reference category.
If this rule is violated, no ANOVA table is produced and a message identifying the first
offending variable is displayed.
• Given an n-way crosstabulation of the independent variables, there must be no empty
cells defined by the lowest specified category of any of the independent variables. If this
restriction is violated, one or more levels of interaction effects are suppressed and a
warning message is issued. However, this constraint does not apply to categories defined
for an independent variable but not occurring in the data. For example, given two
independent variables, each with categories of 1, 2, and 4, the (1,1), (1,2), (1,4), (2,1), and
(4,1) cells must not be empty. The (1,3) and (3,1) cells will be empty but the restriction
on empty cells will not be violated. The (2,2), (2,4), (4,2), and (4,4) cells may be empty,
although the degrees of freedom will be reduced accordingly.
To comply with these restrictions, specify precisely the lowest nonempty category of each
independent variable. Specifying a value range of (0,9) for a variable that actually has values
of 1 through 9 results in an error, and no ANOVA table is produced.
Classic Experimental Approach
Each type of effect is assessed separately in the following order (unless WITH or AFTER is
specified on the COVARIATES subcommand):
• Effects of covariates
• Main effects of factors
• Two-way interaction effects
• Three-way interaction effects
• Four-way interaction effects
• Five-way interaction effects
The effects within each type are adjusted for all other effects of that type and also for the
effects of all prior types (see Table 1).
Hierarchical Approach
The hierarchical approach differs from the classic experimental approach only in the way it
handles covariate and factor main effects. In the hierarchical approach, factor main effects
and covariate effects are assessed hierarchically—factor main effects are adjusted only for
the factor main effects already assessed, and covariate effects are adjusted only for the
covariates already assessed (see Table 1). The order in which factors are listed on the ANOVA
command determines the order in which they are assessed.