Publication date: 08/13/2020

Effect Tests

The Effect Tests report appears only when there are fixed effects in the model. The effect test for a given effect tests the null hypothesis that all parameters associated with that effect are zero. An effect might have only one parameter as for a single continuous explanatory variable. In this case, the test is equivalent to the t test for that term in the Parameter Estimates report. A nominal or ordinal effect can have several associated parameters, based on its number of levels. The effect test for such an effect tests whether all of the associated parameters are zero.

Note the following:

Effect tests are conducted, when possible, for effects whose terms are involved in linear dependencies. See Models with Linear Dependencies among Model Terms.

Parameterization and handling of singularities differ from the SAS GLM procedure. For more information about parameterization and handling of singularities, see the The Factor Models in the Statistical Details section.

The Effects Test report contains the following columns:


The effects in the model.


The number of parameters associated with the effect. A continuous effect has one parameter. The number of parameters for a nominal or ordinal effect is one less than its number of levels. The number of parameters for a crossed effect is the product of the number of parameters for each individual effect.


The degrees of freedom for the effect test. Ordinarily, Nparm and DF are the same. They can differ if there are linear dependencies among the predictors. In such cases, DF might be less than Nparm, indicating that at least one parameter associated with the effect is not testable. Whenever DF is less than Nparm, the note LostDFs appears to the right of the line in the report. If there are degrees of freedom for error, the test is conducted. See Effect Tests Report.

Sum of Squares

The sum of squares for the hypothesis that the effect is zero.

F Ratio

The F statistic for testing that the effect is zero. The F Ratio is the ratio of the mean square for the effect divided by the mean square for error. The mean square for the effect is the sum of squares for the effect divided by its degrees of freedom.

Prob > F

The p-value for the effect test.

Mean Square

The mean square for the effect, which is the sum of squares for the effect divided by its DF.

Note: Appears only if you right-click in the report and select Columns > Mean Square.

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