Sequential Tests
In the Generalized Linear Mixed Model report, the Sequential (Type 1) Tests report contains a table of sequential (type I) tests of the fixed effects. The report contains the sums of squares as effects are added to the model sequentially. The order of entry is defined by the order of effects as they appear in the Fit Model launch window’s Construct Model Effects list.
The sums of squares that form the basis for sequential tests are also called Type I Sums of Squares. They are computed by fitting models in steps following the specified entry order of effects. Consider a specific effect. Compute the model sum of squares for a model containing all effects entered prior to that effect. Then compute the model sum of squares for a model containing those effects and the specified effect. The sequential sum of squares for the specified effect is the increase in the model sum of squares.
Sequential tests are considered appropriate in the following situations:
• balanced analysis of variance models specified in proper sequence (that is, two-way interactions follow main effects in the effects list, and so on)
• purely nested models specified in the proper sequence
• polynomial regression models specified in the proper sequence.
The Sequential (Type 1) Tests report contains the following columns:
Source
The fixed effects in the model.
Nparm
The number of parameters that are 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.
DFNum
The numerator degrees of freedom for the effect test.
DFDen
The denominator degrees of freedom for the effect test, or the degrees of freedom for error. DFDen is calculated using the Kenward-Roger first order approximation.
F Ratio or ChiSquare
The computed F ratio or chi-square statistic for testing that the effect is zero. If no random effects are present in the model and the specified Distribution has only one parameter, the test is a chi-square test. Otherwise, the test is an F test.
Prob > F or Prob>ChiSq
The p-value for the effect test.