User Guide

16
Chapter 2
Residuals, predicted values, Cook’s distance, and leverage values can be saved as
new variables in your data file for checking assumptions. Also available are a residual
SSCP matrix, which is a square matrix of sums of squares and cross-products of
residuals, a residual covariance matrix, which is the residual SSCP matrix divided by
the degrees of freedom of the residuals, and the residual correlation matrix, which is
the standardized form of the residual covariance matrix.
WLS Weight allows you to specify a variable used to give observations different
weights for a weighted least-squares (WLS) analysis, perhaps to compensate for
different precision of measurement.
Example. Twelve students are assigned to a high- or low-anxiety group based on their
scores on an anxiety-rating test. The anxiety rating is called a between-subjects factor
because it divides the subjects into groups. The students are each given four trials on a
learning task, and the number of errors for each trial is recorded. The errors for each
trial are recorded in separate variables, and a within-subjects factor (trial) is defined
with four levels for the four trials. The trial effect is found to be significant, while the
trial-by-anxiety interaction is not significant.
Methods. Type I, Type II, Type III, and Type IV sums of squares can be used to evaluate
different hypotheses. Type III is the default.
Statistics. Post hoc range tests and multiple comparisons (for between-subjects
factors): least significant difference, Bonferroni, Sidak, Scheffé, Ryan-Einot-Gabriel-
Welsch multiple F, Ryan-Einot-Gabriel-Welsch multiple range, Student-Newman-
Keuls, Tukey’s honestly significant difference, Tukey’s-b, Duncan, Hochberg’s GT2,
Gabriel, Waller Duncan t test, Dunnett (one-sided and two-sided), Tamhane’s T2,
Dunnett’s T3, Games-Howell, and Dunnett’s C. Descriptive statistics: observed means,
standard deviations, and counts for all of the dependent variables in all cells; the
Levene test for homogeneity of variance; Box’s M; and Mauchly’s test of sphericity.
Plots. Spread-versus-level, residual, and profile (interaction).
Data. The dependent variables should be quantitative. Between-subjects factors divide
the sample into discrete subgroups, such as male and female. These factors are
categorical and can have numeric values or string values of up to eight characters.
Within-subjects factors are defined in the Repeated Measures Define Factor(s) dialog
box. Covariates are quantitative variables that are related to the dependent variable. For
a repeated measures analysis, these should remain constant at each level of a within-
subjects variable.
The data file should contain a set of variables for each group of measurements on
the subjects. The set has one variable for each repetition of the measurement within the