A Step by Step Guide for SPSS and Exercise Studies

94 Statistical tests
Table 28
Table 29
1. If there are multiple covariates, they should not be highly correlated
(r > .90) with each other in order to avoid computational problems.
2. Relationships between covariate(s) and dependent variables, as well as
between different covariates should be linear (i.e., represented by a straight
line). Non-linear relationships increase the chance for Type II error, that is,
the possibility of finding erroneous non-significant results. To test this
assumption, use the residual plots described in Figure 29. You can also
produce simple scatterplots (see the Graphs menu), plotting the dependent
variable against each covariate at each level of the independent variable
(use Select Cases in the Data menu to select each level in turn).
3. There is no interaction between the independent variable(s) and the
covariate(s). A significant interaction indicates that the relationship between
the dependent variable and the covariate(s) varies across the different
categories of the independent variable(s). To test the assumption of nonsignificant
interaction click Model in Dialog box 69 and select Custom to
open Dialog box 75. In the Factors & Covariates box you can see the two
independent factors and the covariate. Click on one variable at a time and
move it into the Model box. Then, highlight one of the independent
variables and the covariate, and move the pair into the Model box. Repeat
the same procedure with the second independent variable and the covariate.
Finally, highlight both two independent variables and the covariate and