Archive:Advanced ANOVA/Assumptions

Template:50%done This page outlines and summarises the assumptions for various ANOVA models, including t-tests.

All models

1. Dependent variables must be:
   • Measured at interval or ratio level level of measurement.
   • Normally distributed in all groups of the independent variable.
     • ANOVA is quite robust to violations of this assumption if sample sizes are large and approximately equal (> 15 cases per group)

t-tests, one-way ANOVA and factorial ANOVA

1. The data in each cell is normally distributed.
2. Homogeneity of variance:
   • The variance for each cell should be similar - a rule of thumb is that one SD should not be more than double another cell SD.
   • If violated,
     • the p-values for significance tests are inaccurate.
     • SPSS has post-hoc tests to adjust for this.
3. Cells are independent - Cases represent random samples from the target populations and the scores of the test variable should be independent of each other (i.e., the scores in one cell are not dependent on the scores in another cell).
   • Inaccurate p-values are produced if the independence assumption is violated.