There appears to be a few possible alternatives to determining whether there is a significant interaction effect.
It is not clear to me whether testing the statistical significance of an interaction term works the same way for different types of scenarios:
(1) 2 WAY ANOVA: Categorical by Binary (where A= 4 level factor, leading to 3 interaction terms being tested)
It seems that F-value for specific interaction term is the most widely used.
Are the following acceptable alternative indices? incremental tests using chi-square or F-value change with and without the interaction term
(2) Continuous by Continuous
- Significance of the overall model (Y=A+B+AB, using overall F-value) and the significant p-value for AB
(4) Continuous by Categorical - Anova would still generate an F-value for (A, B, A*B). - Are there alternative indices for this scenario when A is a factor (4 levels), B is binary?
(5) Categorical by Categorical by Continuous (3-way interaction)
Suggestions for any or all of the above (keeping in mind R is the platform) would be much appreciated.