Zach’s facts have been extracted from the book to remind you of the key concepts you and Zach have learned in each chapter.
Zach's Facts 17.1 Analysing factorial designs
- When you want to compare several means when there are two independent variables you can use a linear model with those independent variables and their interaction as predictors. This model is often called a two-way ANOVA.
- You can test for homogeneity of variance using Levene’s test: if its p-value is less than 0.05 then the assumption is violated.
- The analysis will test the significance of the individual effects of each independent variable (known as main effects) and their interaction by computing a p-value.
- The interaction effect tests whether the effect of one independent variable is different at different levels of the other independent variable. To interpret a significant interaction effect, look at an interaction graph or conduct simple effects analysis.
- You don’t need to interpret main effects if an interaction effect involving that variable is significant.