When reviewing a factorial design, what does the "criss-cross" mean refer to?

In the context of factorial designs, the term "criss-cross" typically refers to the interaction effects that can occur when two or more independent variables are examined simultaneously. This happens when the effect of one independent variable depends on the level of another independent variable. When you visualize the results, the lines representing the levels of one independent variable can cross over the lines for another independent variable. This crossing indicates that the impact of one variable isn't consistent across the different levels of the other variable, which is a hallmark of interaction effects.

Understanding interactions is crucial because they reveal more complex relationships within the data that may not be evident when examining each variable in isolation. Thus, recognizing the "criss-cross" can help researchers interpret how various factors interact to influence the outcome, leading to more nuanced conclusions about their effects on the dependent variable.