in a factorial design, the joint effect of two or more independent variables on a dependent variable above and beyond the sum of their individual effects: The independent variables combine to have a different (and multiplicative) effect, such that the value of one is contingent upon the value of another. This indicates that the relationship between the independent variables changes as their values change. For example, if a researcher is studying how gender (female vs. male) and dieting (Diet A vs. Diet B) influence weight loss, an interaction effect would occur if women using Diet A lost more weight than men using Diet A. Interaction effects contrast with—and may obscure—main effects. See also higher order interaction. Compare additive effect.