EXPERIMENTS WITH TWO INDEPENDENT VARIABLES CAN SHOW INTERACTIONSExperiments with more than one independent variable allows researchers to look for an interaction effect. This is an effect where the effect of the original independent variable depends on the level of another independent variable. If the two lines of the independent variables cross, there is a crossover interaction, also known as “it depends”. If the lines are not parallel, there is an interaction and if the lines are parallel, there is no interaction. A spreading interaction occurs when the two lines spread out and can be labelled as an “only when..” interaction. An interaction is a difference in differencesFACTORIAL DESIGNS STUDY TWO INDEPENDENT VARIABLESTesting for interactions is done with factorial designs. A factorial design is one in which there are two or more independent variables. In a factorial design, researchers study each possible combination of the independent variables. A participant variable is a variable whose levels are selected, but cannot be manipulated (e.g: age, the level for this variable can be selected, but not manipulated). Using factorial designs to test limits is called testing for moderators and it is a way to test the external validity of an experiment. Factorial designs can also test theories and hypotheses. INTERPRETING FACTORIAL RESULTS: MAIN EFFECTS AND INTERACTONSResearchers test each independent variable to look for main effects, the overall effect of one independent variable on another independent variable. Marginal means are the arithmetic means for each level of an independent variable, averaging over levels of the other independent variable. The main effect is not the most important effect, but the overall effect of one independent variable on another independent variable. The interaction itself is the most important effect. In a factorial design with two independent variables, the first to results obtained are the main effects for...