Research methods in psychology by B. Morling (third edition) – Chapter 12 summary

EXPERIMENTS WITH TWO INDEPENDENT VARIABLES CAN SHOW INTERACTIONS
Experiments 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 differences

FACTORIAL DESIGNS STUDY TWO INDEPENDENT VARIABLES
Testing 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 INTERACTONS
Researchers 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 each independent variable. The third result is the interaction effect.

FACTORIAL VARIATIONS
In a mixed factorial design, one variable is manipulated as independent groups and the other is manipulated as within-groups (e.g: age and driving while on the phone. Age is independent groups and driving while on the phone is within-groups). When plotting a three-way factorial design and you want to check for three-way-interactions, you have to look for differences between the two states. If the lines are the same for both states in the three-way interaction, then there is a two-way interaction, but not a three-way interaction (unless the lines are parallel).

IDENTIFYING FACTORIAL DESIGNS IN YOUR READING
When looking for factorial designs in research articles it is important to look at the method part of the research description. When looking for factorial designs in regular articles it is important to look for the phrases it depends and only when.