up:: [[Types of Experimental Design]] Tags:: #🌱 # Factorial Design Factorial designs have multiple dependent variables instead of just one. Each IV is responsible for one main effect or the overall effect of the independent variable on the dependent variable. Main effects = the number of IVs. #### What is the purpose of factorial designs? Factorial designs are useful for testing the interaction between two different independent variables. Even though each factor has it's own main effect it's the interaction that is the most important effect. For instance, the effect of breaking speed while driving depending on cell phone usage and age of the driver. Most of life in the real world is comprised of tons and tons of interactions which means it makes sense to study interactions in studies. In the psychological sciences, they are most useful in testing theories in the real world. ###### What are interaction effects? [[Interaction effects]]. ###### What are marginal means? A marginal mean is (as the name suggests) **a mean found in the margins (i.e. the edges) of a contingency table**. In other words, it's the average scores from a group or subgroup in an experiment. ### How to measure interaction effects ### Table If you find a difference between main effects on the same factor than you know there is an interaction. ![[Pasted image 20221109132644.png]] ### Graph If the lines are not parallel, there is an interaction. ![[Pasted image 20221109132708.png]] ### Bar graph If you can draw to lines in between the tops of the bar graphs and they aren't parallel, there is an interaction effect. ## Types of factorial designs ### Independent group designs There are multiple separate groups for each level of the factors. ### Within groups designs All participants experience all levels of all factors. ### Mixed Designs There is a mixing of independent groups design and within groups design. ### How do you analyze factorial designs? Factorial designs are analyzed using [[ANOVA]]. Related: Created: [[28-09-2022]] ___ # Resources