Researchers often aim to design experiments to capture the complexity of the topic under study. Such complex research designs are known as multifactorial because two or more independent variables (“factors”) are manipulated at the same time within the same experiment.
A multifactorial experiment allows for the testing of effects of all possible combinations of the independent variables on the dependent variable. Of particular importance is that these designs provide a means to investigate interactions between independent variables.
Such experiments can vary the independent variables between subjects, others can vary them within subjects, and still others can combine between-subjects and within-subjects manipulation of the independent variables.
The analysis of variance (abbreviated as ANOVA), also known as the F test, is a general statistical procedure used to analyze results of a multifactorial experiment. The larger the F ratio, the more likely it is the results of the experiment are statistically significant and not due to chance alone.