When scientists start thinking about a research question, they must consider the ideal design to address it. In most cases, analyzing the relationship between two variables, scientists can reach their goal. However, there are more complex kinds of studies that request a different analysis, and consequently a less obvious design like what we saw until now. When more than one independent variables (or quasi-independent variable) are relevant to influence the existence of the dependent variables being studied, it is necessary to use a factorial design to understand how these two variables act over the dependent variable alone. This kind of design is widely used to analyze the effect of educational methods among students, and in agriculture science, testing the effect of variables on crops for example.
Based on the fact that it allows researchers to manipulate in a large number of variables, the factorial design has several applications creating a more realistic situation for several researchers develop their studies. In some cases, as in real-life situations, isolate and test each variable separately, may be unpractical and very unproductive too. This kind of design is an excellent method to start a preliminary study, being useful to acknowledge possible links between variables and avoid some eventual confounding variables as well.
In this design, when more than two independent variables are combined, they can be called as factors and eventually name the design by the numbers of factors being analyzed. Each factor is expressed by a letter, which considers the number of values or level for each factor (2x2). It is important to consider that the increase in factors and levels in a study can demand an excellent control over the measurements to avoid flaws in the analysis.
The main benefit of using this kind of design is that it permits that through the combinations of more than one variables, researches can observe how the independent variables can interfere with each other, creating unique conditions than acting alone. Factorial design can result in interaction between the factor when the effects of one factor depend on the different levels of the additional factors or no interactions at all. Sometimes, the effects are interdependent, and no factor influence each other.
There are different constructions of factorial designs in which researchers can use any combination of factors considering groups of a within-subject design and between-subjects designs. Commonly, factorial designs are helpful to offer new options through mixing distinct strategies and designs to increase the potential of findings, proposing new solutions to address scientific questions that could not be answered by any regular strategy.
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