Factorial design-Experimental Research

Complex designs can also be called factorial designs because they involve factorial combination of independent variables. Factorial combination involves pairing each level of one independent variable with each level of a second in- dependent variable. This makes it possible to determine the effect of each inde- pendent variable alone (main effect) and the effect of the independent variables in combination (interaction effect).

In this type of study, there are two factors (or independent variables) and each factor has two levels. The number of digits tells you how many in independent variables (IVs) there are in an experiment while the value of each number tells you how many levels there are for each independent variable. So, for example, a 4×3 factorial design would involve two independent variables with four levels for one IV and three levels for the other IV.

DESCRIBING EFFECTS IN A COMPLEX DESIGN

• Researchers use complex designs to study the effects of two or more

independent variables in one experiment.

• In complex designs, each independent variable can be studied with an

independent groups design or with a repeated measures design.

• The simplest complex design is a 2 2 design—two independent variables,

each with two levels.

• The number of different conditions in a complex design can be determined

by multiplying the number of levels for each independent variable (e.g., 2 2 4).

• More powerful and efficient complex designs can be created by including

more levels of an independent variable or by including more independent variables in the design.

The Advantages and Challenges of Using Factorial Designs

One of the big advantages of factorial designs is that they allow researchers to look for interactions between independent variables. An interaction is a result in which the effects of one experimental manipulation depends upon the experimental manipulation of another independent variable.

For example, imagine that researchers want to test the effects of a memory-enhancing drug. Participants are given one of three different drug doses, and then asked to either complete a simple or complex memory task. The researchers note that the effects of the memory drug are more pronounced with the simple memory tasks, but not as apparent when it comes to the complex tasks. In this 3×2 factorial design, there is an interaction effect between the drug dosage and the complexity of the memory task.

So if researchers are manipulating two or more independent variables, how exactly do they know which effects are linked to which variables?

“It is true that when two manipulations are operating simultaneously, it is impossible to disentangle their effects completely,” explain authors Breckler, Olson, and Wiggins in their book Social Psychology Alive. “Nevertheless, the researchers can explore the effects of each independent variable separately by averaging across all levels of the other independent variable. This procedure is called looking at the main effect.”

Examples of Factorial Designs

A university wants to assess the starting salaries of their MBA graduates. The study looks at graduates working in four different employment areas: accounting, management, finance, and marketing. In addition to looking at the employment sector, the researchers also look at gender. In this example, the employment sector and gender of the graduates are the independent variables, and the starting salaries are the dependent variables. This would be considered a 4×2 factorial design.

Researchers want to determine how the amount of sleep a person gets the night before an exam impacts performance on a math test the next day. But the experimenters also know that many people like to have a cup of coffee (or two) in the morning to help them get going. So, the researchers decide to look at how the amount of sleep and the amount of caffeine influence test performance.  The researchers then decide to look at three levels of sleep (4 hours, 6 hours, and 8 hours) and only two levels of caffeine consumption (2 cups versus no coffee). In this case, the study is a 3×2 factorial design.

_References

 _{Breckler, S. J., Olson, J. M., & Wiggins, E. C. (2006). Social Psychology Alive. Belmont, CA: Cengage Learning.}

 _{Davis, S. F., & Buskist, W. (2008). 21st Century Psychology: A Reference Handbook. Thousand Oaks, CA: SAGE Publications.}

 _{Van der Merwe, L., & Viljoen, C.S. (2000). Applied Elementary Statistics. Pearson.}