Factorial Design (1 of 2)
When an experimenter is interested in the effects of two or more independent variables, it is usually more efficient to manipulate these variables in one experiment than to run a separate experiment for each variable. Moreover, only in experiments with more than one independent variable is it possible to test for interactions among variables. Consider a hypothetical experiment on the effects of a stimulant drug on the ability to solve problems. There were three levels of drug dosage: 0 mg, 100 mg, and 200 mg. A second variable, type of task, was also manipulated. There were two types of tasks: a simple well-learned task (naming colors) and a more complex task (finding hidden figures in a complex display). The mean time to complete the task for each condition in the experiment is shown below:
| Dose | Simple Task |
Complex Task |
|---|---|---|
0 mg |
32 |
80 |
100 mg |
25 |
91 |
200 mg |
21 |
96 |
As you can see, each level of dosage is paired with each level of type of task. The number of conditions (six) is therefore the product of the number of levels of dosage (three) and type of task (two).