Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor, and each subdivision of a factor is called a level. Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

The most basic factorial design is a factorial design, which consists of two treatments, each with two levels (such as high/low or present/absent). For instance, say that you want to compare the learning outcomes of two different types of instructional techniques (in-class and online instruction), and you also want to examine whether these effects vary with the time of instruction (one and a half or three hours per week). In this case, you have two factors: instructional type and instructional time, each with two levels (in-class and online for instructional type, and one and a half and three hours/week for instructional time), as shown in Figure 8.1. If you wish to add a third level of instructional time (say six hours/week), then the second factor will consist of three levels and you will have a factorial design. On the other hand, if you wish to add a third factor such as group work (present versus absent), you will have a factorial design. In this notation, each number represents a factor, and the value of each factor represents the number of levels in that factor.

Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. represents random assignment of subjects to treatment groups, represents the treatment groups themselves (the subscripts of represent the level of each factor), and represent observations of the dependent variable. Notice that the factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the design will have six treatment groups, and the design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all. These are called incomplete factorial designs. Such incomplete designs hurt our ability to draw inferences about the incomplete factors.

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.