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FACTORIAL DESIGNS: MAIN EFFECTS AND INTERACTION EFFECTS

Most experiments involve analyzing the effects of several independent variables at once. A factorial design lays out all the combinations of all the categories of the independent variables. That way you know how many participants you need, how many to assign to each condition, and how to run the analysis when the data are in.

It is widely believed that a good laugh has healing power. Rotton and Shats (1996) developed an experimental design to test this. They recruited 39 men and 39 women who were scheduled for orthopedic surgery. The patients were assigned randomly to one of nine groups—eight experimental groups and one control group. The patients in the eight treatment groups got to watch a movie in their room the day after their surgery.

There were three variables: choice, humor, and expectancy. The participants in the high-choice group got a list of 20 movies from which they chose 4. The participants in the low-choice group watched a movie that one of the people in the high-choice group had selected. Half the participants watched humorous movies and half watched action or adventure movies. Before watching their movie, half the participants read an article about the benefits of humor and half read an article about the healthful benefits of exciting movies.

Figure 4.4 is a branching tree diagram that shows how these three variables, each with two attributes, create the eight logical groups for Rotton and Shats’s experiment. Table 4.1 shows the same eight-group design, but in a format that is more common. The eight nodes at the bottom of the tree in figure 4.4 and the sets of numbers in the eight boxes of table 4.1 are called conditions.

The eight conditions in Rotton and Shats's 2 X 2 X 2 design.

SOURCE: J. Rotton and M. Shats, ''Effects of State Humor, Expectancies, and Choice on Postsurgical Mood and Self-Medication: A Field Experiment,'' Journal of Applied Social Psychology, Vol. 26, pp. 1775-94, 1996. Copyright © 1996 by V. H. Winston & Son, Inc. Reprinted with permission.

FIGURE 4.4.

The dependent variables in this study included a self-report by patients on the amount of pain they had and a direct measure of the amount of pain medication they took. All the patients had an access device that let them administer more or less of the analgesics that are used for controlling pain after orthopedic surgery.

In assessing the results of a factorial experiment, researchers look for main effects and interaction effects. Main effects are the effects of each independent variable on each dependent variable. Interaction effects are effects on dependent variables that occur as a result of interaction between two or more independent variables. In this case, Rotton and Shats wanted to know the effects of humor on postoperative pain, but they wanted to know the effect in different contexts: in the context of choosing the vehicle of humor or not, in the context of being led to believe that humor has healing benefits or not, and so on.

As it turned out, being able to choose their own movie had no effect when patients saw action films. But patients who saw humorous films and who had not been able to make their own choice of film gave themselves more pain killer than did patients who

Table 4.1 Three-Way, 2 X 2 X 2, Factorial Design

Variable 3

Variable 2

Attribute 1

Attribute 2

Variable 1

Attribute 1

Attribute 1

1,1,1 Condition 1

1,1,2 Condition 2

Attribute 2

1,2,1 Condition 3

1,2,2 Condition 4

Attribute 2

Attribute 1

2,1,1 Condition 5

2,1,2 Condition 6

Attribute 2

2,2,1 Condition 7

2,2,2 Condition 8

saw humorous films and had been able to make the selection themselves (Rotton and Shats 1996).

We’ll look at how to measure these effects when we take up ANOVA, or analysis of variance, in chapter 21.

 
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