In Chapter 12, we extend the logic of the experiment that uses one independent variable to experiments that use two or more independent variables. The main advantages of studying more than two independent variables at a time are
|No Noise||Group 1||Group 2|
|Noise||Group 3||Group 4|
If, on the other hand, there is no interaction, then you can talk about the effects of noise and caffeine separately. You do not have to qualify your statements about the effects of noise by saying things like "However, the effect of noise is qualified by a noise by caffeine interaction. The effect of noise is different in the no caffeine condition than in the caffeine condition."
To get a general idea of what interactions represent, see Table 12-1 and 12-2 (pp. 475-477).
After explaining interactions, we made two important distinctions.
First, we distinguished between ordinal and disordinal (cross-over) interactions. We pointed out that you can easily tell which type of interaction you have by graphing it. More importantly, we showed how ordinal interactions--rather than meaning that the combination of two variables has a smaller (or bigger) psychological effect than would be expected by looking at the variables' individual effects--could be due to having ordinal scale data. Cross-over interactions, on the other hand, can't be due to having ordinal scale data. Thus, we can usually be more confident of what a cross-over interaction represents.
Second, we distinguished between "true " ( "strong ") independent variables that we can randomly assign (level of caffeine) and "weak " independent variables that we can't randomly assign (sex, personality type, etc.) . We stressed that you can't make causal statements about "weak " independent variables (variables that we do not manipulate).
Finally, you learned how you can improve a simple experiment by adding a predictor variable (thus turning it into a 2 X 2). For example, the 2 X2 might have more external validity or might allow you to find a moderating factor.
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