Independent, Dependent and Intervening Vari-

8.2 Independent, Dependent and Intervening Vari-

  ables

  By convention the variables manipulated by the experimenter are known as the independent variables (e.g, the interference level). The different values of the in- dependent variable are called treatment levels, or simply treatments. For exam- ple, in Stroop’s experiment the treatments were the “Neutral” and “Interference”. Experiments are designed to test whether the different levels of the independent variable have an effect on another variable known as the dependent variable. In Stroop’s experiment the dependent variable is the reaction time.

  Figure 8.1: Independent and intervening variables have a potential effect on the dependent variable.

  Intervening variables are variables other than the independent variable that have a potential effect on the dependent variable. Even though we are interested on the relationship between the independent and the dependent variables, in prac- tice there are many other variables that are also having an effect on the dependent variable. For example, the temperature of the room, the age of the subject, the particular mood of the subject at test time, may also have an effect on the depen-

  84 CHAPTER 8. INTRO TO EXPERIMENTAL DESIGN

  dent variable. Some experimenters emphasize the fact that they are not interested in the effects of some intervening variables, by calling them nuisance variables.

  Good experimental designs control for all possible intervening variables so that the only systematic source of variation that can explain the results is the in- dependent variable. If a study is poorly designed and intervening variables may actually explain our results we say that the design lacks internal validity, or that it has confounding variables. For example, if we choose all the subjects in the neutral condition to be males and all the subjects in the interference condition to

  be females, we would say that sex is a confounding variable. The design lacks internal validity and we would not be able to tell whether the effects on the depen- dent variable are due to the variable “sex” or to the treatment conditions (Neutral vs. Interference).

  The art of experimental design consists of making sure intervening variables distribute in a “fair” fashion amongst the different treatment conditions so that the only systematic differences between the different treatment groups can be at- tributed to the independent variable. The concept of “fair” distribution of the intervening variables is very tricky and we should return to it later. For now we’ll just use the concept intuitively and say that when an intervening variable is distributed in a “fair” fashion amongst the treatment conditions we say that the variable has been controlled. If an intervening variable has been controlled then it cannot be used to explain away our results. For example, if all our subjects are females, and we still find a difference between the two neutral and the interference conditions, clearly sex cannot explain this difference. By holding the sex of all subjects constant we have controlled its possible effect. In scientific parlance we’d say that sex is a controlled variable. Only when all possible intervening variables are controlled we can proceed to analyze whether the independent variable X has an effect on the dependent variable Y . In the next section we discuss the most common methods of control available to scientists.