Source: sleeping man: public domain; http://morguefile.com/archive/display/47680
Hello, class. So in today's lesson, we're going to be talking about what variables are, as well as the different types of variables that we'll be seeing in a psychological experiment. So in an experiment, a psychologist has to consider all the variables that are involved that can possibly affect the results, either intentionally or unintentionally, within the experiment.
A variable is something that can change or something that can be measured by or can affect the results of an experiment. So these are the causes and effects that are being manipulated and measured inside the experiment. For an example, in an experiment that's measuring the effect of sleep on intelligence-- say on taking the test-- the variables would be the amount of sleep that the subjects get and their intelligence as it's measured on a test score. So we've got those two variables to consider within that experiment.
There are different types of variables that a scientist needs to consider to determine the accuracy of an experiment itself. And those are independent variables, dependent variables, and extraneous variables. So an independent variable is any kind of variable that is changed and that is controlled by the experimenter themself. In other words, the independent variables are the causes that are being researched. And these are the changes that are going to be made in the experimental group-- the things that they put in there to test.
And these are also the things being controlled or being left out completely of the control groups within an experiment. For example, going back to the example we said about sleep and its effect on intelligence and test scores, the independent variable would be the amount of sleep. That's the thing that we can change, and we can see what the results would be.
On the other hand, the dependent variable is a variable that results from the experiment and from the independent variables themselves. These are the results of these things. Or in other words, these are the effects that are being researched. Remember, cause and effect is behind all experiments. For example, if we look at the example again, sleep is the independent variable, which would mean the dependent variable-- the result of the amount of sleep-- would be the intelligence that's tested from the subjects. And usually dependent variables are measured by something very specific or concrete, because we are talking about scientific measures. So in this case, intelligence would be measured by test scores.
And finally, we have extraneous variables. And extraneous variables are any outside variables or conditions that might affect the results of the experiment. So, for example, in the intelligence experiment and the effects of sleep, the extraneous variables might be the previous intelligence of the subject-- so whatever their levels of intelligence were before the experiment-- or, possibly, maybe what they eat that night, which might affect their test scores in some way. Some people say a big meal can make somebody more apt to do well on a test.
So the experimenter needs to recognize that these different extraneous variables might have an effect and design their experiment to control for them so they don't influence the experiment. For example, the experimenter might choose people that are at a similar level of intelligence, so there aren't any differences in their test scores. Or they might give the same thing to eat for everybody that's in the experiment the night before, so that doesn't affect it. We also need to consider extraneous variables when we're analyzing the data. And we're recognizing, when there are different or outlying results, that these might be the effect of extraneous variables.
Any condition that can change and might have an effect on the experiment.
Condition altered by the experimenter; experimenter sets their size, amount, or value. These are predicted cause for behavioral differences.
Measures the results of the experiment; condition is affected by independent variable.
Conditions that a researcher wants to prevent from affecting the outcomes of the experiment.