The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average acrosslevels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.
To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 6.1 shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.
Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory
Because IQ also differs across conditions, it is a confounding variable.
KEY TAKEAWAYS
- An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
- Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
- Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
EXERCISES
- Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
- Practice: For each of the following topics, decide whether that topic could be studied using an experimental research design and explain why or why not.
- Effect of parietal lobe damage on people’s ability to do basic arithmetic.
- Effect of being clinically depressed on the number of close friendships people have.
- Effect of group training on the social skills of teenagers with Asperger’s syndrome.
- Effect of paying people to take an IQ test on their performance on that test.
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What are Confounding Variables?
A confounding variable, also known as a third variable or a mediator variable, influences both the independent variable and dependent variable. Being unaware of or failing to control for confounding variables may cause the researcher to analyze the results incorrectly. The results may show a false correlation between the dependent and independent variables, leading to an incorrect rejection of the null hypothesis.
The Problem with Confounding Variables
For example, a research group might design a study to determine if heavy drinkers die at a younger age.
They proceed to design a study, and set about gathering data. Their results, and a battery of statistical tests, indeed show that people who drink excessively are likely to die younger.
Unfortunately, when the researchers gather data from their subjects’ non-drinking peers, they discover that they, too, die earlier than average. Maybe there is another factor, not measured, that influences both drinking and longevity?
The weakness in the experimental design was that they failed to take into account confounding variables, and did not try to eliminate or control any other factors.
Imagine that in this case, there is in fact no relationship between drinking and longevity. But there may be other variables which bring about both heavy drinking and decreased longevity. If they are unaware of these variables, the researchers may assume that heavy drinking is causing reduced longevity, i.e. they’ll make what’s called a “spurious association.” In reality, decreased longevity may be better explained by a third, confounding variable.
For example, it is quite possible that the heaviest drinkers hailed from a different background or social group. This group might be, for unrelated reasons, shorter lived than other groups. Heavy drinkers may be more likely to smoke, or eat junk food, all of which could be factors in reducing longevity. In any case, it is the fact they belong to this group that is responsible for their decreased longevity, and not heavy drinking.
Without controlling for potential confounding variables, the internal validity of the experiment is undermined.
Extraneous Variables
Any variable that researchers are not deliberately studying in an experiment is an extraneous (outside) variable that could threaten the validity of the results. In the example above, these could include age and gender, junk food consumption or marital status.
An extraneous variable becomes a confounding variable when it varies along with the factors you are actually interested in. In other words, it becomes difficult to separate out which effect belongs to which variable, complicating the data.
To return to the example, age might be an extraneous variable. The researchers could control for age by making sure that everyone in the experiment is the same age. If they didn’t, age would become a confounding variable.
Any time there is another variable in an experiment that offers an alternative explanation for the outcome, it has the potential to become a confounding variable. Researchers must therefore control for these as much as possible.
Minimizing the Effects of Confounding Variables
In many fields of science, it is difficult to remove entirely all of the confounding variables, especially outside the controlled conditions of a lab.
A well-planned experimental design, and constant checks, will filter out the worst confounding variables.
For example, randomizing groups, utilizing strict controls, and sound operationalization practice all contribute to eliminating potential third variables.
After research, when the results are discussed and assessed by a group of peers, this is the area that stimulates the most heated debate. When you read stories of different foods increasing your risk of cancer, or hear claims about the next super-food, assess these findings carefully.
Many media outlets jump on sensational results, but never pay any regard to the possibility of confounding variables.
Mini-quiz:
Imagine that a research project attempts to study the effect of a popular herbal antidepressant. They sample participants from an online alternative medicine group and ask them to take the remedy for a month. The participants complete a depression inventory before and after the month to measure whether they experience any improvement in their mood. The researchers do indeed find that the participants’ moods are better after a month of treatment.
Can you identify any variables which may have confounded this result? The answer is at the bottom of the page.
Correlation and Causation
The principle is closely related to the problem of correlation and causation.
For example, a scientist performs statistical tests, sees a correlation and incorrectly announces that there is a causal link between two variables.
The problem is that the research has not actually isolated a true cause and effect relationship. It is similar to a researcher who notices that the fewer storks there are in a country, the lower the birth rate is. They would be mistaken to assume that a decrease in storks causes a decrease in birth rate.
Though these factors might show some correlation, it doesn’t mean that one is causing the other. In fact, two variables may move with one another purely by coincidence!
Constant monitoring, before, during and after an experiment, is the only way to ensure that any confounding variables are eliminated.
Statistical tests, whilst excellent for detecting correlations, can be almost too accurate.
Human judgment is always needed to eliminate any underlying problems, ensuring that researchers do not jump to conclusions.
Mini-quiz Answer
The fact that the participants were sampled from a group with an interest in alternative medicine may mean that a third variable, their belief in the effectiveness of the remedy, was responsible. You may have thought of other confoudning variables. For example their mood might have improved for a number of other unrelated reasons, like a change in weather, holidays, or an improvement in personal circumstances.