Which variable are factors other than the independent variable that may cause a result?

Overview

Extraneous variables are variables other than the independent variable that may bear any effect on the behaviour of the subject being studied.

Classification

Extraneous variables are often classified into three main types:

1. Subject variables, which are the characteristics of the individuals being studied that might affect their actions. These variables include age, gender, health status, mood, background, etc.
2. Experimental variables are characteristics of the persons conducting the experiment which might influence how a person behaves. Gender, the presence of racial discrimination, language, or other factors may qualify as such variables.
3. Situational variables are features of the environment in which the study or research was conducted, which have a bearing on the outcome of the experiment. Included are the air temperature, level of activity, lighting, and the time of day.

There are two strategies of controlling extraneous variables. Either a potentially influential variable is kept the same for all subjects in the research, or they balance the variables in a group.

Take for example an experiment, in which a salesperson sells clothing on a door-to-door basis. The independent variable is the salesperson, and the dependent variable is the clothing sales. The extraneous variables, which are variables which have a bearing in the experiment being studied, are the salesperson's gender, age, or price.

Types

According to Campbell and Stanley[citation needed], there are at least eight kinds of extraneous variables:

History

These are the occurrence of events during the course of the experiment, but may affect the results of the dependent variable. This is a concern in education psychology, wherein there is a long time span for the duration of the experiment.

Maturation

These are changes that occur within the subjects during the passage of time. Variables such as physical growth, aging, hunger, etc. change over time and tend to be confounding variables to the experiment. For example, if you put a long span between the pre-testing and the post-testing of infants during your study of memory, it will not be internally valid for the reason that infants brain development is high, and its brain may have developed enough to have an effect on the post-test, thus showing that there is an increase in the memory capabilities of the infant, notwithstanding the fact that it has grown over time.

Testing

This is a threat to internal validity when a pre-test has had an effect on the post-test. This variable occurs in experiments using repeated testing, wherein the subject being tested becomes 'knowledgeable' about the experiment by putting his/her thoughts about the experiment, these are called demand characteristics.

Instrumentation

This is a threat to validity due to some misdemeanors on the part of the experimenter or checker, it has nothing to do with participants.

Selection

Improper assignment of test units to treatment conditions. This problem can be solved by random assignment of test units to treatment conditions.

Statistical Regression

Attrition or Experimental Mortality

A subject quits the experiment while the experiment is in progress.

Selection Interaction Effects

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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.

What are the 2 types of variables that can influence the results of an experiment?

What variable caused by independent variable and is it a cause of the dependent variable?

Which factors are considered as independent variables?

What variable is the factor that results in the change in the independent variables it is also known as the responding variable in the experimentation?