Skip to Content

What Is an Extraneous Variable? Definition and Challenges

An extraneous variable is anything in a psychology experiment other than the independent and dependent variables. The variables can present challenges and introduce errors, so it is important for experiments to control these extraneous factors.

Researchers accomplish this by holding the extraneous variables constant across all conditions of the experiment.

In this article, learn more about what extraneous variables are and how they affect experiments.

How Does an Extraneous Variable Work?

Imagine that a researcher has devised an experiment to investigate whether giving study extra study time can help reduce test anxiety. The amount of study time the students have is the independent variable (since it is what the experimenter manipulates), and the amount of test anxiety the students experience is the dependent variable (since it is what the researchers are measuring).

However, extraneous variables such as the temperature of the room and the time of day the student is tested might have an influence on the independent variable as well.

In order to control for these extraneous variables, the research should ensure that all students are tested at the same time of day in rooms of the same temperature.

Examples of Extraneous Variables

  • In an investigation, researchers want to explore whether a new teaching method can improve student scores on math exams. One extraneous variable that might influence the results would be whether students have previous knowledge of the math covered on the exam.
  • Researchers want to determine if listening to fast-paced music improves performance during a marathon. Extraneous variables might include the volunteers’ physical condition, motivation to succeed, and overall energy levels on the day of the marathon.
  • Experimenters want to determine how sleep deprivation impacts driving performance. Extraneous variables might include the road conditions of the day of the driving test and individual differences in how participants cope with tiredness.

Controlling for an Extraneous Variable

There are two key methods that social scientists utilize to control for an extraneous variable:

Standardized Procedures

Standardized procedures involve making all aspects of an experiment identical with the exception of the independent variable. As much as possible, researchers will:

  • Recruit participants the same way
  • Conduct the experiments in the same setting
  • Offer the same rewards for participation in the study
  • Give participants the same explanations and give similar feedback once the experiment is over

Other standardized procedures might involve performing the experiment at the same time of day for each condition and making sure the conditions in the lab are the same for participants in all conditions (same temperature, brightness, and noise levels).

Random Assignment

Random assignment means that all participants have an equal chance of being assigned to any of the experimental conditions. Using random assignment in an experiment helps reduce the likelihood that the personal characteristics of the participants themselves will have an influence over the independent variables.

For example, in our previous example looking at study time and test anxiety, the researcher would use random assignment to assign students to either the experimental condition or the control condition. This reduces the likelihood that students who are simply less anxious in general will be assigned to one group while more anxious students are assigned to another group.

Randomizing the assignment process ensures that all students have an equal chance of being assigned to either group.

Challenges With Extraneous Variables

Breckler, Olson, and Wiggins (2006) note that while the control of extraneous variables is fairly simple in many fields, but is much more difficult when it comes to the social sciences. Why? Fields such as the physical sciences allow a great deal of control over the materials that are being studied.

When it comes to social science, researchers often have very little control over extraneous variables that may ultimately have an impact on the outcome.

A social psychologist, for example, might be interested in looking at human behaviors as they naturally occur. This makes it very difficult to construct a setting that allows complete control over all extraneous variables yet still permits the participants to behave as freely and spontaneously as they would in a more naturalistic setting.

Kantowitz, Roediger, and Elmes (2009) also note that exercising control over extraneous variables becomes particularly important in cases where the independent variable produces a small effect on the dependent variable.

Holding these variables constant can be very challenging in research that occurs outside of the lab.

Because controlling for extraneous variables is more challenging in real-world settings, researchers also utilize statistical techniques to help control for these extraneous factors.

For example, analysis of covariance (ANOVA) is one statistical technique that can be utilized to reduce the impact of an extraneous variable.

Extraneous vs. Confounding Variables

A confounding variable is one type of extraneous variable. An extraneous variable is any variable that is not being studied but could still potentially impact the dependent variable. Confounding variables are things that are not measured but can affect both the independent and dependent variables.

Examples of confounding variables can include participant characteristics (such as age, gender, or personality traits), environmental variables (like the lighting, temperature, or distractions), and demand characteristics (which are clues about the purpose of an experiment).


Breckler, S., Olson, J., & Wiggins, E. (2006). Social Psychology Alive. Belmont, CA: Wadsworth.

Kantowitz, B. H., Roediger, H. L., & Elmes, D. G. (2009). Experimental psychology. Belmont, CA: Wadsworth.

Pourhoseingholi, M. A., Baghestani, A. R., & Vahedi, M. (2012). How to control confounding effects by statistical analysis. Gastroenterology and hepatology from bed to bench5(2), 79–83.

Image: Sanja Gjenero /