When conducting an experiment, there are several factors that can affect the result especially when the experiment is not controlled. Some of these variables to watch out for is called extraneous variables.

Even though they are not an independent variable, they still affect changes in the outcome of an experiment.

In this article, we are going to discuss extraneous variables and how they affect research.

What are Extraneous Variables

Extraneous variables, also known as confounding variables, are defined as all other variables that could affect the findings of an experiment but are not independent variables.

For example, in research about the impact of sleep deprivation on test performance, the researcher will divide the participants into two groups.

While the first group will be fully rested before taking their test, the second group will be sleep-deprived. After conducting the test, the score of the participants from both groups will then be the dependent variable while sleep will be the independent variable. 

To determine whether there are other factors associated with the test performance, you must consider what causes sleep deprivation.

Sleep deprivation in most cases is caused by stress. Therefore, the test performance of your participants may be caused by stress and that led to sleep deprivation which ultimately has an effect on their score (dependent variable). 

This is why the researcher must ensure that the impact on the dependent variable is caused solely by the manipulation of the independent variable. 

To ascertain this, all other variables that can affect the dependent variable and cause a change must be monitored and controlled.

The researcher must control as many extraneous variables as possible because they may be essential in providing alternative explanations as to why the effect occurred.

Explore: Research Bias: Definition, Types + Examples

Effect of Extraneous Variables

Extraneous variables impact independent variables in two ways. One of these ways is by introducing noise or variability to the data while the other way is by becoming confounding variables.

By becoming confounding variables, the true effect of the independent variable on the dependent variables will be unknown and overshadowed by the confounding variables that are undetected.

This makes extraneous variables a threat because they are likely to have some effect on the dependent variable. For example, there’s a high chance a participants’ health will be affected by many factors except whether or not they write expressively.

It then becomes difficult to distinguish the effect of the independent variables from the effect of the extraneous variables because of these additional factors. 

Extraneous variables tend to provide an entirely unrelated explanation for the changes that occur in your research. This is why it is important to introduce a control method for extraneous variables. This can be done by holding them constant.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

Types of Extraneous Variable

There are four known types of extraneous variables.

1. Demand characteristics

Demand characteristics provide cues that motivate participants to conform to the behavioral expectations of the researcher. Oftentimes, the experimental settings or the research material can give away the intention of the research study to the participants.

The participants can in turn use these cues to behave in ways that are related and consistent with the hypotheses of the study. This can cause bias in the results of the research and lower the external validity of the generalization of the results in the population. 

For example: In an experimental research group, some research participants were asked to put on lab coats. These participants put in more effort to do well in the quiz because they already deduced the questions based on the research settings and their scientific knowledge.

Demand characteristics can be avoided by making it difficult for participants to guess the intention of your research.

Note that in-demand characteristics, the participants can be affected by their environment, the characteristics of the researcher, the nonverbal communication of the researcher, and the participant’s interpretation of the situation.

2. Experimenter or investigator effects

These are unintentional or unknown actions of the researchers that can influence the results of the study. Experimental effects can be divided into two.

One, experimental interaction with the participants which can unintentionally influence the behaviors of the participants and the errors in observation, measurement, analysis, and interpretation by the researcher. These errors can change the results of the research and lead to false conclusions. 

For example, the researcher encourages the participants to wear their lab coats and perform well in the quiz. This act of motivation makes the participants more comfortable in the lab environment and feel confident about going and responding to the quiz questions; therefore, leading them to perform well.

Also, the participants putting on non-lab coats are not encouraged to do well in the quiz. So, they don’t feel obligated to work hard on their responses.

Experimenter effects can be avoided through the introduction or implementation of masking (blinding). This will hide the condition for the assignment from participants and experimenters. You can also make use of a double-blind study to caution researchers from influencing the participants towards acting in expected ways. 

3. Situational variables

Situational variables can affect or change the behaviors of the participants because of the influence of factors such as lighting or temperature. These factors are the sources of random error or random variation in experimental measurements. A reduction in situational factors will show the actual relationship that exists between independent and dependent variables. 

For example: If you need to use school lab rooms to perform your experiment, and they are only available either early in the morning or late in the day. Then there’s a possibility that the time of day may affect the test performance of the participants. This becomes an extraneous variable.

Situational variables can be avoided by holding the variables constant throughout the research. If you conduct the first test in the morning, perform subsequent tests in the morning so that the time of the day factor can be eliminated.

4. Participant or person variables

This is any trait or aspect from the background of the participant that can affect the research results, even when it is not in the interest of the experiment.

These variables include gender, religion, age sex, educational attainment, and marital status. Because these differences can lead to different results in the research participants, it is important to first analyze these factors. 

For example, Participants that have strong educational backgrounds in STEM subjects are most likely to outperform. This is because undergraduate majors are important in educational attainment and can influence the participant variables for your study on scientific reasoning.

You can control participant variables, by using random assignment to divide your sample into control and experimental groups. Here the participants may be influenced by nerves, intelligence, mood, and even anxiety.

How to Control Extraneous Variables

One of the ways you can control extraneous variables is through the use of random sampling. Random sampling will not eliminate the extraneous variable, but it will ensure they are equally distributed between the groups.

If you do not make use of random sampling or other techniques, the effect that an extraneous variable may pose on the research results can be a concern.

Another way to control extraneous variables is through elimination or inclusion. You can eliminate or include extraneous variables that seem to be likely or potential threats in an experiment. Control by elimination means that you will remove the likely extraneous variables by holding them constant in all experimental conditions. 

As against control by elimination, the researcher can include the potential extraneous variables in the research experiment. Instead of eliminating this variable, the researcher can actually include it as a determining factor in the experiment.

For example, if the sex or gender of the counselors is the extraneous variable, instead of eliminating it, the researcher can include this gender across the board for all the counselors. Although it must be evenly done.

This will allow the experiment to measure and analyze the research from the points of the administered treatment, the effect of the counselor’s gender, and the interaction or relationship between both independent variables.

Extraneous Variables vs Confounding Variable

Confounding variables is one of the extraneous variables. According to its name, the work of the confounding variables is to confuse the true effects of the independent variables across all levels. Because just as the independent variables, confounding variables also differ across the conditions that the researcher may introduce.

The confounding variables then provide an alternate explanation to the changes observed in the research study. As the confounding variables influence the dependent variable, it also causally affects the independent variable.

For example, if you have participants who work in scientific labs, they would pose as the confounding variables in your study because their type of work relates to wearing a lab coat and they may have higher scientific knowledge in general. This will make it unlikely that your manipulation will increase the scientific reasoning abilities of these participants.

On the other hand, extraneous variables are those variables that only have an effect on scientific reasoning. They include the interest of the participants in science and undergraduate majors. This is because while a participants’ interest in science may affect his/her scientific reasoning ability, it does not necessarily relate to influencing from wearing a lab coat.

Conclusion

Extraneous variables may become confounding variables and when they are not controlled early enough in a study, they could affect the results of the experimental research.

Without proper control in your experiment population, you may not be able to determine if these variables differ between the groups, whether your results come from your independent variable manipulation, or from the extraneous variables. This can lead to drawing an erroneous conclusion.