What are the two main methods for controlling extraneous variables in experimental research?

By Dr. Saul McLeod, updated 2019

Table of Contents Show

1. Situational Variables
2. Participant / Person Variable
3. Experimenter / Investigator Effects
4. Demand Characteristics
Types and controls of extraneous variables
Demand characteristics
Experimenter effects
Situational variables
Participant variables

When we conduct experiments there are other variables that can affect our results, if we do not control them.

Anything that is not the independent variable that has the potential to affect the results is called an extraneous variable. It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a feature of the environment such as lighting or noise.

The researcher wants to make sure that it is the manipulation of the independent variable that has an effect on the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

There are four types of extraneous variables:

1. Situational Variables

These are aspects of the environment that might affect the participant’s behavior, e.g. noise, temperature, lighting conditions, etc. Situational variables should be controlled so they are the same for all participants.

Standardized procedures are used to ensure that conditions are the same for all participants. This includes the use of standardized instructions

2. Participant / Person Variable

This refers to the ways in which each participant varies from the other, and how this could affect the results e.g. mood, intelligence, anxiety, nerves, concentration etc.

For example, if a participant that has performed a memory test was tired, dyslexic or had poor eyesight, this could effect their performance and the results of the experiment. The experimental design chosen can have an affect on participant variables.

Situational variables also include order effects that can be controlled using counterbalancing, such as giving half the participants condition 'A' first, while the other half get condition 'B' first. This prevents improvement due to practice, or poorer performance due to boredom.

Participant variables can be controlled using random allocation to the conditions of the independent variable.

3. Experimenter / Investigator Effects

The experimenter unconsciously conveys to participants how they should behave - this is called experimenter bias.

The experiment might do this by giving unintentional clues to the participants about what the experiment is about and how they expect them to behave. This affects the participants’ behavior.

The experimenter is often totally unaware of the influence which s/he is exerting and the cues may be very subtle but they may have an influence nevertheless.

Also, the personal attributes (e.g. age, gender, accent, manner etc.) of the experiment can affect the behavior of the participants.

4. Demand Characteristics

Demand characteristics are all the clues in an experiment which convey to the participant the purpose of the research. Demand characteristics can change the results of an experiment if participants change their behavior to conform to expectations.

Participants will be affected by: (i) their surroundings; (ii) the researcher’s characteristics; (iii) the researcher’s behavior (e.g. non-verbal communication), and (iv) their interpretation of what is going on in the situation.

Experimenters should attempt to minimize these factors by keeping the environment as natural as possible, carefully following standardized procedures. Finally, perhaps different experimenters should be used to see if they obtain similar results.

Suppose we wanted to measure the effects of Alcohol (IV) on driving ability (DV) we would have to try to ensure that extraneous variables did not affect the results. These variables could include:

• Familiarity with the car: Some people may drive better because they have driven this make of car before.
• Familiarity with the test: Some people may do better than others because they know what to expect on the test.
• Used to drinking. The effects of alcohol on some people may be less than on others because they are used to drinking.
• Full stomach. The effect of alcohol on some subjects may be less than on others because they have just had a big meal.

If these extraneous variables are not controlled they may become confounding variables, because they could go on to affect the results of the experiment.

How to reference this article:

McLeod, S. A. (2019, July 30). Extraneous variable. Simply Psychology. www.simplypsychology.org/extraneous-variable.html

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In an experiment, an extraneous variable is any variable that you’re not investigating that can potentially affect the outcomes of your research study.

If left uncontrolled, extraneous variables can lead to inaccurate conclusions about the relationship between independent and dependent variables. They can also introduce a variety of research biases to your work, particularly selection bias

Research question	Extraneous variables
Is memory capacity related to test performance?	Test-taking time of day Test anxiety Level of stress
Does sleep deprivation affect driving ability?	Road conditions Years of driving experience Noise
Does light exposure improve learning ability in mice?	Type of mouse Genetic background Learning environment

Extraneous variables can threaten the internal validity of your study by providing alternative explanations for your results.

When not accounted for, these variables can also introduce many biases to your research, particularly types of selection bias such as:

Survivorship bias : when researchers draw conclusions by only focusing on examples of successful individuals (the “survivors”) rather than the group as a whole.

Nonresponse bias: when people who don’t respond to a survey are different in significant ways from those who do.

Undercoverage bias: when some members of your population are not represented in the sample.

In an experiment, you manipulate an independent variable to study its effects on a dependent variable.

Example: Experimental studyIn a study on mental performance, you test whether wearing a white lab coat, your independent variable, improves scientific reasoning, your dependent variable.

You recruit students from a university to participate in the study. You manipulate the independent variable by splitting participants into two groups:

Participants in the experimental group are asked to wear a lab coat during the study.
Participants in the control group are asked to wear a casual coat during the study.

All participants are given a scientific knowledge quiz, and their scores are compared between groups.

When extraneous variables are uncontrolled, it’s hard to determine the exact effects of the independent variable on the dependent variable, because the effects of extraneous variables may mask them.

Uncontrolled extraneous variables can also make it seem as though there is a true effect of the independent variable in an experiment when there’s actually none.

Example: Extraneous variablesIn your experiment, these extraneous variables can affect the science knowledge scores:

Participant’s major (e.g., STEM or humanities)
Participant’s interest in science
Demographic variables such as gender or educational background
Time of day of testing
Experiment environment or setting

If these variables systematically differ between the groups, you can’t be sure whether your results come from your independent variable manipulation or from the extraneous variables.

Controlling extraneous variables is an important aspect of experimental design. When you control an extraneous variable, you turn it into a control variable.

A confounding variable is a type of extraneous variable that is associated with both the independent and dependent variables.

An extraneous variable is anything that could influence the dependent variable.
A confounding variable influences the dependent variable, and also correlates with or causally affects the independent variable.

In a conceptual framework diagram, you can draw an arrow from a confounder to the independent variable as well as to the dependent variable. You can draw an arrow from extraneous variables to a dependent variable.

Example: Confounding vs. extraneous variablesHaving participants who work in scientific professions (in labs) is a confounding variable in your study, because this type of work correlates with wearing a lab coat and better scientific reasoning.

People who work in labs would regularly wear lab coats and may have higher scientific knowledge in general. Therefore, it’s unlikely that your manipulation will increase scientific reasoning abilities for these participants.

Variables that only impact on scientific reasoning are extraneous variables. These include participants’ interests in science and undergraduate majors. While interest in science may affect scientific reasoning ability, it’s not necessarily related to wearing a lab coat.

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Types and controls of extraneous variables

Demand characteristics

Demand characteristics are cues that encourage participants to conform to researchers’ behavioral expectations.

Sometimes, participants can infer the intentions behind a research study from the materials or experimental settings, and use these hints to act in ways that are consistent with study hypotheses. These demand characteristics can bias the study outcomes and reduce the external validity, or generalizability, of the results.

Example: Demand characteristicsResearch participants in the experimental group easily draw links between the lab setting, being asked to wear lab coats, and the questions on their scientific knowledge.

They work harder to do well on the quiz by paying more attention to the questions.

You can avoid demand characteristics by making it difficult for participants to guess the aim of your study. Ask participants to perform unrelated filler tasks or fill out plausibly relevant surveys to lead them away from the true nature of the study.

Experimenter effects

Experimenter effects are unintentional actions by researchers that can influence study outcomes.

There are two main types of experimenter effects:

Experimenters’ interactions with participants can unintentionally affect their behaviours.
Errors in measurement, observation, analysis, or interpretation may change the study results.

Example: Experimenter effectYou motivate and encourage the participants wearing the lab coats to do their best on the quiz. They are more comfortable in the lab environment and feel confident going into the quiz; therefore, they perform well.

Participants wearing the non-lab coats are not encouraged to perform well on the quiz. Therefore, they don’t work as hard on their responses.

To avoid experimenter effects, you can implement masking (blinding) to hide the condition assignment from participants and experimenters. In a double-blind study, researchers won’t be able to bias participants towards acting in expected ways or selectively interpret results to suit their hypotheses.

Situational variables

Situational variables, such as lighting or temperature, can alter participants’ behaviors in study environments. These factors are sources of random error or random variation in your measurements.

To understand the true relationship between independent and dependent variables, you’ll need to reduce or eliminate the effect of situational factors on your study outcomes.

Example: Situational variableTo perform your experiment, you use the lab rooms on campus. They are only available either early in the morning or late in the day. Because time of day may affect test performance, it’s an extraneous variable.

To avoid situational variables from influencing study outcomes, it’s best to hold variables constant throughout the study or statistically account for them in your analyses.

Participant variables

A participant variable is any characteristic or aspect of a participant’s background that could affect study results, even though it’s not the focus of an experiment.

Participant variables can include sex, gender identity, age, educational attainment, marital status, religious affiliation, etc.

Since these individual differences between participants may lead to different outcomes, it’s important to measure and analyze these variables.

Example: Participant variablesEducational background and undergraduate majors are important participant variables for your study on scientific reasoning. Participants with strong educational backgrounds in STEM subjects are likely to perform better than others.

To control participant variables, you should aim to use random assignment to divide your sample into control and experimental groups. Random assignment makes your groups comparable by evenly distributing participant characteristics between them.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables:

Demand characteristics: environmental cues that encourage participants to conform to researchers’ expectations.
Experimenter effects: unintentional actions by researchers that influence study outcomes.
Situational variables: environmental variables that alter participants’ behaviors.
Participant variables: any characteristic or aspect of a participant’s background that could affect study results.

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Bhandari, P. (2022, December 05). Extraneous Variables | Examples, Types & Controls. Scribbr. Retrieved December 8, 2022, from https://www.scribbr.com/methodology/extraneous-variables/