How to control extraneous variables scholarly articles

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 questionExtraneous variablesIs 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

Table of contents

Why do extraneous variables matter?

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

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

• Sampling bias or ascertainment bias: when some members of the intended population are less likely to be included than others.

• Attrition bias: when participants who drop out of a study are systematically different from those who stay.

• 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.

Extraneous vs. confounding variables

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 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.

Frequently asked questions about extraneous variables

What’s the difference between extraneous and confounding variables?

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.

What are the types of extraneous variables?

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.

Why are control variables important?

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity.

If you don’t control relevant extraneous variables, they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable.

What does “controlling for a variable” mean?

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

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How to control extraneous variables in research scholarly articles?

How to control extraneous variables.

Determine which type of extraneous variables are present in your study. As you plan your study, consider analyzing each part of the research process to determine if any extraneous variables may appear. ... .

Select a method of control. ... .

Implement the method of control..

What is the most effective method of controlling extraneous variables?

Random assignment is the most important technique used to control for confounding variables because it has the ability to control for both known and unknown confounding extraneous variables. Because of this characteristic, you should randomly assign whenever and wherever possible.

What is the common way to control the extraneous variable in research?

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables. Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs.

What are the three general ways of controlling extraneous variables?

By using a consistent environment, experimental design, and randomization, researchers can minimize the effect that potential extraneous variables can have on experiment.