Extraneous Variables – Examples & How to Avoid Them (2024)

Extraneous Variables – In a Nutshell

• Extraneous variables may become confounding variables when not managed.
• Without proper control in your experiment population, one can’t determine if these parameters differ between groups, whether your results are due to the manipulation of your independent variable or extraneous variables.
• Researchers can minimize the impact of potential extraneous variables by utilizing a consistent setting, experimental design, and randomization.

Definition: Extraneous variable

An extraneous variable in an experiment is any variable that is not being investigated but has the potential to influence the results of the experiment.

Uncontrolled extraneous variables can result in erroneous conclusions on the link between the independent and dependent variables.

Use of extraneous variables

Extraneous variables might compromise the internal validity of a study by presenting alternative interpretations of the outcomes.

In an experiment, one manipulates an independent variable to examine its influence on a dependent variable.

Example

In a mental performance study, you investigate whether wearing a white lab coat (your independent variable), promotes scientific reasoning (your dependent variable).

Students from a nearby university are recruited to take part in the research. The independent variable is manipulated by dividing the participants into two groups:

• The experimental group is required to wear lab coats for the duration of the investigation.
• The control group is instructed to wear a casual coat during the duration of the trial.

The scores of all participants on a test of scientific knowledge are compared across groups.

When extraneous factors are uncontrolled, it is difficult to detect the precise impacts of the independent variable on the dependent variable, as the influence of extraneous variables may mask them.

Example

These extraneous variables may influence the science knowledge scores in your experiment:

If these variables consistently differ between groups, you cannot be sure whether your results are due to the manipulation of the independent variable or to the extraneous variables.

Controlling extraneous variables is a crucial component of experimental design. When an extraneous variable is controlled, it becomes a control variable.

Extraneous variables vs. confounding variables

Confounding variables are extraneous variables that are connected to both the independent and dependent variables:

• Extraneous variables may be any variable that potentially affects the dependent variable.
• Confounding variables affect the dependent variable and correlate with or have a causal effect on the independent variable.

Example

Participants who work in scientific professions (in labs) are a confounding variable in your study, as this sort of activity corresponds with lab coat-wearing and superior scientific reasoning.

People who work in laboratories wear lab coats and may have a broader scientific understanding. Therefore, it is improbable that your treatment will improve these participants’ scientific reasoning skills.

Extraneous variables are variables that only affect scientific thinking. These include individuals’ scientific interests and undergraduate majors. Although a passion for science may influence scientific reasoning skills, wearing a lab coat is not necessarily associated with this trait.

Extraneous Variables – Types and controls

There are four known types of extraneous variables:

Demand characteristic

Demand characteristics serve as cues that urge participants to comply with the researcher’s behavioral expectations. Experiment settings and study materials can often give away the research study’s purpose to participants.

The participants can then use these cues to engage in behavior that is relevant to and consistent with the study’s hypothesis. This may introduce bias into the research’s findings and reduce the generalizability of those findings to the population.

Example

The experimental group’s research participants readily make connections between the lab environment, the requirement to wear lab coats, and the questions assessing their scientific knowledge. They exert more significant effort to perform well on the test by paying closer attention to the questions.

You can eliminate demand characteristics by making it challenging for participants to guess the purpose of your study.

Experimenter effects

Experimenter effects are unintended behaviors performed by researchers that can alter the results of a study.

There are two categories of experimenter effects:

• Interactions between experimenters and subjects can unintentionally influence their behaviors.
• The study results may be affected by measurement, observation, analytical, or interpretation errors.

Example

You encourage and motivate the participants in lab coats to perform their best on the test. They perform better because they are more at ease in the lab and more confident when taking the test.

Non-laboratory coat-wearing participants are discouraged from performing well on the test. As a result, they don’t exert as much effort into their responses.

Participants and researchers can be blinded (masked) to avoid the influence of the researcher on their results. In a double-blind study, researchers will be unable to influence participants’ behavior or selectively draw conclusions to support their theories.

Situational variables

Situational variables, like lighting and temperature, can affect the behavior of participants in a study. These variables are causes of random error or fluctuation in your measurements.

You must limit or remove the influence of situational factors on your study outcomes to ascertain the actual relationship between independent and dependent variables.

Example

You conduct your experiment in on-campus laboratories. They are only accessible in the early morning or late afternoon. Given that time of day may influence test performance, it is an extraneous variable.

It is crucial to maintain constant variables throughout the research or statistically account for them to prevent situational variables from impacting study outcomes.

Participant variables

This is an attribute or aspect of the participant’s background that can influence the research outcomes, even when not in the experiment’s best interest.

These variables consist of gender, religion, age, level of education, and marital status. Since these variances can result in varied outcomes for the research participants, it is necessary first to assess them.

Example

Education level and undergraduate majors are critical participant factors for your investigation of scientific thinking. Those having an excellent educational foundation in STEM domains are likely to outperform others.

You can use random assignment to keep track of participant variables when splitting your sample into experimental and control groups. Here, participants may be affected by nerves, intelligence, disposition, and even worry.