In an experiment a variable that is not influenced by any other variable is called mcq

In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, vehicle exhaust is the independent variable while asthma is the dependent variable.  

A confounding variable, or confounder, affects the relationship between the independent and dependent variables. A confounding variable in the example of car exhaust and asthma would be differential exposure to other factors that increase respiratory issues, like cigarette smoke or particulates from factories. Because it would be unethical to expose a randomized group of people to high levels of vehicle exhaust,[1] a study comparing two populations with differential exposure to vehicle exhaust would rely on a natural experiment, or a situation in which this already occurs due to factors unrelated to the researchers. In this natural experiment, a community living near higher concentrations of car exhaust may also live near factories that pollute or have higher rates of smoking.

When running a study or analyzing statistics, researchers try to remove or account for as many of the confounding variables as possible in their study design or analysis. Confounding variables lead to bias, or a factor that may cause an estimate to differ from the true population value. Bias is a systematic error in study design, subject recruitment, data collection, or analysis that results in a mistaken estimate of the true population parameter.[2]

Although there are many types of bias, two common types are selection bias and information bias.  Selection bias occurs when the procedures used to select subjects and others factors that influence participation in the study produce a result that is different from what would have been obtained if all members of the target population were included in the study.[2]  For example, an online website that rates the quality of primary care physicians based on patients’ input may produce ratings that suffer from selection bias.  This is because individuals that had a particularly bad (or good) experience with the physician may be more likely to go to the website and provide a rating. 

Information bias refers to a “systematic error due to inaccurate measurement or classification of disease, exposure, or other variables.”[3]  Recall bias, a type of information bias, occurs when study participants do not remember the information they report accurately or completely.  The subject of confounding and bias relates to a larger discussion of the relationship between correlation and causation.  Although two variables may be correlated, this does not imply that there is a causal relationship between them. 

One way to determine whether a relationship between variables is causal is based on three criteria for research design: temporal precedence meaning that the hypothesized cause happens before the measured effect; covariation of the cause and effect meaning that there is an established relationship between the two variables regardless of causation; and a lack of plausible alternative explanations. Plausible alternative explanations are other factors that may cause the dependent variable under observation.[4]. These alternative explanations are closely related to the concept of internal validity.  

[1]Trochim, W.M.K. “Establishing Cause and Effect.” Research Methods Knowledge Base, 10/20/2006. Web 1/24/2017.
[2] “Bias, Confounding and Effect Modification” Stat 507, Epidemiological Research Methods, Penn State Eberly College of Science, 2017 Web 1/24/17.
[3] Aschengrau A. and G.R. Seage. (2014) Epidemiology in public health. 3rd ed. Burlington, MA: Jones & Bartlett Learning.
[4]. Due to a long history of unethical research in health and social sciences, researchers have many ethical obligations when conducting research, particularly with human subjects. These obligations were first codified in the Nuremburg Code in 1946, which specified that the benefits of research must outweigh the foreseeable risks. Ethical obligations continue to evolve to protect human subjects, including confidentiality and anonymity unless waived and informed consent. Increasingly, communities that have a stake in the outcomes of research are involved in its design and informed of the outcomes of the study. All federally funded research in the United States is subject to review by an Institutional Review Board (IRB).

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1. Experiments
2. Extraneous Variable

Extraneous Variable

By Dr. Saul McLeod, updated 2019

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:

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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Is an experiment a variable that is not influenced by any other variable is called?

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What is an extraneous variable Mcq?