What is the variable that has a potential effect on the dependent variable but is not part of the research study?

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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What Is a Dependent Variable?

The dependent variable is the variable that is being measured or tested in an experiment. For example, in a study looking at how tutoring impacts test scores, the dependent variable would be the participants' test scores since that is what is being measured.

This is different than the independent variable in an experiment, which is a variable that stands on its own. In the example above, the independent variable would be tutoring. The independent variable (tutoring) doesn't change based on other variables, but the dependent variable (test scores) may.

One way to help identify the dependent variable is to remember that it depends on the independent variable. When researchers make changes to the independent variable, they then measure any resulting changes to the dependent variable.

The dependent variable is called "dependent" because it is thought to depend, in some way, on the variations of the independent variable.

Independent vs. Dependent Variable

In a psychology experiment, researchers study how changes in one variable (the independent variable) change another variable (the dependent variable). Manipulating independent variables and measuring the effect on dependent variables allows researchers to draw conclusions about cause-and-effect relationships.

These experiments can range from simple to quite complicated, so it can sometimes be a bit confusing to know how to identify the independent vs. dependent variables. Here are a couple of questions to ask to help you learn which is which.

Which Variable Is the Experimenter Measuring?

Keep in mind that the dependent variable is the one being measured. So, if the experiment is trying to see how one variable affects another, the variable that is being affected is the dependent variable.

In many psychology experiments and studies, the dependent variable is a measure of a certain aspect of a participant's behavior. In an experiment looking at how sleep affects test performance, the dependent variable would be test performance.

Which Variable Does the Experimenter Manipulate?

The independent variable is "independent" because the experimenters are free to vary it as they need. This might mean changing the amount, duration, or type of variable that the participants in the study receive as a treatment or condition.

For example, it's common for treatment-based studies to have some subjects receive a certain treatment while others receive no treatment at all. In this case, the treatment is an independent variable because it is the one being manipulated or changed.

Independent Variable

• Variable being manipulated

• Doesn't change based on other variables

• Stands on its own

Dependent Variable

• Variable being measured

• May change based on other variables

• Depends on other variables

How do researchers determine what will be a good dependent variable? There are a few key features that a scientist might consider.

Stability

Stability is often a good sign of a higher quality dependent variable. If the experiment is repeated with the same participants, conditions, and experimental manipulations, the effects on the dependent variable should be very close to what they were the first time around.

Complexity

A researcher might also choose dependent variables based on the complexity of their study. While some studies only have one dependent variable and one independent variable, it is possible to have several of each type.

Researchers might also want to learn how changes in a single independent variable affect several dependent variables. For example, imagine an experiment where a researcher wants to learn how the messiness of a room influences people's creativity levels.

This research might also want to see how the messiness of a room might influence a person's mood. The messiness of a room would be the independent variable and the study would have two dependent variables: level of creativity and mood.

Ability to Operationalize

Operationalization is defined as "translating a construct into its manifestation." In simple terms, it refers to how a variable will be measured. So, a good dependent variable is one that you are able to measure.

If measuring burnout, for instance, researchers might decide to use the Maslach Burnout Inventory. If measuring depression, they could use the Patient Health Questionnaire-9 (PHQ-9).

Dependent Variable Examples

As you are learning to identify the dependent variables in an experiment, it can be helpful to look at examples. Here are just a few dependent variable examples in psychology research.

• How does the amount of time spent studying influence test scores? The test scores would be the dependent variable and the amount of studying would be the independent variable. The researcher could also change the independent variable by instead evaluating how age or gender influences test scores.
• How does stress influence memory? The dependent variable might be scores on a memory test and the independent variable might be exposure to a stressful task.
• How does a specific therapeutic technique influence the symptoms of psychological disorders? In this case, the dependent variable might be defined as the severity of the symptoms a patient is experiencing, while the independent variable would be the use of a specific therapy method.
• Does listening to classical music help students perform better on a math exam? The scores on the math exams are the dependent variable and classical music is the independent variable.
• How long does it take people to respond to different sounds? The length of time it takes participants to respond to a sound is the dependent variable, while the sounds are the independent variable.
• Do first-born children learn to speak at a younger age than second-born children? In this example, the dependent variable is the age at which the child learns to speak and the independent variable is whether the child is first- or second-born.
• How does alcohol use influence reaction time while driving? The amount of alcohol a participant ingests is the independent variable, while their performance on the driving test is the dependent variable.

A Word From Verywell

Understanding what a dependent variable is and how it is used can be helpful for interpreting different types of research that you encounter in different settings. When you are trying to determine which variables are which, remember that the independent variables are the cause while the dependent variables are the effect.

Frequently Asked Questions

• What does the dependent variable depend on?

  The dependent variable depends on the independent variable. Thus, if the independent variable changes, the dependent variable would likely change too.

• Where does the dependent variable go on a graph?

  The dependent variable is placed on a graph's y-axis. This is the vertical line or the line that extends upward. The independent variable is placed on the graph's x-axis or the horizontal line.

• How do you find a dependent variable?

• What is a controlled variable?

  A controlled variable is a variable that doesn't change during the experiment. This enables researchers to assess the relationship between the dependent and independent variables more accurately. For example, if trying to assess the impact of drinking green tea on memory, researchers might ask subjects to drink it at the same time of day. This would be a controlled variable.

Verywell Mind uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy.

1. U.S. National Library of Medicine. Dependent and independent variables.

2. Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders. Clin Interv Aging. 2015;10:1189-1199. doi:10.2147/CIA.S81868

3. Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J. 2019;10(1):82-86. doi:10.4103/idoj.IDOJ_468_18

4. Flannelly LT, Flannelly KJ, Jankowski KR. Independent, dependent, and other variables in healthcare and chaplaincy research. J Health Care Chaplain. 2014;20(4):161-70. doi:10.1080/08854726.2014.959374

5. Weiten W. Psychology: Themes and Variations. Cengage Learning.

6. Roediger HL, Elmes DG, Kantowitz BH. Experimental Psychology. Cengage Learning.

7. Vassar M, Matthew H. The retrospective chart review: important methodological considerations. J Educ Eval Health Prof. 2013;10:12. doi:10.3352/jeehp.2013.10.12

By Kendra Cherry
Kendra Cherry, MS, is an author and educational consultant focused on helping students learn about psychology.

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