example of independent and dependent variables in an experiment

15 Independent and Dependent Variable Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).

By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.

This can provide very valuable information when studying just about any subject.

Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.

The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.

Definition of Independent and Dependent Variables

The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .

Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.

The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).

Other variations of an experiment might include having multiple levels of the independent variable.

If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.  

Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.

These concepts should not be confused with predictor and outcome variables .

Examples of Independent and Dependent Variables

1. gatorade and improved athletic performance.

A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.

If they can back up that claim with hard scientific data, that would be great for sales.

So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.

All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.

In this example, the independent variable is Gatorade, and the dependent variable is heart rate.  

2. Chemotherapy and Cancer

A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.

The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.

Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.

In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.

3. Interior Design Color and Eating Rate

A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.

So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.

Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.

The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.

4. Hair Color and Attraction

A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.

Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.

At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.

The independent variable is hair color, and the dependent variable is pupil dilation.

5. Mozart and Math

After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.

During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.

The researchers then compare the scores of the exams between the two groups of classrooms.

Although there are a lot of obvious limitations to this hypothetical, it is the first step.

The independent variable is Mozart, and the dependent variable is exam scores.

6. Essential Oils and Sleep

A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.

The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.

At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.

The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.

7. Teaching Style and Learning

A group of teachers is interested in which teaching method will work best for developing critical thinking skills.

So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.

At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.

The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.

8. Concrete Mix and Bridge Strength

A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.

So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.

In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.

9. Recipe and Consumer Preferences

People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.

The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.

Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.

The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.

10. Protein Supplements and Muscle Mass

A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.

The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.

They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.

At the end of three months, the muscle mass of all participants is measured.

The independent variable is the supplement, and the dependent variable is muscle mass.  

11. Air Bags and Skull Fractures

In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.

In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.

The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.

The independent variable was the airbag and the dependent variable was the amount of skull damage.

12. Vitamins and Health

Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.

They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”

Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.

In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.

At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.

In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.

13. Meditation and Stress

Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.

All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.

Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.

How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.

In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).

14. Video Games and Aggression

When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.

Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.

Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.

So many studies have used so many different ways of measuring aggression.

In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.

15. Vehicle Exhaust and Cognitive Performance

Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.

One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.

After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.

One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.

In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.

Read Next: Extraneous Variables Examples

The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.

For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.

The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.

Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941

Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374

Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.

Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66.  https://doi.org/10.1080/10790195.2012.10850354

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Theory of Planned Behavior Examples

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Independent and Dependent Variables

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

Related Articles

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Phenomenology In Qualitative Research

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Metasynthesis Of Qualitative Research

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Statistics By Jim

Making statistics intuitive

Independent and Dependent Variables: Differences & Examples

By Jim Frost 15 Comments

In this post, learn the definitions of independent and dependent variables, how to identify each type, how they differ between different types of studies, and see examples of them in use.

What is an Independent Variable?

Independent variables (IVs) are the ones that you include in the model to explain or predict changes in the dependent variable. The name helps you understand their role in statistical analysis. These variables are independent . In this context, independent indicates that they stand alone and other variables in the model do not influence them. The researchers are not seeking to understand what causes the independent variables to change.

Independent variables are also known as predictors, factors , treatment variables, explanatory variables, input variables, x-variables, and right-hand variables—because they appear on the right side of the equals sign in a regression equation. In notation, statisticians commonly denote them using Xs. On graphs, analysts place independent variables on the horizontal, or X, axis.

In machine learning, independent variables are known as features.

For example, in a plant growth study, the independent variables might be soil moisture (continuous) and type of fertilizer (categorical).

Statistical models will estimate effect sizes for the independent variables.

Relate post : Effect Sizes in Statistics

Including independent variables in studies

The nature of independent variables changes based on the type of experiment or study:

Controlled experiments : Researchers systematically control and set the values of the independent variables. In randomized experiments, relationships between independent and dependent variables tend to be causal. The independent variables cause changes in the dependent variable.

Observational studies : Researchers do not set the values of the explanatory variables but instead observe them in their natural environment. When the independent and dependent variables are correlated, those relationships might not be causal.

When you include one independent variable in a regression model, you are performing simple regression. For more than one independent variable, it is multiple regression. Despite the different names, it’s really the same analysis with the same interpretations and assumptions.

Determining which IVs to include in a statistical model is known as model specification. That process involves in-depth research and many subject-area, theoretical, and statistical considerations. At its most basic level, you’ll want to include the predictors you are specifically assessing in your study and confounding variables that will bias your results if you don’t add them—particularly for observational studies.

For more information about choosing independent variables, read my post about Specifying the Correct Regression Model .

Related posts : Randomized Experiments , Observational Studies , Covariates , and Confounding Variables

What is a Dependent Variable?

The dependent variable (DV) is what you want to use the model to explain or predict. The values of this variable depend on other variables. It is the outcome that you’re studying. It’s also known as the response variable, outcome variable, and left-hand variable. Statisticians commonly denote them using a Y. Traditionally, graphs place dependent variables on the vertical, or Y, axis.

For example, in the plant growth study example, a measure of plant growth is the dependent variable. That is the outcome of the experiment, and we want to determine what affects it.

How to Identify Independent and Dependent Variables

If you’re reading a study’s write-up, how do you distinguish independent variables from dependent variables? Here are some tips!

Identifying IVs

How statisticians discuss independent variables changes depending on the field of study and type of experiment.

In randomized experiments, look for the following descriptions to identify the independent variables:

• Independent variables cause changes in another variable.
• The researchers control the values of the independent variables. They are controlled or manipulated variables.
• Experiments often refer to them as factors or experimental factors. In areas such as medicine, they might be risk factors.
• Treatment and control groups are always independent variables. In this case, the independent variable is a categorical grouping variable that defines the experimental groups to which participants belong. Each group is a level of that variable.

In observational studies, independent variables are a bit different. While the researchers likely want to establish causation, that’s harder to do with this type of study, so they often won’t use the word “cause.” They also don’t set the values of the predictors. Some independent variables are the experiment’s focus, while others help keep the experimental results valid.

Here’s how to recognize independent variables in observational studies:

• IVs explain the variability, predict, or correlate with changes in the dependent variable.
• Researchers in observational studies must include confounding variables (i.e., confounders) to keep the statistical results valid even if they are not the primary interest of the study. For example, these might include the participants’ socio-economic status or other background information that the researchers aren’t focused on but can explain some of the dependent variable’s variability.
• The results are adjusted or controlled for by a variable.

Regardless of the study type, if you see an estimated effect size, it is an independent variable.

Identifying DVs

Dependent variables are the outcome. The IVs explain the variability or causes changes in the DV. Focus on the “depends” aspect. The value of the dependent variable depends on the IVs. If Y depends on X, then Y is the dependent variable. This aspect applies to both randomized experiments and observational studies.

In an observational study about the effects of smoking, the researchers observe the subjects’ smoking status (smoker/non-smoker) and their lung cancer rates. It’s an observational study because they cannot randomly assign subjects to either the smoking or non-smoking group. In this study, the researchers want to know whether lung cancer rates depend on smoking status. Therefore, the lung cancer rate is the dependent variable.

In a randomized COVID-19 vaccine experiment , the researchers randomly assign subjects to the treatment or control group. They want to determine whether COVID-19 infection rates depend on vaccination status. Hence, the infection rate is the DV.

Note that a variable can be an independent variable in one study but a dependent variable in another. It depends on the context.

For example, one study might assess how the amount of exercise (IV) affects health (DV). However, another study might study the factors (IVs) that influence how much someone exercises (DV). The amount of exercise is an independent variable in one study but a dependent variable in the other!

How Analyses Use IVs and DVs

Regression analysis and ANOVA mathematically describe the relationships between each independent variable and the dependent variable. Typically, you want to determine how changes in one or more predictors associate with changes in the dependent variable. These analyses estimate an effect size for each independent variable.

Suppose researchers study the relationship between wattage, several types of filaments, and the output from a light bulb. In this study, light output is the dependent variable because it depends on the other two variables. Wattage (continuous) and filament type (categorical) are the independent variables.

After performing the regression analysis, the researchers will understand the nature of the relationship between these variables. How much does the light output increase on average for each additional watt? Does the mean light output differ by filament types? They will also learn whether these effects are statistically significant.

Related post : When to Use Regression Analysis

Graphing Independent and Dependent Variables

As I mentioned earlier, graphs traditionally display the independent variables on the horizontal X-axis and the dependent variable on the vertical Y-axis. The type of graph depends on the nature of the variables. Here are a couple of examples.

Suppose you experiment to determine whether various teaching methods affect learning outcomes. Teaching method is a categorical predictor that defines the experimental groups. To display this type of data, you can use a boxplot, as shown below.

The groups are along the horizontal axis, while the dependent variable, learning outcomes, is on the vertical. From the graph, method 4 has the best results. A one-way ANOVA will tell you whether these results are statistically significant. Learn more about interpreting boxplots .

Now, imagine that you are studying people’s height and weight. Specifically, do height increases cause weight to increase? Consequently, height is the independent variable on the horizontal axis, and weight is the dependent variable on the vertical axis. You can use a scatterplot to display this type of data.

It appears that as height increases, weight tends to increase. Regression analysis will tell you if these results are statistically significant. Learn more about interpreting scatterplots .

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Reader Interactions

April 2, 2024 at 2:05 am

Hi again Jim

Thanks so much for taking an interest in New Zealand’s Equity Index.

Rather than me trying to explain what our Ministry of Education has done, here is a link to a fairly short paper. Scroll down to page 4 of this (if you have the inclination) – https://fyi.org.nz/request/21253/response/80708/attach/4/1301098%20Response%20and%20Appendix.pdf

The Equity Index is used to allocate only 4% of total school funding. The most advantaged 5% of schools get no “equity funding” and the other 95% get a share of the equity funding pool based on their index score. We are talking a maximum of around $1,000NZD per child per year for the most disadvantaged schools. The average amount is around $200-$300 per child per year.

My concern is that I thought the dependent variable is the thing you want to explain or predict using one or more independent variables. Choosing the form of dependent variable that gets a good fit seems to be answering the question “what can we predict well?” rather than “how do we best predict the factor of interest?” The factor is educational achievement and I think this should have been decided upon using theory rather than experimentation with the data.

As it turns out, the Ministry has chosen a measure of educational achievement that puts a heavy weight on achieving an “excellence” rating on a qualification and a much lower weight on simply gaining a qualification. My reading is that they have taken what our universities do when looking at which students to admit.

It doesn’t seem likely to me that a heavy weighting on excellent achievement is appropriate for targeting extra funding to schools with a lot of under-achieving students.

However, my stats knowledge isn’t extensive and it’s definitely rusty, so your thoughts are most helpful.

Regards Kathy Spencer

April 1, 2024 at 4:08 pm

Hi Jim, Great website, thank you.

I have been looking at New Zealand’s Equity Index which is used to allocate a small amount of extra funding to schools attended by children from disadvantaged backgrounds. The Index uses 37 socioeconomic measures relating to a child’s and their parents’ backgrounds that are found to be associated with educational achievement.

I was a bit surprised to read how they had decided on the dependent variable to be used as the measure of educational achievement, or dependent variable. Part of the process was as follows- “Each measure was tested to see the degree to which it could be predicted by the socioeconomic factors selected for the Equity Index.”

Any comment?

Many thanks Kathy Spencer

April 1, 2024 at 9:20 pm

That’s a very complex study and I don’t know much about it. So, that limits what I can say about it. But I’ll give you a few thoughts that come to mind.

This method is common in educational and social research, particularly when the goal is to understand or mitigate the impact of socioeconomic disparities on educational outcomes.

There are the usual concerns about not confusing correlation with causation. However, because this program seems to quantify barriers and then provide extra funding based on the index, I don’t think that’s a problem. They’re not attempting to adjust the socioeconomic measures so no worries about whether they’re directly causal or not.

I might have a small concern about cherry picking the model that happens to maximize the R-squared. Chasing the R-squared rather than having theory drive model selecting is often problematic. Chasing the best fit increases the likelihood that the model fits this specific dataset best by random chance rather than being truly the best. If so, it won’t perform as well outside the dataset used to fit the model. Hopefully, they validated the predicted ability of the model using other data.

However, I’m not sure if the extra funding is determined by the model? I don’t know if the index value is calculated separately outside the candidate models and then fed into the various models. Or does the choice of model affect how the index value is calculated? If it’s the former, then the funding doesn’t depend on a potentially cherry picked model. If the latter, it does.

So, I’m not really clear on the purpose of the model. I’m guessing they just want to validate their Equity Index. And maximizing the R-squared doesn’t really say it’s the best Index but it does at least show that it likely has some merit. I’d be curious how the took the 37 measures and combined them to one index. So, I have more questions than answers. I don’t mean that in a critical sense. Just that I know almost nothing about this program.

I’m curious, what was the outcome they picked? How high was the R-squared? And what were your concerns?

February 6, 2024 at 6:57 pm

Excellent explanation, thank you.

February 5, 2024 at 5:04 pm

Thank you for this insightful blog. Is it valid to use a dependent variable delivered from the mean of independent variables in multiple regression if you want to evaluate the influence of each unique independent variable on the dependent variables?

February 5, 2024 at 11:11 pm

It’s difficult to answer your question because I’m not sure what you mean that the DV is “delivered from the mean of IVs.” If you mean that multiple IVs explain changes in the DV’s mean, yes, that’s the standard use for multiple regression.

If you mean something else, please explain in further detail. Thanks!

February 6, 2024 at 6:32 am

What I meant is; the DV values used as parameters for multiple regression is basically calculated as the average of the IVs. For instance:

From 3 IVs (X1, X2, X3), Y is delivered as :

Y = (Sum of all IVs) / (3)

Then the resulting Y is used as the DV along with the initial IVs to compute the multiple regression.

February 6, 2024 at 2:17 pm

There are a couple of reasons why you shouldn’t do that.

For starters, Y-hat (the predicted value of the regression equation) is the mean of the DV given specific values of the IV. However, that mean is calculated by using the regression coefficients and constant in the regression equation. You don’t calculate the DV mean as the sum of the IVs divided by the number of IVs. Perhaps given a very specific subject-area context, using this approach might seem to make sense but there are other problems.

A critical problem is that the Y is now calculated using the IVs. Instead, the DVs should be measured outcomes and not calculated from IVs. This violates regression assumptions and produces questionable results.

Additionally, it complicates the interpretation. Because the DV is calculated from the IV, you know the regression analysis will find a relationship between them. But you have no idea if that relationship exists in the real world. This complication occurs because your results are based on forcing the DV to equal a function of the IVs and do not reflect real-world outcomes.

In short, DVs should be real-world outcomes that you measure! And be sure to keep your IVs and DV independent. Let the regression analysis estimate the regression equation from your data that contains measured DVs. Don’t use a function to force the DV to equal some function of the IVs because that’s the opposite direction of how regression works!

I hope that helps!

September 6, 2022 at 7:43 pm

Thank you for sharing.

March 3, 2022 at 1:59 am

Excellent explanation.

February 13, 2022 at 12:31 pm

Thanks a lot for creating this excellent blog. This is my go-to resource for Statistics.

I had been pondering over a question for sometime, it would be great if you could shed some light on this.

In linear and non-linear regression, should the distribution of independent and dependent variables be unskewed? When is there a need to transform the data (say, Box-Cox transformation), and do we transform the independent variables as well?

October 28, 2021 at 12:55 pm

If I use a independent variable (X) and it displays a low p-value <.05, why is it if I introduce another independent variable to regression the coefficient and p-value of Y that I used in first regression changes to look insignificant? The second variable that I introduced has a low p-value in regression.

October 29, 2021 at 11:22 pm

Keep in mind that the significance of each IV is calculated after accounting for the variance of all the other variables in the model, assuming you’re using the standard adjusted sums of squares rather than sequential sums of squares. The sums of squares (SS) is a measure of how much dependent variable variability that each IV accounts for. In the illustration below, I’ll assume you’re using the standard of adjusted SS.

So, let’s say that originally you have X1 in the model along with some other IVs. Your model estimates the significance of X1 after assessing the variability that the other IVs account for and finds that X1 is significant. Now, you add X2 to the model in addition to X1 and the other IVs. Now, when assessing X1, the model accounts for the variability of the IVs including the newly added X2. And apparently X2 explains a good portion of the variability. X1 is no longer able to account for that variability, which causes it to not be statistically significant.

In other words, X2 explains some of the variability that X1 previously explained. Because X1 no longer explains it, it is no longer significant.

Additionally, the significance of IVs is more likely to change when you add or remove IVs that are correlated. Correlated IVs is known as multicollinearity. Multicollinearity can be a problem when you have too much. Given the change in significance, I’d check your model for multicollinearity just to be safe! Click the link to read a post that wrote about that!

September 6, 2021 at 8:35 am

nice explanation

August 25, 2021 at 3:09 am

it is excellent explanation

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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Methodology

• Types of Variables in Research & Statistics | Examples

Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts
• Categorical data represents groupings

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables .

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

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Statistics Made Easy

Independent vs. Dependent Variables: What’s the Difference?

In an experiment, there are two main variables:

The independent variable:  the variable that an experimenter changes or controls so that they can observe the effects on the dependent variable.

The dependent variable:  the variable being measured in an experiment that is “dependent” on the independent variable.

In an experiment, an experimenter is interested in seeing how the dependent variable changes as a result of the independent being changed or manipulated in some way.

Example of an Independent and Dependent Variable

For example, a researcher might change the amount of water they provide to a certain plant to observe how it affects the growth rate of the plant.

In this example, the amount of water given to the plant is controlled by the researcher and, thus, is the independent variable . The growth rate is the dependent variable because it is directly dependent on the amount of water that the plant receives and it’s the variable we’re interested in measuring.

How to Remember the Difference Between Independent and Dependent Variables

An easy way to remember the difference between independent and dependent variables is to insert the two variables into the following sentence in such a way that it makes sense:

Changing (independent variable) affects the value of (dependent variable) .

For example, it would make sense to say:

Changing the amount of water  affects the value of  the plant growth rate .

This is how we know that amount of water is the independent variable and plant growth rate is the dependent variable.

If we tried reversing the positions of these two variables, the sentence wouldn’t make sense:

Changing the plant growth rate  affects the value of  the amount of water .

Thus, we know that we must have the independent and dependent variables switched around.

More Examples 

Here are a few more examples of independent and dependent variables.

A marketer changes the amount of money they spend on advertisements to see how it affects total sales.

Independent variable:  amount spent on advertisements

Dependent variable:  total sales

A doctor changes the dose of a particular medicine to see how it affects the blood pressure of a patient.

Independent variable:  dosage level of medicine

Dependent variable:  blood pressure

A researcher changes the version of a study guide given to students to see how it affects exam scores.

Independent variable:  the version of the study guide

Dependent variable:  exam scores

Independent vs. Dependent Variables on a Graph

When we create a graph, the independent variable will go on the x-axis and the dependent variable will go on the y-axis.

For example, suppose a researcher provides different amounts of water for 20 different plants and measures the growth rate of each plant. The following scatterplot shows the amount of water and the growth rate for each plant:

The independent variable (amount of water) is shown on the x-axis while the dependent variable (growth rate) is shown on the y-axis:

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Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

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Difference Between Independent and Dependent Variables

Independent vs. Dependent Variables

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The two main variables in a scientific experiment are the independent and dependent variables. An independent variable is changed or controlled in a scientific experiment to test the effects on another variable. This variable being tested and measured is called the dependent variable.

As its name suggests, the dependent variable is "dependent" on the independent variable. As the experimenter changes the independent variable, the effect on the dependent variable is observed and recorded.

Key Takeaways

• There can be many variables in an experiment, but the two key variables that are always present are the independent and dependent variables.
• The independent variable is the one the researcher intentionally changes or controls.
• The dependent variable is the factor that the research measures. It changes in response to the independent variable; in other words, it depends on it.
• Examples of Independent and Dependent Variables

Let's say a scientist wants to see if the brightness of light has any effect on a moth's attraction to the light. The brightness of the light is controlled by the scientist. This would be the independent variable . How the moth reacts to the different light levels (such as its distance to the light source) would be the dependent variable .

As another example, say you want to know whether eating breakfast affects student test scores. The factor under the experimenter's control is the presence or absence of breakfast, so you know it is the independent variable. The experiment measures test scores of students who ate breakfast versus those who did not. Theoretically, the test results depend on breakfast, so the test results are the dependent variable. Note that test scores are the dependent variable even if it turns out there is no relationship between scores and breakfast.

For another experiment, a scientist wants to determine whether one drug is more effective than another at controlling high blood pressure. The independent variable is the drug, while the patient's blood pressure is the dependent variable. In some ways, this experiment resembles the one with breakfast and test scores. However, when comparing two different treatments, such as drug A and drug B, it's usual to add another variable, called the control variable. The control variable , which in this case is a placebo that contains the same inactive ingredients as the drugs, makes it possible to tell whether either drug actually affects blood pressure.

How to Tell Independent and Dependent Variables Apart

The independent and dependent variables in an experiment may be viewed in terms of cause and effect. If the independent variable is changed, then an effect is seen, or measured, in the dependent variable. Remember, the values of both variables may change in an experiment and are recorded. The difference is that the value of the independent variable is controlled by the experimenter, while the value of the dependent variable only changes in response to the independent variable.

Remembering Variables and How to Plot Them

When results are plotted in graphs, the convention is to use the independent variable as the x-axis and the dependent variable as the y-axis. The DRY MIX acronym can help keep the variables straight:

D is the dependent variable R is the responding variable Y is the axis on which the dependent or responding variable is graphed (the vertical axis)

M is the manipulated variable or the one that is changed in an experiment I is the independent variable X is the axis on which the independent or manipulated variable is graphed (the horizontal axis)

• Null Hypothesis Examples
• What Is a Dependent Variable?
• Popular Math Terms and Definitions
• Independent Variable Definition and Examples
• Dependent Variable Definition and Examples
• Scientific Variable
• Scientific Method Vocabulary Terms
• DRY MIX Experiment Variables Acronym
• What Is a Variable in Science?
• The Difference Between Control Group and Experimental Group
• What Is an Experiment? Definition and Design
• The Differences Between Explanatory and Response Variables
• Six Steps of the Scientific Method
• Understanding Experimental Groups
• The Role of a Controlled Variable in an Experiment

Microbe Notes

Independent vs. Dependent variables: 10 Differences, Examples

Table of Contents

Interesting Science Videos

Independent Variables

The Independent variable is a type of variable used in experimental sciences, statistical modeling, and mathematical modeling which doesn’t depend on any other variables in the scope of the experiment.

• An independent variable can be manipulated in an experiment, which in turn affects the changes in the dependent variables.
• Mostly in mathematical equations, independent variables are denoted by ‘x’.
• Independent variables are also termed as “explanatory variables,” “manipulated variables,” or “controlled variables.”
• In a graph, the independent variable is usually plotted on the X-axis.
• Independent variables are mostly used in experiments to determine their effects on other dependent variables.
• However, in other cases where their influence is not of primary importance, they are used to account for their potential confounding effect.
• The concept of independent variables might differ from one sector to another. In statistics, independent variables are those that are manipulated by the experimenter.
• In research, independent variables are the variables that are selected to determine their possible effects on other variables being studied.
• Independent variables are required for the existence or study of any dependent variable. Also, one independent variable might affect two different dependent variables.
• These variables are used in experiments to study the cause-effect relationship where changes in independent variables make up the ‘cause’ part of the experiment.
• In an experiment testing the behavior of moths to light and dark by turning the light on and off, the light is the independent variable.
• In a study determining the effects of temperature on the plant pigmentation, the temperature is the independent variable.

Dependent variables

The dependent variable is a type of variable used in experimental sciences, statistical modeling, and mathematical modeling which depends on any other variables in the scope of the experiment.

• A dependent variable cannot be manipulated by the experimenter as the changes are brought by the independent variables.
• Mostly in mathematical equations, dependent variables are denoted by ‘y’.
• Dependent variables are also termed as “measured variable,” the “responding variable,” or the “explained variable”.
• In a graph, dependent variables are usually plotted on the Y-axis.
• Dependent variables are used in experiments to study their values under the supposition or hypothesis that they depend on, by some law or rule, on other variables, called independent variables.
• Most experiments are involved in the observation of changes or variations in the dependent variables.
• The concept of dependent variables might also differ from one sector to another. In statistics, dependent variables are those that are expected to change when the independent variables are manipulated.
• A dependent variable responds to the one or many independent variables and thus, ‘depends’ on those variables.
• Dependent variables cannot exist without independent variables, and one dependent variable can only be affected by one independent variable during one study.
• These variables are used in experiments to study the cause-effect relationship where changes in dependent variables caused due to independent variables make up the ‘effect’ part of the experiment.
• The effect on the dependent variable forms the basis of any experiment.
• In an experiment testing the behavior of moths to light and dark by turning the light on and off, the behavior of moths towards the light is the dependent variable.
• In a study determining the effects of temperature on the plant pigmentation, the changes in plant pigmentation as a response to temperature is the dependent variable.

Key Differences (Independent variable vs Dependent variables)

Examples of independent variables

Non-living variables.

• In experiments, it is easier to implement non-living independent variables as the manipulation of such variables is easier.
• Examples of independent variables can be studied in a study testing two different smoothing processes on four different brands of dental cement.
• In the study mentioned above, the independent variables can be the application method and materials, light-curing intensities on the cement, specimen storage (temperature and duration), the length of time of the polishing process, the settings of the electron microscope, and the rotation speed of the polishing device.
• The result of changes in any one of these variables on the process is the basis for this experiment.

Living variables

• Living variables are much complex and thus more challenging to control.
• Due to these reasons, most experiments use simpler organisms like microbes, insects, and rats to generate the results.
• These experiments are only used on human samples once all the results from the study on simpler animals are obtained.
• It is also important to limit other variables not being studied as they might cause changes in independent and dependent variables.
• Some common independent variables in living systems include age group, gender, or body mass index.

Examples of dependent variables

Recovery of patients.

• In a study to determine the effectiveness of drugs on the recovery of patients suffering from cold, the rate of recovery of the patients is the dependent variable.
• Here, half of the patients are given the drugs while the rest are not. Then, the rate of recovery of the patients taking the drugs and those not taking the drugs are observed.
• If the rate of recovery in patients taking the drug is higher than those not taking the drugs, the drug is deemed effective.
• However, if the rates of recovery are the same in both cases, the drug is deemed ineffective against the cold.

Changes in plant pigmentation with temperature

• In another study conducted to determine the effect of temperature on plant pigmentation, the changes in plant pigmentation with changes in temperature is the dependent variable.
• The study is conducted while changing the temperature of the environment, and the changes in pigmentation on the plants is observed and noted down.
• Based on this, the effect of temperature on plant pigmentation can be determined.

References and Sources

• 3% – https://www.thoughtco.com/definition-of-independent-variable-605238
• 2% – https://simplicable.com/new/experiment-variables
• 1% – https://www.simplypsychology.org/controlled-experiment.html
• 1% – https://www.lifepersona.com/what-are-dependent-and-independent-variables-examples
• 1% – https://en.wikipedia.org/wiki/Regressand
• 1% – https://en.wikipedia.org/wiki/Dependent_Variable
• 1% – https://blog.prepscholar.com/independent-and-dependent-variables
• <1% – https://www.verywellmind.com/what-is-a-variable-2795789
• <1% – https://www.thoughtco.com/independent-and-dependent-variable-examples-606828
• <1% – https://www.simplypsychology.org/variables.html
• <1% – https://www.mbaknol.com/research-methodology/research-variables-dependent-and-independent-variables/
• <1% – https://www.britannica.com/science/graph-mathematics
• <1% – https://www.answers.com/Q/The_independent_variable_is_plotted_on_what_axis
• <1% – https://www.annualreviews.org/doi/abs/10.1146/annurev.pp.04.060153.002023
• <1% – https://indoor.lbl.gov/sites/all/files/lbnl-60946.pdf
• <1% – https://explorable.com/dependent-variable
• <1% – https://difference.guru/difference-between-independent-and-dependent-variables/
• <1% – http://www.opentextbooks.org.hk/ditatopic/35412

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Sat / act prep online guides and tips, independent and dependent variables: which is which.

General Education

Independent and dependent variables are important for both math and science. If you don't understand what these two variables are and how they differ, you'll struggle to analyze an experiment or plot equations. Fortunately, we make learning these concepts easy!

In this guide, we break down what independent and dependent variables are , give examples of the variables in actual experiments, explain how to properly graph them, provide a quiz to test your skills, and discuss the one other important variable you need to know.

What Is an Independent Variable? What Is a Dependent Variable?

A variable is something you're trying to measure. It can be practically anything, such as objects, amounts of time, feelings, events, or ideas. If you're studying how people feel about different television shows, the variables in that experiment are television shows and feelings. If you're studying how different types of fertilizer affect how tall plants grow, the variables are type of fertilizer and plant height.

There are two key variables in every experiment: the independent variable and the dependent variable.

Independent variable: What the scientist changes or what changes on its own.

Dependent variable: What is being studied/measured.

The independent variable (sometimes known as the manipulated variable) is the variable whose change isn't affected by any other variable in the experiment. Either the scientist has to change the independent variable herself or it changes on its own; nothing else in the experiment affects or changes it. Two examples of common independent variables are age and time. There's nothing you or anything else can do to speed up or slow down time or increase or decrease age. They're independent of everything else.

The dependent variable (sometimes known as the responding variable) is what is being studied and measured in the experiment. It's what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).

An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

It can be a lot easier to understand the differences between these two variables with examples, so let's look at some sample experiments below.

Examples of Independent and Dependent Variables in Experiments

Below are overviews of three experiments, each with their independent and dependent variables identified.

Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels so you can get the most value for your money. You test different brands of popcorn to see which bag pops the most popcorn kernels.

• Independent Variable: Brand of popcorn bag (It's the independent variable because you are actually deciding the popcorn bag brands)
• Dependent Variable: Number of kernels popped (This is the dependent variable because it's what you measure for each popcorn brand)

Experiment 2 : You want to see which type of fertilizer helps plants grow fastest, so you add a different brand of fertilizer to each plant and see how tall they grow.

• Independent Variable: Type of fertilizer given to the plant
• Dependent Variable: Plant height

Experiment 3: You're interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures.

• Independent Variable: Ocean temperature
• Dependent Variable: The number of algae in the sample

For each of the independent variables above, it's clear that they can't be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.

Where Do You Put Independent and Dependent Variables on Graphs?

Independent and dependent variables always go on the same places in a graph. This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis.

Here's an example:

As you can see, this is a graph showing how the number of hours a student studies affects the score she got on an exam. From the graph, it looks like studying up to six hours helped her raise her score, but as she studied more than that her score dropped slightly.

The amount of time studied is the independent variable, because it's what she changed, so it's on the x-axis. The score she got on the exam is the dependent variable, because it's what changed as a result of the independent variable, and it's on the y-axis. It's common to put the units in parentheses next to the axis titles, which this graph does.

There are different ways to title a graph, but a common way is "[Independent Variable] vs. [Dependent Variable]" like this graph. Using a standard title like that also makes it easy for others to see what your independent and dependent variables are.

Are There Other Important Variables to Know?

Independent and dependent variables are the two most important variables to know and understand when conducting or studying an experiment, but there is one other type of variable that you should be aware of: constant variables.

Constant variables (also known as "constants") are simple to understand: they're what stay the same during the experiment. Most experiments usually only have one independent variable and one dependent variable, but they will all have multiple constant variables.

For example, in Experiment 2 above, some of the constant variables would be the type of plant being grown, the amount of fertilizer each plant is given, the amount of water each plant is given, when each plant is given fertilizer and water, the amount of sunlight the plants receive, the size of the container each plant is grown in, and more. The scientist is changing the type of fertilizer each plant gets which in turn changes how much each plant grows, but every other part of the experiment stays the same.

In experiments, you have to test one independent variable at a time in order to accurately understand how it impacts the dependent variable. Constant variables are important because they ensure that the dependent variable is changing because, and only because, of the independent variable so you can accurately measure the relationship between the dependent and independent variables.

If you didn't have any constant variables, you wouldn't be able to tell if the independent variable was what was really affecting the dependent variable. For example, in the example above, if there were no constants and you used different amounts of water, different types of plants, different amounts of fertilizer and put the plants in windows that got different amounts of sun, you wouldn't be able to say how fertilizer type affected plant growth because there would be so many other factors potentially affecting how the plants grew.

3 Experiments to Help You Understand Independent and Dependent Variables

If you're still having a hard time understanding the relationship between independent and dependent variable, it might help to see them in action. Here are three experiments you can try at home.

Experiment 1: Plant Growth Rates

One simple way to explore independent and dependent variables is to construct a biology experiment with seeds. Try growing some sunflowers and see how different factors affect their growth. For example, say you have ten sunflower seedlings, and you decide to give each a different amount of water each day to see if that affects their growth. The independent variable here would be the amount of water you give the plants, and the dependent variable is how tall the sunflowers grow.

Experiment 2: Chemical Reactions

Explore a wide range of chemical reactions with this chemistry kit . It includes 100+ ideas for experiments—pick one that interests you and analyze what the different variables are in the experiment!

Experiment 3: Simple Machines

Build and test a range of simple and complex machines with this K'nex kit . How does increasing a vehicle's mass affect its velocity? Can you lift more with a fixed or movable pulley? Remember, the independent variable is what you control/change, and the dependent variable is what changes because of that.

Quiz: Test Your Variable Knowledge

Can you identify the independent and dependent variables for each of the four scenarios below? The answers are at the bottom of the guide for you to check your work.

Scenario 1: You buy your dog multiple brands of food to see which one is her favorite.

Scenario 2: Your friends invite you to a party, and you decide to attend, but you're worried that staying out too long will affect how well you do on your geometry test tomorrow morning.

Scenario 3: Your dentist appointment will take 30 minutes from start to finish, but that doesn't include waiting in the lounge before you're called in. The total amount of time you spend in the dentist's office is the amount of time you wait before your appointment, plus the 30 minutes of the actual appointment

Scenario 4: You regularly babysit your little cousin who always throws a tantrum when he's asked to eat his vegetables. Over the course of the week, you ask him to eat vegetables four times.

Summary: Independent vs Dependent Variable

Knowing the independent variable definition and dependent variable definition is key to understanding how experiments work. The independent variable is what you change, and the dependent variable is what changes as a result of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

When graphing these variables, the independent variable should go on the x-axis (the horizontal axis), and the dependent variable goes on the y-axis (vertical axis).

Constant variables are also important to understand. They are what stay the same throughout the experiment so you can accurately measure the impact of the independent variable on the dependent variable.

What's Next?

Independent and dependent variables are commonly taught in high school science classes. Read our guide to learn which science classes high school students should be taking.

Scoring well on standardized tests is an important part of having a strong college application. Check out our guides on the best study tips for the SAT and ACT.

Interested in science? Science Olympiad is a great extracurricular to include on your college applications, and it can help you win big scholarships. Check out our complete guide to winning Science Olympiad competitions.

Quiz Answers

1: Independent: dog food brands; Dependent: how much you dog eats

2: Independent: how long you spend at the party; Dependent: your exam score

3: Independent: Amount of time you spend waiting; Dependent: Total time you're at the dentist (the 30 minutes of appointment time is the constant)

4: Independent: Number of times your cousin is asked to eat vegetables; Dependent: number of tantrums

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Dependent Variables (Definition + 30 Examples)

Welcome to a journey through the essential world of dependent variables! Whether you’re an avid learner, a seasoned researcher, or simply curious, unraveling the mysteries of dependent variables is crucial for making sense of scientific discoveries and everyday wonders.

A dependent variable is what we observe and measure in an experiment. It's called "dependent" because it changes based on the alterations we make to another variable, known as the independent variable. Think of it as a series of revealing clues, shedding light on the story of how one thing can affect another.

Embark with us on an enlightening adventure, as we delve into the significance of dependent variables, explore their relationship with independent variables, and uncover how they help us interpret and shape the world around us.

History of Dependent Variables

The concept of dependent variables finds its roots in the early foundations of scientific thought.

The ancient Greeks, notably Aristotle , laid down the groundwork for systematic observation and the study of cause and effect. Aristotle's ideas on causality, although different from today’s understanding, were pivotal in shaping the way we approach scientific inquiry.

Emergence of Experimental Science

The Renaissance period marked a significant shift in scientific thinking. Pioneers like Galileo Galilei and Sir Francis Bacon advocated for empirical observation and experimentation.

This period saw the emergence of experimental science, where the relationships between different variables, including dependent and independent ones, were systematically studied.

Development of Statistical Methods

The 18th and 19th centuries witnessed the development of statistical methods , which played a crucial role in understanding dependent variables.

Sir Francis Galton, a cousin of Charles Darwin, made significant contributions to the field of statistics and introduced the concept of regression, a foundational element in studying dependent variables.

Modern Day Applications

Today, the concept of dependent variables is integral to research across diverse fields, from biology and physics to psychology and economics. The evolution of research methodologies and statistical tools has allowed scientists and researchers to study dependent variables with increased precision and insight.

Conclusion on Origins

Understanding the origin of dependent variables offers a fascinating glimpse into the evolution of scientific thought and the relentless human pursuit of knowledge.

From the musings of ancient philosophers to the sophisticated research of today, dependent variables have journeyed through time, contributing to the rich tapestry of scientific discovery and progress.

What are Dependent Variables?

Understanding dependent variables is like piecing together a puzzle – it’s essential for seeing the whole picture! Dependent variables are at the core of scientific experiments, acting as the outcomes we observe and measure.

They respond to the changes we make in the independent variables , helping us unravel the connections and relationships between different elements in an experiment .

Dependent Variables in Scientific Experiments

In the realm of scientific experiments, dependent variables play the starring role of the outcome. When scientists alter something, the dependent variable is what reacts to this change.

For instance, if a botanist is examining how different amounts of sunlight (the independent variable) affect plant growth, the growth of the plant is the dependent variable.

Relationship with Independent Variables

Dependent variables and independent variables share a unique dance in the world of science. The independent variable leads, changing and altering, while the dependent variable follows, reacting and showing the effects of these changes.

It’s this intricate relationship that allows scientists and researchers to draw conclusions and make discoveries.

Making Observations and Drawing Conclusions

Observing dependent variables is like watching a story unfold. By carefully measuring and recording how they respond to changes, scientists can draw meaningful conclusions and answer pressing questions.

Whether it’s understanding how temperature affects sea levels or how diet influences health, dependent variables are the narrators of these scientific stories.

But remember, experimenters make errors, and sometimes those errors are based on their biases, or what they want to find or believe they will find, so keeping the variables in check is one way to avoid experimenter bias .

Real-World Applications

The insights gained from studying dependent variables don’t just stay in the lab – they ripple out into the real world!

From developing new medicines to improving educational techniques, understanding dependent variables is pivotal. They help us make informed decisions, solve problems, and enhance the quality of life for people around the globe.

Everyday Examples

In our everyday lives, we encounter countless instances of dependent variables.

When you adjust the brightness of your room to see how well you can read a book, the readability is your dependent variable.

Or, when a chef experiments with ingredients to observe the flavor of a dish, the taste is the dependent variable.

The Impact on Knowledge

Dependent variables are the building blocks of knowledge. They help us test hypotheses, validate theories, and expand our understanding of the universe.

Every observation, every measurement, brings us one step closer to unraveling the mysteries of the world and advancing human knowledge.

By grasping the role of dependent variables, we open doors to a myriad of possibilities, uncovering the secrets of the natural world and contributing to the rich tapestry of scientific discovery.

Dependent Variables in Research

Diving deeper into the realm of dependent variables, we uncover why they hold such an important role in the tapestry of scientific discovery and everyday life.

These variables are the storytellers, the revealers of effects, and the markers of change, helping us navigate the sea of knowledge and make waves of progress.

Scientific Discovery and Innovation

In the laboratory of discovery, dependent variables are the guiding stars. They help scientists and researchers observe the effects of changes, leading to breakthroughs and innovations.

Whether it’s finding a cure for a disease, inventing a new technology, or understanding the mysteries of the universe, dependent variables are at the heart of the eureka moments that shape our world.

Real-World Problem Solving

Outside the lab, the insights gained from dependent variables illuminate the path to solving real-world problems.

They play a crucial role in improving healthcare, education, environmental conservation, and numerous other fields, enabling us to develop solutions that enhance well-being and sustainability.

By understanding how dependent variables react, we can tailor strategies to address challenges and create a positive impact.

Informing Decision-Making

Every day, we make countless decisions, big and small. Dependent variables are like compasses, guiding our choices and actions.

Whether deciding on the best method to grow a garden, choosing a fitness routine, or selecting the right ingredients for a recipe, recognizing the dependent variables helps us make informed and effective decisions to achieve our goals.

Enhancing Understanding and Knowledge

The study of dependent variables enriches our comprehension of the world around us. They provide insights into cause and effect, helping us understand how different elements interact and influence each other.

This deepened understanding broadens our knowledge, fuels our curiosity, and inspires further exploration and learning.

Fostering Curiosity and Exploration

Peeling back the layers of dependent variables uncovers a world of wonder and curiosity. They invite us to ask questions, seek answers, and explore the intricate web of relationships in the natural and social world.

This sense of wonder and exploration drives scientific inquiry and fosters a lifelong love of learning and discovery.

Conclusion on Importance

The importance of dependent variables cannot be overstated. They are the keys that unlock the doors of understanding, the catalysts for innovation and progress, and the guides on our journey through the ever-evolving landscape of knowledge.

As we continue to explore and learn, the role of dependent variables remains central to our quest for understanding and discovery.

Challenges with Dependent Variables

While dependent variables illuminate the path of discovery, working with them can sometimes feel like navigating a labyrinth.

It’s essential to recognize the challenges and considerations that come with the territory, ensuring accurate, reliable, and meaningful outcomes in our pursuit of knowledge.

Measurement Accuracy

In the world of dependent variables, accuracy is king. Measuring outcomes precisely is crucial to avoid distorting the picture. Imagine trying to solve a puzzle with misshaped pieces – it wouldn’t fit together right! Ensuring accurate measurement means the story told by the dependent variable is true to reality.

External Influences

Sometimes, unseen forces can influence our dependent variables. These are called confounding variables , and they can sneak in and alter the outcomes, like a gust of wind turning the pages of a book.

Being aware of and controlling these external influences is essential to maintain the integrity of our observations and conclusions.

Consistency and Reliability

Consistency is the heartbeat of reliable results. When working with dependent variables, it’s vital to maintain consistent methods of measurement and observation. This consistency ensures that the story revealed is trustworthy and that the insights gained can be the foundation for further discovery and understanding.

Ethical Considerations

Exploring dependent variables also brings us face to face with ethical considerations . Whether it’s respecting privacy, ensuring safety, or acknowledging rights, it’s paramount to navigate the journey with integrity and responsibility. Ethical practices build trust and uphold the values that guide the pursuit of knowledge.

Varied Contexts and Applications

Dependent variables are versatile storytellers, revealing different tales in varied contexts and applications. Recognizing the diversity in application and interpretation is like tuning into different genres of stories – each holds unique insights and contributes to the richness of our understanding.

Reflection on Challenges and Considerations

Understanding and addressing the challenges and considerations in working with dependent variables is like sharpening the tools in our scientific toolbox. It strengthens the foundation of our exploration, ensuring that the journey is fruitful, the discoveries are genuine, and the stories told are authentic.

Famous Studies Involving Dependent Variables

The stage of scientific discovery has been graced by numerous studies and experiments where dependent variables played a starring role. These studies have shaped our understanding, answered profound questions, and paved the way for further exploration and innovation.

Ivan Pavlov’s Classical Conditioning

In the early 20th century, Ivan Pavlov ’s experiments with dogs shone a spotlight on dependent variables. He observed how dogs (the dependent variable) salivated in response to the sound of a bell (the independent variable), leading to groundbreaking insights into classical conditioning and learning.

Sir Isaac Newton’s Laws of Motion

Delving back into the 17th century, Sir Isaac Newton ’s exploration of the laws of motion involved observing how objects (the dependent variables) moved and interacted in response to forces (the independent variables). His work laid the foundations of classical mechanics and continues to influence science today .

Gregor Mendel’s Pea Plant Experiments

In the 19th century, Gregor Mendel ’s work with pea plants opened the doors to the world of genetics. By observing the traits of pea plants (the dependent variables) in response to different genetic crosses (the independent variables), Mendel unveiled the principles of heredity .

The Stanford Prison Experiment

In 1971, the Stanford Prison Experiment , led by Philip Zimbardo , explored the effects of perceived power and authority. The behavior of participants (the dependent variable) was observed in response to assigned roles as guards or prisoners (the independent variable), revealing insights into human behavior and ethics.

The Hawthorne Effect

In the 1920s and 1930s, studies at the Western Electric Hawthorne Works in Chicago observed worker productivity (the dependent variable) in response to changes in working conditions (the independent variables). This led to the discovery of the Hawthorne Effect , highlighting the influence of observation on human behavior.

Reflection on Famous Studies

These famous studies and experiments spotlight the pivotal role of dependent variables in scientific discovery. They illustrate how observing and measuring dependent variables have expanded our knowledge, led to breakthroughs, and addressed fundamental questions about the natural and social world.

Examples of Dependent Variables

1) test scores.

In an educational setting, student test scores often serve as a dependent variable to measure academic achievement.

2) Heart Rate

In health and exercise science, heart rate can be a dependent variable indicating cardiovascular response to activity.

3) Plant Growth

In botany, the growth of plants can be observed as a dependent variable when studying the effects of different environmental conditions.

4) Sales Revenue

In business, sales revenue may be a dependent variable analyzed in relation to advertising strategies.

5) Blood Pressure

In medicine, blood pressure levels can be a dependent variable to study the effects of medication or diet.

6) Job Satisfaction

In organizational psychology, job satisfaction levels of employees may be the dependent variable.

7) Ice Melt Rate

In climate studies, the rate at which ice melts can be a dependent variable in relation to temperature changes.

8) Customer Satisfaction

In service industries, customer satisfaction levels are often the dependent variable.

9) Reaction Time

In psychology, an individual's reaction time can be measured as a dependent variable in cognitive studies.

10) Fuel Efficiency

In automotive studies, the fuel efficiency of a vehicle may be the dependent variable.

11) Population Size

In ecology, the size of animal or plant populations can be a dependent variable.

12) Productivity Levels

In the workplace, employee productivity can be observed as a dependent variable.

13) Immune Response

In immunology, the body’s immune response can be the dependent variable when studying vaccines or infections.

14) Enzyme Activity

In biochemistry, the activity levels of enzymes can be measured as a dependent variable.

15) Market Share

In business, a company’s market share can be the dependent variable in relation to competition strategies.

16) Voter Turnout

In political science, voter turnout can be a dependent variable studied in relation to campaign efforts.

17) Concentration Levels

In cognitive studies, individual concentration levels can be measured as a dependent variable.

18) Pollution Levels

In environmental science, levels of pollution can be a dependent variable in relation to industrial activity.

19) Reading Comprehension

In education, students’ reading comprehension can be the dependent variable.

20) Muscle Strength

In kinesiology, an individual’s muscle strength can be measured as a dependent variable.

21) Website Traffic

In digital marketing, the traffic a website receives can be the dependent variable.

22) Patient Recovery Time

In healthcare, the recovery time of patients can be observed as a dependent variable.

23) Student Attendance

In education, student attendance rates can be a dependent variable.

24) Rainfall Amounts

In meteorology, the amount of rainfall can be a dependent variable.

25) Consumer Spending

In economics, consumer spending levels can be observed as a dependent variable.

26) Energy Consumption

In energy studies, the amount of energy consumed can be a dependent variable.

27) Body Mass Index (BMI)

In health studies, an individual’s BMI can be measured as a dependent variable.

28) Employee Retention

In human resources, employee retention rates can be the dependent variable.

29) Water Quality

In environmental studies, the quality of water can be observed as a dependent variable.

30) Customer Loyalty

In business, customer loyalty can be a dependent variable in relation to brand reputation and service quality.

These examples illustrate the diverse nature of dependent variables and how they are used to measure outcomes across a multitude of disciplines and scenarios.

Real-World Examples of Dependent Variables

Dependent variables are not just confined to textbooks; they dance through our daily lives, telling tales of change and effect. Let’s take a closer look at some real-life scenarios where dependent variables play a key role in telling the story of cause and effect.

In healthcare, dependent variables help doctors and researchers understand the effects of treatments and interventions.

For example, a patient’s blood sugar level is a dependent variable when studying the effectiveness of diabetes medication. Monitoring this variable helps healthcare professionals tailor treatments and manage health conditions effectively.

In the realm of education, dependent variables like test scores and attendance rates help educators gauge the effectiveness of teaching methods and interventions.

By observing these variables, teachers can adapt their strategies to enhance student learning and well-being.

Environmental Conservation

In the world of environmental conservation, dependent variables such as animal population sizes and pollution levels provide insights into the impact of conservation efforts.

These observations guide strategies to protect ecosystems and biodiversity, ensuring a harmonious balance between humans and nature.

Technology and Innovation

In the field of technology and innovation, dependent variables like user engagement and product performance are crucial in developing and refining groundbreaking technologies.

Observing these variables enables innovators to optimize designs, improve user experiences, and drive progress in the digital age.

Fitness and Well-being

In the pursuit of fitness and well-being, dependent variables such as muscle strength and heart rate are observed to measure the effects of different exercise routines and dietary choices.

These observations guide individuals in achieving their health and fitness goals, fostering a sense of well-being and vitality.

Social Sciences

In social sciences, dependent variables like voter turnout and job satisfaction offer insights into human behavior and societal dynamics. Studying these variables helps researchers and policymakers understand societal trends, human motivations, and the intricate tapestry of social interactions.

Business and Economics

In the business and economic landscape, dependent variables such as sales revenue and consumer spending reveal the effectiveness of marketing strategies and economic policies.

Analyzing these variables helps businesses and governments make informed decisions, fueling economic growth and prosperity.

Culinary Arts

In culinary arts, dependent variables like taste and texture are observed to perfect recipes and culinary creations. Chefs experiment with ingredients and cooking techniques, using the feedback from these variables to craft delightful culinary experiences.

Arts and Entertainment

In arts and entertainment, audience reception and ticket sales are dependent variables that offer insights into the appeal of creative works. Artists and creators use this feedback to hone their craft, create meaningful connections with the audience, and contribute to the rich tapestry of culture and creativity.

Conclusion on Real-Life Applications

Exploring the real-life scenarios and applications of dependent variables brings to light the omnipresence and significance of these variables in shaping our world.

From healthcare to the arts, understanding and observing dependent variables enable us to learn, adapt, and thrive in a constantly evolving environment.

Identifying Dependent Variables

Spotting a dependent variable might seem like looking for a needle in a haystack, but with the right tools and know-how, it becomes a fascinating treasure hunt!

Knowing how to identify dependent variables is essential whether you’re conducting an experiment, analyzing data, or just curious about the relationships between different factors.

To be a true dependent variable detective, let’s revisit its definition: a dependent variable is what we measure in an experiment and what changes in response to the independent variable. It’s like the echo to a shout, the reaction to an action.

Relationship with Changes

In the dance of variables, the dependent variable is the one that responds. When something is tweaked, adjusted, or altered (that’s the independent variable), the dependent variable is what shows the effect of those changes. It’s the piece of the puzzle that helps us see the bigger picture.

Tips and Tricks for Identification

Identifying dependent variables can be a breeze with a few handy tips!

First, ask yourself, “What am I measuring or observing?” This is usually your dependent variable.

Next, look for the effect or change that is happening as a result of manipulating something else.

If you’re still unsure, try to phrase your observation as “If we change X, then Y will respond.” Y is typically the dependent variable.

Practice Makes Perfect: Scenarios

Let’s put our knowledge to the test! Can you spot the dependent variables in these scenarios?

• Cooking Time: You’re experimenting with cooking times to see how soft the cookies become.
• Exercise Routine: Trying out different types of exercise routines to see which one increases your stamina the most.
• Plant Fertilizer: Applying different types of fertilizers to your tomato plants to observe which one produces the juiciest tomatoes.
• Study Environment: Testing various study environments to identify which one improves your focus and learning.
• Sleep Duration: Adjusting the number of hours you sleep to observe its impact on your energy level the next day.

Answers and Explanation

Got your answers ready? Let’s see how you did!

• Cooking Time: The softness of the cookies is the dependent variable.
• Exercise Routine: The increase in stamina is what you are measuring, making it the dependent variable.
• Plant Fertilizer: The juiciness of the tomatoes is the dependent variable here.
• Study Environment: Your focus and learning are the dependent variables in this scenario.
• Sleep Duration: The energy level the next day is your dependent variable.

Identifying dependent variables is a skill that sharpens with practice, helping us unravel the wonders of cause and effect in the world around us.

Final Thoughts on Identification

Mastering the art of identifying dependent variables is like gaining a superpower. It allows us to see the world through a lens of relationships and effects, deepening our understanding of how changes in one element can impact another.

In the intricate dance of cause and effect, dependent variables tell tales of outcomes, changes, and responses. From the realm of science to the canvas of art, they shape our understanding of the world and drive progress in countless fields.

The challenges faced in measuring these variables only add layers to their complexity, but the pursuit of knowledge and the joy of discovery make every step of the journey worthwhile.

As we conclude our exploration of dependent variables, we leave with a sense of wonder and curiosity, equipped with the knowledge to observe, question, and explore the world around us.

The stories of dependent variables continue to unfold, and the adventure of learning and discovery is boundless.

Thank you for joining us on this enlightening journey through the world of dependent variables. Keep exploring, stay curious, and continue to marvel at the wonders of the world we live in!

Related posts:

• Independent Variables (Definition + 43 Examples)
• Confounding Variable in Psychology (Examples + Definition)
• Positive Correlation (Meaning + 39 Examples + Quiz)
• 19+ Experimental Design Examples (Methods + Types)
• 45+ Negative Correlation Examples (Definition + Use-cases)

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What Is an Independent Variable? Definition and Examples

The independent variable is the variable that is controlled or changed in a scientific experiment to test its effect on the dependent variable . It doesn’t depend on another variable and isn’t changed by any factors an experimenter is trying to measure. The independent variable is denoted by the letter  x  in an experiment or graph.

INDEPENDENT VARIABLE EXAMPLE

Two classic examples of independent variables are age and time. They may be measured, but not controlled. In experiments, even if measured time isn’t the variable, it may relate to duration or intensity.

For example, a scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light and the moth’s reaction is the dependent variable.

For another example, say you are measuring whether amount of sleep affects test scores. The hours of sleep would be the independent variable while the test scores would be dependent variable.

A change in the independent variable directly causes a change in the dependent variable. If you have a hypothesis written such that you’re looking at whether  x  affects  y , the  x  is always the independent variable and the  y  is the dependent variable.

GRAPHING THE INDEPENDENT VARIABLE

If the dependent and independent variables are plotted on a graph, the x-axis would be the independent variable and the y-axis would be the dependent variable. You can remember this using the DRY MIX acronym, where DRY means dependent or responsive variable is on the y-axis, while MIX means the manipulated or independent variable is on the x-axis.

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Independent Variables in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amanda Tust is a fact-checker, researcher, and writer with a Master of Science in Journalism from Northwestern University's Medill School of Journalism.

Adam Berry / Getty Images

• Identifying

Potential Pitfalls

The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment.

For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).

In general, experiments have these three types of variables: independent, dependent, and controlled.

Identifying the Independent Variable

If you are having trouble identifying the independent variables of an experiment, there are some questions that may help:

• Is the variable one that is being manipulated by the experimenters?
• Are researchers trying to identify how the variable influences another variable?
• Is the variable something that cannot be changed but that is not dependent on other variables in the experiment?

Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants).

Below are the key differences when looking at an independent variable vs. dependent variable.

Expected to influence the dependent variable

Doesn't change as a result of the experiment

Can be manipulated by researchers in order to study the dependent variable

Expected to be affected by the independent variable

Expected to change as a result of the experiment

Not manipulated by researchers; its changes occur as a result of the independent variable

There can be all different types of independent variables. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating.

Independent variables also have different levels. In some experiments, there may only be one level of an IV. In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have.

In an experiment on the effects of the type of diet on weight loss, for example, researchers might look at several different types of diet. Each type of diet that the experimenters look at would be a different level of the independent variable while weight loss would always be the dependent variable.

To understand this concept, it's helpful to take a look at the independent variable in research examples.

In Organizations

A researcher wants to determine if the color of an office has any effect on worker productivity. In an experiment, one group of workers performs a task in a yellow room while another performs the same task in a blue room. In this example, the color of the office is the independent variable.

In the Workplace

A business wants to determine if giving employees more control over how to do their work leads to increased job satisfaction. In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not. The amount of input the workers have over their work is the independent variable in this example.

In Educational Research

Educators are interested in whether participating in after-school math tutoring can increase scores on standardized math exams. In an experiment, one group of students attends an after-school tutoring session twice a week while another group of students does not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable.

In Mental Health Research

Researchers want to determine if a new type of treatment will lead to a reduction in anxiety for patients living with social phobia. In an experiment, some volunteers receive the new treatment, another group receives a different treatment, and a third group receives no treatment. The independent variable in this example is the type of therapy .

Sometimes varying the independent variables will result in changes in the dependent variables. In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured.

At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV.

Choosing an Independent Variable

If you are designing an experiment, here are a few tips for choosing an independent variable (or variables):

• Select independent variables that you think will cause changes in another variable. Come up with a hypothesis for what you expect to happen.
• Look at other experiments for examples and identify different types of independent variables.
• Keep your control group and experimental groups similar in other characteristics, but vary only the treatment they receive in terms of the independent variable.   For example, your control group will receive either no treatment or no changes in the independent variable while your experimental group will receive the treatment or a different level of the independent variable.

It is also important to be aware that there may be other variables that might influence the results of an experiment. Two other kinds of variables that might influence the outcome include:

• Extraneous variables : These are variables that might affect the relationships between the independent variable and the dependent variable; experimenters usually try to identify and control for these variables. 
• Confounding variables : When an extraneous variable cannot be controlled for in an experiment, it is known as a confounding variable . 

Extraneous variables can also include demand characteristics (which are clues about how the participants should respond) and experimenter effects (which is when the researchers accidentally provide clues about how a participant will respond).

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

Weiten, W. Psychology: Themes and Variations, 10th ed . Boston, MA: Cengage Learning; 2017.

National Library of Medicine. Dependent and independent variables .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."