In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, vehicle exhaust is the independent variable while asthma is the dependent variable.
A confounding variable, or confounder, affects the relationship between the independent and dependent variables. A confounding variable in the example of car exhaust and asthma would be differential exposure to other factors that increase respiratory issues, like cigarette smoke or particulates from factories. Because it would be unethical to expose a randomized group of people to high levels of vehicle exhaust,[1] a study comparing two populations with differential exposure to vehicle exhaust would rely on a natural experiment, or a situation in which this already occurs due to factors unrelated to the researchers. In this natural experiment, a community living near higher concentrations of car exhaust may also live near factories that pollute or have higher rates of smoking.
When running a study or analyzing statistics, researchers try to remove or account for as many of the confounding variables as possible in their study design or analysis. Confounding variables lead to bias, or a factor that may cause an estimate to differ from the true population value. Bias is a systematic error in study design, subject recruitment, data collection, or analysis that results in a mistaken estimate of the true population parameter.[2]
Although there are many types of bias, two common types are selection bias and information bias. Selection bias occurs when the procedures used to select subjects and others factors that influence participation in the study produce a result that is different from what would have been obtained if all members of the target population were included in the study.[2] For example, an online website that rates the quality of primary care physicians based on patients’ input may produce ratings that suffer from selection bias. This is because individuals that had a particularly bad (or good) experience with the physician may be more likely to go to the website and provide a rating.
Information bias refers to a “systematic error due to inaccurate measurement or classification of disease, exposure, or other variables.”[3] Recall bias, a type of information bias, occurs when study participants do not remember the information they report accurately or completely. The subject of confounding and bias relates to a larger discussion of the relationship between correlation and causation. Although two variables may be correlated, this does not imply that there is a causal relationship between them.
One way to determine whether a relationship between variables is causal is based on three criteria for research design: temporal precedence meaning that the hypothesized cause happens before the measured effect; covariation of the cause and effect meaning that there is an established relationship between the two variables regardless of causation; and a lack of plausible alternative explanations. Plausible alternative explanations are other factors that may cause the dependent variable under observation.[4]. These alternative explanations are closely related to the concept of internal validity.
[1]Trochim, W.M.K. “Establishing Cause and Effect.” Research Methods Knowledge Base, 10/20/2006. Web 1/24/2017.
[2] “Bias, Confounding and Effect Modification” Stat 507, Epidemiological Research Methods, Penn State Eberly
College of Science, 2017 Web 1/24/17.
[3] Aschengrau A. and G.R. Seage. (2014) Epidemiology in public health. 3rd ed. Burlington, MA: Jones & Bartlett Learning.
[4]. Due to a long history of
unethical research in health and social sciences, researchers have many ethical obligations when conducting research, particularly with human subjects. These obligations were first codified in the Nuremburg Code in 1946, which specified that the benefits of research must outweigh the foreseeable risks. Ethical obligations continue to evolve to protect human subjects, including confidentiality and anonymity unless waived and informed consent. Increasingly, communities that have a stake in the
outcomes of research are involved in its design and informed of the outcomes of the study. All federally funded research in the United States is subject to review by an Institutional Review Board (IRB).
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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
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