• 5-minute read
• 3rd December 2021

What Are Independent and Dependent Variables?

If you are involved in any kind of scientific research, from anthropology to zoology, you’ll come across the terms “independent variable” and “dependent variable.” But what exactly are independent and dependent variables?

In this post, we’ll explain the basics and how to use variable in your work.

What Are Variables?

In scientific research, a variable is any factor that can change and be measured. This could be almost anything, from blood pressure to coffee consumption. It all depends on what you are researching.

In all cases, though, variables reflect a cause-and-effect relationship in an experiment.  And they therefore come in two key types:

• Independent variables are the causal element of an experiment (i.e., the things a researcher will vary to see how it effects the results).
• Dependent variables represent the effect in an experiment (i.e., what a researcher measures to see the effects of changing an independent variable).

We’ll explain what these are and how they’re used in more detail below.

What Is an Independent Variable?

An independent variable what the researcher controls in an experiment. For example, in an experiment to find out whether drinking coffee affects running performance, you would need to vary the amount of coffee that participants drink.

In such a study, you might instruct volunteers to run 100 meters before and one hour after drinking a cup of coffee, and then record the two times. Because the amount of coffee is the element you have changed, it is the independent variable.

But what if you wanted to study the effect of foot size on running performance? You can’t adjust the size of participants” feet! Instead, you might divide volunteers into groups according to their shoe size, then record the average speed for each group. In this experiment, the independent variable would be foot size.

What Is a Dependent Variable?

A dependent variable is so called because it depends on other variables. In other words, it is the thing that changing an independent variable affects in an experiment. It is therefore also what researchers measure to get their results.

In each of our example studies above, for instance, the dependent variable is running speed, because that is what is being measured.

It is possible for a study to include more than one dependent variable. As well as noting the influence of coffee on running speed, for example, you could also record participants” heart rates before and after drinking coffee to see how it changes. You would then have two dependent variables: running speed and heart rate.

Using Variables in Experimental Research

If an experiment is going to give meaningful results, it is important to clearly define the variables involved. This will give you a clear sense of what you are testing.

For example, it wouldn’t be enough to just say you are going to study the effect of coffee on running performance. After all, “coffee” could refer to a range of drinks of different sizes and strengths. And “running performance” refer to speed or distance.

To use these things as scientific variable, then, you’d need to set out what you mean by “coffee” and “running performance” in your study. This is called operationalization.

For example, you could state that, for the purposes of your experiment, “coffee” refers to “one 225 ml cup of black coffee containing 100 mg of caffeine” and that “running performance” refers to “the time it takes participants to run 100 meters.”

By operationalizing your variables, you allow others to replicate your experiment and make it possible to compare your findings with the results of other studies.

When you present data in a graph, moreover, you should always put the independent variable on the “x” (horizontal) axis and the dependent variable on the “y” (vertical) axis. This will help ensure clarity for readers.

Variables in Other Research

Not all research permits direct control of variables in the way described above for experimental research. However, you will usually still need to identify an independent variable to vary and a dependent variable to measure.

For example, to study the effect of rainfall on people’s wellbeing, your independent variable (i.e., the thing being varied) would need to be rainfall, while your dependent variable (i.e., the thing being measured) would be people’s wellbeing.

Since you can’t control the weather, though, you’d need to find a different way to vary the independent variable. This might mean picking one city where it rains a lot, such as Portland, Oregon, and another where it is drier, like Las Vegas.

The amount of rainfall in each city would then be your “independent” variable. And by conducting a survey about people’s wellbeing in each city, you could compare the effect of this on the dependent variable.

Summary: Independent and Dependent Variables

One way to remember the difference between independent and dependent variables in scientific studies is to use the following phrases:

• The researcher is in charge of the independent variable.
• The dependent variable is the one that provides data.

You can test yourself by naming the independent and dependent variables in each of the following hypotheses (answers available below):

1. Students who sleep more get better test scores.
2. Playing Madden NFL 21 makes people better at real-life football.
3. Diners eat more if they chew gum before meals.
4. Assignments that are proofread get higher grades.

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To see the answers to the quiz above, select the text below:

1. IV = Amount of sleep; DV= Test scores.
2. IV = Time spent playing Madden 21; DV = Real football skill.
3. IV = Whether gum is chewed; DV = Amount of food eaten.
4. IV = Whether assignment is proofread; DV = grade awarded.