Independent and dependent variables
In research, variables are any characteristics that can take on different values, such as height, age, species, or exam score.
In scientific research, we often want to study the effect of one variable on another one. For example, you might want to test whether students who spend more time studying get better exam scores.
The variables in a study of a causeandeffect relationship are called the independent and dependent variables.
 The independent variable is the cause. Its value is independent of other variables in your study.
 The dependent variable is the effect. Its value depends on changes in the independent variable.
Research Question  Independent variable(s)  Dependent variable(s) 

Do tomatoes grow fastest under fluorescent, incandescent, or natural light? 


What is the effect of diet and regular soda on blood sugar levels? 


How does phone use before bedtime affect sleep? 


How well do different plant species tolerate salt water? 


Independent and dependent variables in experiments
In experimental research, the independent variable is manipulated or changed by the experimenter to measure the effect of this change on the dependent variable.
The independent variable is usually applied at different levels to see how the outcome differs.
You can apply just two levels (e.g. the new medication and the placebo) in order to find out if the independent variable has an effect at all.
You can also apply multiple levels (e.g. three different doses of the new medication) to find out how the independent variable affects the dependent variable.
Variables in other types of research
Outside of an experimental setting, researchers often cannot directly manipulate or change the independent variable that they’re interested in.
Instead, they must find alreadyexisting examples of the independent variable, and investigate how changes in this variable affect the dependent variable.
In nonexperimental research, it’s more difficult to establish a definite causeandeffect relationship, because other variables that you haven’t measured might be influencing the changes. These are known as confounding variables.
In types of research where the exact relationship between variables is less certain, you might use different terms for independent and dependent variables.
Other names for independent variables
Sometimes, the variable you think is the cause might not be fully independent – it might be influenced by other variables. In this case, one of these terms is more appropriate:
 Explanatory variables (they explain an event or outcome)
 Predictor variables (they can be used to predict the value of a dependent variable)
 Righthandside variables (they appear on the righthand side of a regression equation).
Other names for dependent variables
Dependent variables are also known by these terms:
 Response variables (they respond to a change in another variable)
 Outcome variables (they represent the outcome you want to measure)
 Lefthandside variables (they appear on the lefthand side of a regression equation)
Visualizing independent and dependent variables
Researchers often use charts or graphs to visualize the results of their studies. The norm is to place the independent variable on the “x”or horizontal axis and the dependent variable on the “y” or vertical axis.
For instance, how might a graph look from our example study on the impact of a new medication on blood pressure?
Frequently asked questions
 What are independent and dependent variables?

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.
 Why are independent and dependent variables important?

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
 What is an example of an independent and a dependent variable?

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment.
 The type of soda – diet or regular – is the independent variable.
 The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.
 Can a variable be both independent and dependent?

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
 Can I include more than one independent or dependent variable in a study?

Yes, but including more than one of either type requires multiple research questions.
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable.
To ensure the internal validity of an experiment, you should only change one independent variable at a time.
1 comment
Lauren Thomas (Scribbr Team)
May 20, 2020 at 6:49 PMThanks for reading! Hope you found this article helpful. If anything is still unclear, or if you didn’t find what you were looking for here, leave a comment and we’ll see if we can help.