Independent vs. Dependent Variables  Definition & Examples
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test causeandeffect relationships.
 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.
Table of contents
 What is an independent variable?
 Types of independent variables
 What is a dependent variable?
 Identifying independent vs. dependent variables
 Independent and dependent variables in research
 Visualizing independent and dependent variables
 Other interesting articles
 Frequently asked questions about independent and dependent variables
What is an independent variable?
An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
 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).
These terms are especially used in statistics, where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.
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Types of independent variables
There are two main types of independent variables.
 Experimental independent variables can be directly manipulated by researchers.
 Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.
Experimental variables
In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.
You can apply just two levels in order to find out if an independent variable has an effect at all.
You can also apply multiple levels to find out how the independent variable affects the dependent variable.
A true experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.
Subject variables
Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.
It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasiexperimental design because there’s no random assignment. Note that any research methods that use nonrandom assignment are at risk for research biases like selection bias and sampling bias.
What is a dependent variable?
A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
 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)
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.
Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.
Identifying independent vs. dependent variables
Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper.
A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design.
Here are some tips for identifying each variable type.
Recognizing independent variables
Use this list of questions to check whether you’re dealing with an independent variable:
 Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
 Does this variable come before the other variable in time?
 Is the researcher trying to understand whether or how this variable affects another variable?
Recognizing dependent variables
Check whether you’re dealing with a dependent variable:
 Is this variable measured as an outcome of the study?
 Is this variable dependent on another variable in the study?
 Does this variable get measured only after other variables are altered?
Independent and dependent variables in research
Independent and dependent variables are generally used in experimental and quasiexperimental research.
Here are some examples of research questions and corresponding independent and dependent variables.
Research question  Independent variable  Dependent variable(s) 

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


What is the effect of intermittent fasting on blood sugar levels? 


Is medical marijuana effective for pain reduction in people with chronic pain? 


To what extent does remote working increase job satisfaction? 


For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis.
The type of test is determined by:
 your variable types
 level of measurement
 number of independent variable levels.
You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.
Visualizing independent and dependent variables
In quantitative research, it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the xaxis (horizontal) and the dependent variable on the yaxis (vertical).
The type of visualization you use depends on the variable types in your research questions:
 A bar chart is ideal when you have a categorical independent variable.
 A scatter plot or line graph is best when your independent and dependent variables are both quantitative.
Other interesting articles
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.
Methodology
Frequently asked questions about independent and dependent variables
 What’s the definition of an independent variable?

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
 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).
 What’s the definition of a dependent variable?

A dependent variable is what changes as a result of the independent variable manipulation in experiments. It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
 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)
 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.
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