What is an independent variable?
An independent variable is the variable you change or manipulate in an experiment to test its effect on an outcome. A is what you measure—it changes in response to the independent variable. Put simply: the independent variable is the cause, and the dependent variable is the effect.
For example, how well someone performs on a test (the dependent variable) could be influenced by how long they study or how much sleep they get the night before (independent variables).
Being clear about which variables are which is essential in any design—it’s how you establish cause-and-effect relationships between the variables you’re studying.
[Embed: 1QRx4m3axKbEtdiGslIn1K]
Types of independent variables
Independent variables can take several forms, depending on the and research. The most common types are experimental independent variables and subject variables.
Experimental independent variables
Experimental variables are those you can directly manipulate in a study to discover how they influence your dependent variables.
For example, you may have two study groups split by an independent variable: one receiving a new drug treatment and one receiving a placebo. These studies generally require randomly assigning participants to different groups to observe how results vary under different conditions.
A proper experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment controls for participant characteristics so they don’t affect your results. It gives you confidence that changes in the dependent variable come solely from your manipulation of the independent variable.
Subject variables
Subject variables are independent variables that can’t be changed in a study but can be used to categorize participants. They’re features that differ between subjects. As a social researcher, for instance, you might use gender identification, race, education level, or income to classify your research subjects.
Unlike experimental variables, subject variables call for a approach because there’s no random assignment—these attributes are inherent to participants and can’t be assigned.
Instead, you compare the findings of different groups of participants based on their features. Keep in mind that any using non-random assignment is vulnerable to study biases such as sampling and selection bias.
What is the importance of independent variables?
Independent variables are critical in study design because they help researchers determine cause-and-effect relationships. Controlled experiments require minimal outside influence to support valid conclusions.
Identifying independent variables is one way to eliminate external influences and gain greater certainty that results are representative. By controlling for outside influences as much as possible, you can make meaningful inferences about the link between independent and dependent variables.
In most cases, changes in the independent variables cause changes in the dependent variables. If you compare across an independent variable like age, you might expect a dependent variable like cognitive function or running speed to differ when the age gap is large. That said, sometimes variations in the independent variable don’t influence the dependent variable at all.
How can you choose an independent variable?
Your study objectives drive the choice of independent variables. Start by formulating a hypothesis about the outcome you anticipate, then choose independent variables you believe will significantly influence the dependent variables.
Make sure your experimental and have identical features, differing only in the treatment they receive. Your control group undergoes no treatment or change in the independent variable, while the experimental group receives the treatment or a variation of the independent variable.
How to include an independent variable in an experiment
The type of study greatly affects how an independent variable works. In an experiment with a control condition or group, you’ll need to define and monitor the values of the independent variables within each test condition.
In an observational study, the explanatory variables’ values aren’t predetermined—they’re observed in their natural surroundings.
Model specification is the process of deciding which independent variables to incorporate into a statistical model. It draws on subject-matter knowledge as well as statistical considerations.
Including one independent variable in a regression model is a simple regression; including more than one is a multiple regression. The names differ, but the analysis, interpretation, and assumptions are the same.
What are some examples of independent variables?
These examples show independent variables in different contexts:
- Mental health context: as a medical researcher, you want to find out whether a new treatment reduces anxiety in people with social anxiety disorder. Your study includes three groups of patients—one receives the new treatment, another gets a different treatment, and the last gets no treatment. The type of treatment is the independent variable.
- Workplace context: you want to know if giving employees greater control over how they perform their duties increases job satisfaction. Your study involves two groups of employees, one with a lot of say over how they do their jobs and the other without. The independent variable is the amount of control employees have over their job.
- Educational context: you conduct a study to see if after-school math tutoring improves student performance on standardized math tests. One group of students attends an after-school tutoring session three times a week; another group doesn’t. The independent variable is involvement in after-school math tutoring sessions.
- Organization context: you want to know if the color of an office affects work efficiency. Your research compares employees working in white or yellow rooms. The independent variable is the color of the office.
What is a dependent variable?
A dependent variable changes as a result of manipulating the independent variable. It’s what you test or measure in an experiment. It’s also known as a response variable, since it responds to changes in another variable, or an outcome variable, because it represents the outcome you want to measure.
Statisticians also call these left-hand side variables because they typically sit on the left-hand side of a regression model. On graphs, dependent variables are usually plotted on the y-axis.
For instance, in a study evaluating how a certain treatment affects the symptoms of psychological disorders, the dependent variable might be the severity of a patient’s symptoms. The treatment would be the independent variable.
Experiment results matter because they help you determine the extent to which changes in your independent variable cause variations in your dependent variable. They can also help forecast how much your dependent variable will vary as the independent variable changes.
Identifying independent vs. dependent variables
Differentiating between independent and dependent variables can be challenging, especially in comprehensive research designs. Sometimes a dependent variable from one study serves as an independent variable in another. The key is to pay close attention to the study design.
Recognizing independent variables
To recognize independent variables, ask whether the variable causes variation in another variable. Independent variables are manipulated variables whose values are determined by the researchers. In some experiments, notably in medicine, they’re described as risk factors; in others, experimental factors.
Keep in mind that control groups and treatments are often independent variables. Studies using this approach tend to treat independent variables as categorical grouping variables that establish the experimental groups.
In observational research, the approach differs slightly: independent variables explain, predict, or correlate with variation in the dependent variable. If you see an estimated impact size, it belongs to an independent variable, whatever type of study you’re reading or designing.
Recognizing dependent variables
To identify dependent variables, first determine whether the variable is measurable within the research. Then check whether it relies on another variable in the experiment. If a variable only changes after other variables have been changed, it may be a dependent variable.
Independent and dependent variables in research
Both independent and dependent variables are mainly used in quasi-experimental and experimental studies. When conducting research, you can generate descriptive statistics to illustrate results, then choose a suitable statistical test to validate your hypothesis.
The kind of variable, measurement level, and number of independent variable levels will influence your choice of test. Many studies use either the ANOVA or the t-test for and to answer .
Other key variables
Other variables, beyond independent and dependent ones, can have a major impact on a research outcome. It’s vital to identify and control extraneous variables since they can cause variation in the relationship between the independent and dependent variables.
Examples of include demand characteristics and experimenter effects. When these variables can’t be controlled in an experiment, they’re usually called .
Visualizing independent and dependent variables
You can use a chart or graph to visualize results. By convention, independent variables sit on the horizontal x-axis and dependent variables on the vertical y-axis. How you present the data depends on the nature of the variables in your research questions.
The lowdown
A working knowledge of independent and dependent variables is key to understanding how research projects work. The simplest framing: the independent variable is what you change, and the dependent variable is what changes as a result.
In other words, the independent variable is the cause and the dependent variable is the effect. When graphing them, place the independent variable on the x-axis and the dependent variable on the y-axis.
Remember that other variables can also affect the outcome of an experiment. Identify and control extraneous variables as much as possible to draw valid conclusions from your findings.
FAQs
What are the dependent and independent variables in research?
An independent variable in research or an experiment is what the researcher manipulates or changes. The dependent variable is what’s measured. In general, the independent variable influences the dependent variable.
What are the variables in research examples?
In research or an experiment, a variable refers to something that can be tested. You can use independent and dependent variables to .
Can a variable be both independent and dependent at the same time?
No, because a dependent variable relies on the independent variable. A variable in a single study can only be the cause (independent) or the effect (dependent). However, a dependent variable from one study can be used as an independent variable in another.
Can a study have more than one independent or dependent variable?
Yes. To work well, though, a study with multiple independent and dependent variables needs distinct research questions for each.
Should you be using a customer insights hub?
Do you want to discover previous research faster?
Do you share your research findings with others?
Do you analyze research data?