Independent and Dependent Variables | Difference & Examples
In an experiment, a researcher tests a hypothesis by manipulating an independent variable and measuring its impact on a dependent variable. A variable is any property that can take on different values (e.g., height, temperature, GPA).
Experiments test cause-and-effect relationships:
- Independent variables are the cause—the thing that is changed by the researcher.
- Dependent variables are the effect—the thing that changes in response to manipulations of the independent variable.
In other words, you systematically vary the independent variable and measure the resulting changes in the dependent variable.
| Independent variable | Dependent variable |
|---|---|
| Manipulated by the researcher | Measured by the researcher |
| Acts as the cause | Represents the effect |
| The “if” part of a hypothesis (i.e., “if I change [this variable]…”) | The “then” part of a hypothesis (i.e., “… then this variable should change.”) |
| Plotted on the x-axis of a graph | Plotted on the y-axis of a graph |
| Occurs earlier in time in an experiment | Occurs later in time in an experiment |
| Also called an input, predictor variable, explanatory variable, manipulated variable, or treatment variable | Also called an output, predicted variable, explained variable, measured variable, or outcome |
What is an independent variable?
An independent variable is something that is manipulated in an experimental design to test a hypothesis. Independent variables can be considered inputs. They are intentionally varied to determine whether or how they impact an outcome.
Independent variables take on different amounts or categories. These possible values are referred to as conditions, levels, or treatments. In a true experimental design, participants must be randomly assigned to different conditions to avoid unintended bias.
The independent variable in this study is time spent napping. It has three conditions or levels. The dependent variable is “number of words remembered.”
Independent variables are called “independent” because they are not influenced by other variables in the study. They instead influence changes in dependent variables.
Types of independent variables
Independent variables can be experimental or quasi-experimental.
Experimental independent variables are directly manipulated by the experimenter. Their impact on a dependent variable is then measured.
Quasi-experimental independent variables, also known as subject variables, are variables that cannot be directly manipulated, either because they are inherent to the subject (e.g., sex, race, gender) or because it would be unethical to directly manipulate them (e.g., consumption of illegal drugs).
Because quasi-experimental variables cannot be manipulated, it is not possible to randomly assign participants to categories. You can instead use a quasi-experimental design, where you define groups based on these characteristics and compare their outcomes. Because they lack random assignment, quasi-experimental designs are especially susceptible to research biases.
Independent variables are called different names in different contexts (e.g., covariate, regressor).
What is a dependent variable?
A dependent variable is an outcome that is impacted by changes to the independent variable. In other words, its value depends on the independent variable.
While the independent variable takes on different conditions or levels determined by the researcher, the dependent variable is not directly controlled—the experimenter instead measures how its value or score changes when the independent variable is modified.
Much like independent variables, dependent variables are referred to by different names in different contexts. Other terms for dependent variables include outcome, predicted variable, and measured variable.
Identifying independent vs dependent variables
It can be difficult to identify independent and dependent variables in complicated studies. Often studies have more than one of each. To further complicate things, the independent variable in one study can be the dependent variable in another. This is illustrated by the following example.
Study 1: You are interested in how caffeine intake influences sleep quality. Participants are given either 400 mg of caffeine, 200 mg of caffeine, or a placebo before bed. You then record how long each group sleeps for.
The independent variable in this study is caffeine consumption. The dependent variable is hours of sleep.
Study 2: You are interested in whether people who are sleep deprived drink more caffeine. Participants assigned to a sleep deprivation condition are only allowed to sleep for four hours one night; participants in the control condition are allowed to sleep for up to eight hours. Both groups are then given free access to coffee throughout the following day and their intake is recorded.
Now the independent variable is hours of sleep. The dependent variable is caffeine consumption.
You can ask yourself several questions to determine if a variable is dependent or independent.
Recognizing the independent variable
If the answer to any of these questions is “yes,” you’re probably dealing with an independent variable:
- Is this variable being manipulated or controlled?
- Does this variable occur earlier in time?
- Is this variable being used to group participants?
- Does this variable represent a treatment or condition?
- Has the experiment chosen which values this variable can have?
- Is the researcher trying to understand how this variable affects something else?
Recognizing the dependent variable
If you answer “yes” to any of these questions, the variable is probably dependent:
- Is this variable measured as an outcome of the study?
- Is this variable being measured later in time?
- Is this variable being compared across different groups or conditions?
- Is this variable affected by another variable?
- Is this variable the effect, result, or response being measured?
Dependent and independent variables in mathematics
In mathematics, the relationship between an independent variable and a dependent variable is often expressed as a function. The expression y = f(x) indicates that y changes as a function of x. The symbol “x” is commonly used to denote an independent variable, and “y” refers to the dependent variable.
In multivariate calculus, where there are multiple independent variables, you might see expressions like z = f(x, y). In this case, z is the dependent variable. It changes as a function of the two independent variables x and y.
Conventionally, independent variables are plotted on the x-axis (the horizontal axis), and dependent variables are plotted on the y-axis (the vertical axis).
Dependent and independent variables examples
Consider the following examples of research questions and the related independent and dependent variables.
| Research question | Independent variable | Dependent variable |
|---|---|---|
| Are people more creative in visually pleasing environments? | Experiment setting (a room with bare walls or a decorated room) | Scores on a creativity test |
| Do students perform better on morning vs. evening exams? | Time of exam (morning or evening) | Exam scores |
| Do faster readers miss more typos? | Reading speed | Number of typos detected |
| Is hippocampal engagement during learning predictive of memory? | Hippocampal activity during a task-based functional MRI scan | Performance on a memory task |
Frequently asked questions about independent and dependent variables
- What is an outcome variable?
-
An outcome variable, or outcome measure, is another term for a dependent variable.
Dependent variables are the outcome or response that is measured in a study. Independent variables are manipulated by the researcher, and changes in the dependent variable are recorded and analyzed. An experiment explores cause-and-effect relationships between dependent and independent variables.
- What is an independent variable synonym?
-
Independent and dependent variables are called by various names across different contexts and fields. Some common synonyms for independent variables include the following:
- Predictor variable
- Regressor
- Covariate
- Manipulated variable
- Explanatory variable
- Exposure variable
- Feature
- Input variable
- What is an experiment?
-
An experiment is a study that attempts to establish a cause-and-effect relationship between two variables.
In experimental design, the researcher first forms a hypothesis. They then test this hypothesis by manipulating an independent variable while controlling for potential confounds that could influence results. Changes in the dependent variable are recorded, and data are analyzed to determine if the results support the hypothesis.
Nonexperimental research does not involve the manipulation of an independent variable. Nonexperimental studies therefore cannot establish a cause-and-effect relationship. Nonexperimental studies include correlational designs and observational research.
- What are the 12 threats to internal validity?
-
The 12 main threats to internal validity are:
- History: Changes in the environment or events that occur outside of the study can affect the outcome.
- Maturation: Changes in the participants over time (e.g., age, skill level) can affect the outcome.
- Testing: The act of testing or measurement itself can affect the outcome (testing effect, practice effect, or carryover effect).
- Instrumentation: Changes in the measuring instrument or tool used to collect data can affect the outcome.
- Statistical regression to the mean: The tendency of extreme scores to regress towards the mean, which can lead to a loss of statistical significance.
- Selection: The selection of participants for the study can affect the outcome (selection bias), especially in the case of non-probability sampling.
- Experimental mortality or attrition bias: The loss of participants or dropouts during the study can affect the outcome.
- Multiple-treatment interference: The interaction between different treatments or conditions can affect the outcome.
- Social desirability bias: The participants’ awareness of being in a study and their desire to be well-liked by researchers can affect the outcome.
- Social interaction: The participants’ awareness of being treated differently than people in other groups can affect the outcome.
- Residual confounding: The presence of unmeasured or uncontrolled extraneous or confounding variables that affect the outcome and are not accounted for in the analysis.
- Order effect: The order of the independent variable levels affects the dependent variable.
There are several ways to counter these threats to internal validity, for example, through randomization, the addition of control groups, and blinding.
- What is a dependent variable synonym?
-
You may encounter different terms for independent and dependent variables in different contexts. Some common synonyms for dependent variables are as follows:
- Dependent measure
- Outcome
- Response variable
- Predicted variable
- Output variable
- Measured variable
