Basics of Research Process

What Are Independent and Dependent Variables: Definition, Difference & Examples

Independent and Dependent Variables
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Independent variables and dependent variables are two types of variables used in research to understand the link between various factors, phenomena or events. Simply put, they are used to investigate causal relationships.

  • An independent variable is the one that is changed or manipulated by an experimenter.
  • A dependent variable is the one that responds to adjustments in an independent variable.
Effect of Independent Variable on Dependent Variable

Knowing the difference between independent and dependent variables is key to designing successful experiments. In this article, we will explain what independent and dependent variables mean in research, discuss their main peculiarities, and provide examples. Whether you're a student, researcher, or simply interested in learning more about experimental design, this blog from our research paper writing service will provide you with a comprehensive overview of cause-and-effect connection between variables.

Independent Variable vs Dependent Variable

When it comes to research, we can often observe a cause-and-effect connection between variables. The variable that produces the change is called an independent variable and the one that reacts to this change is referred to as a dependent variable. In other words, you will observe how alterations in one factor trigger changes in the other one. So if you maneuver your independent variable in research, the dependent variable will also be affected in some way.

Examples of Independent and Dependent Variables in Research

To illustrate this concept, let's consider dependent and independent variables examples. This will help us understand how these 2 factors interact in an experiment.

Independent Variable vs Dependent Variable Example

Imagine that you are investigating how different scents affect the mood. It’s completely up to you what fragrance to offer, but their self-reported mood scores are something you can only observe as a result of the scent exposure. 
In this study, you decide to test five different fragrances: lavender, eucalyptus, citrus, vanilla, and a no scent (for control group). You will expose Individuals to each scent in separate sessions and conduct the survey asking to rate their mood on a scale from 1 to 10. You then analyze  the answers to figure out if there is an association between the scent and the mood scores.
In this scenario, the choice of fragrance is the independent variable, as it's a factor you can willingly adjust in the experiment. The self-reported mood scores are the dependent variable, as they represent the outcome you're interested in and depend on the determinant – the specific scent exposure.

What Is an Independent Variable: Definition

An independent variable is the one that is regulated or manipulated by an experimenter. It is also labeled as a predictor variable or explanatory variable, since it explains the effect on a dependent variable. Researchers can control it and manage its levels to isolate its impact on other variables. By scrutinizing occurring changes, researchers can gain insights into the cause-and-effect relationship.

Examples of independent variables in research studies might include the dose of drugs, the type of therapy, or the amount of exposure to a certain stimulus. It's important to note that the independent variable can take many forms, depending on your research question and study design.

Independent Variable Example

For example, if a researcher seeks to explore the effects of different teaching methods on student achievements, an independent variable would be the type of teaching technique (e.g. traditional lectures, inquiry-based teaching, kinesthetic learning).

How to Choose an Independent Variable?

Choosing an independent variable for a research study is a critical step in the experimental design process. Here are some considerations to keep in mind when selecting a predictor variable:

  • Relevance to a research question and study design
  • Presumed cause of observe changes
  • Manipulation or control by researchers
  • Measurability and ability to quantify the data.

>> Learn more: What Is a Controlled Experiment

Types of Independent Variables

While there are different ways to categorize predictors, here are some common types of independent variables used in research studies.

Type of independent variable




Experimental variables are variables that can be intentionally altered or managed. Researchers often use this type of predictor to deliberately manipulate it in order to test whether it has an effect on other variables. Scientists can manipulate experimental independent variables on various levels (e.g., low, medium, high). This allows us to determine whether this factor is responsible for any transformations in dependent variables and to what extent.

In a study examining the effects of a new medication on pain relief, an experimental independent variable would be the dosage of medication administered to participants. Researchers would manipulate the dose to see whether it can relieve pain.

Subject (quasi-independent variables)

Subject independent variables cannot be changed and are recorded as they naturally occur. These can be  participant's characteristics or attributes (e.g., age, gender, ethnicity). These characteristics are used to group participants or assess individual preferences. However, it is important to note that subject variables may introduce confounding variables into a study. 

In an experiment on the impact of age on learning outcomes, age would be a subject variable. Researchers would record the ages of participants and analyze whether there is any difference in learning outcomes between different age groups.


Categorical variables have categories or groups as their values and cannot be quantified. These categories can be nominal (e.g., gender, race) or ordinal (e.g., education level, income level). Categorical independent variables can be manipulated by assigning participants to different categories or groups, such as treatment group vs. control group or male vs. female.

If you are testing the effects of gender on social media usage, gender would be a categorical independent variable. You would divide participants into male and female groups and examine whether there is any difference in social media usage between genders.


Continuous independent variables are the factors that can take on any value within a range. They can be measured on a numerical scale and can include decimal points or fractions.  Examples include age, height, and weight. Continuous independent variables can be changed by varying its value, such as extending the duration of an intervention.

If you are studying the influence of time on weight loss, duration would be a continuous independent variable. Researchers would set a range for duration (e.g., 20 minutes to 40 minutes) and manipulate this value to investigate its impact on weight.


Dichotomous type of independent variable has only two possible values.  They are typically used to group participants in a study based on a binary characteristic or attribute. Examples include yes/no, true/false, and so on.

A study on the relationship between smoking and lung cancer may utilize smoking status as an independent variable to assess the impact of smoking on the incidence of lung cancer. By appointing participants into either smoker or non-smoker groups, a researcher can examine whether smoking has a causal effect on the development of lung cancer.

What Is a Dependent Variable: Definition?

A dependent variable in research is a factor that is influenced by the independent variable. It's also referred to as an outcome variable, response variable, or effect variable since it's what researchers measure as a result of their experiments. The values of this variable completely stem from the values of independent variables.

To define the causal connection between two factors,  researchers need to collect data from dependent variables and carry out statistical analyses. Data analysis helps to find out if the independent variable produces any effect. If any relation is established, researchers can further estimate the magnitude of influence the independent variable has on the dependent variable. This allows quantifying the strength of causal relationship between the two variables and drawing valid conclusions about causality.

Example of Dependent Variable

In a study investigating the influence of different advertising campaigns on product sales, a dependent variable would be the amount of products sold. This data can then be compared to make sure that various campaigns have different impacts on the sales numbers. If a significant difference is found, it would suggest that advertising campaigns do affect product sales, this will help researchers to draw meaningful conclusions that can be applied in marketing or business.

How to Select a Dependent Variable?

It's important to select the right dependent variable in an experiment to make sure that your study goes smoothly. Here are some things you should keep in mind when choosing your response variable:

  • Relevance to the independent variable
  • Sensitivity to changes in causal factors
  • Measurability and reproducibility
  • Availability of reliable data.

What Is the Difference Between Independent and Dependent Variables?

Understanding the difference between an independent and dependent variable lays the foundation for accurate results and interpretation.

Independent variable

Dependent variable


Supposed cause

Supposed effect


Purposely varied or manipulated by researchers

Changes in line with variations in the independent variable


To test its impact on the dependent variable

To measure or observe the effect of the independent variable on the outcome of the study


Usually controlled or selected by the researcher and can have multiple levels or categories

Dependent on the values of the independent variable in an experiment and recorded by the researcher

How to Identify Independent and Dependent Variables?

Identifying the independent and dependent variables is a crucial step in research design, as it helps researchers to understand the potential link.  In this section, we will discuss some strategies for recognizing independent and dependent variables in research.

  1. Define your research question(s) The first step is to develop a research question. This will help to clarify which variables are relevant to your study and what relationships are being examined.
  2. Consider keywords As you review your research question and study design, pay attention to wording. There might be words related to influence (e.g.  "cause," "affect," "impact," "change") or effect (e.g., "result," "outcome," "correlate," "predict,").
  3. Look for causality Identify which variable is being manipulated and which variable is being measured as a result of this manipulation.
  4. Conduct analysis Organize and experiment and analyze the data to confirm if any association really exists.

Independent Variable and Dependent Variable in Experiment

Let's explore real-world examples of independent and dependent variables in research studies. These examples will illustrate what  causal links are identified in a variety of experimental contexts.


Research question

Independent variable

Dependent variable


How does exercise affect heart rate?

Exercise intensity

Heart rate


How does caffeine impact memory recall?

Caffeine consumption

Memory recall


Does exposure to violent media increase aggression in children?

Exposure to violent media

Aggressive behavior in children


Does living in a high-crime neighborhood affect residents' perceptions of safety and trust in their community?

Neighborhood crime rate

Perceptions of safety and trust


How does the concentration of salt affect water density?

Salt concentration

Water density


Does age have an impact on job satisfaction levels?


Job satisfaction levels

How to Visualize an Independent and Dependent Variable on a Graph?

When it comes to handling data in a quantitative study, visualizing cause-and-effect relationships between variables can be helpful. Graphs are a great way to show the influence of an independent variable on a dependent variable over some time or within certain conditions. By plotting the data, researchers can compare different scenarios more easily.

When visualizing the association between your cause and effect, it's important to choose a graph that is appropriate for the type of variables being explored. Here are some guidelines:

Type of graph

Type of independent and dependent variables 


Scatter plot

2 Continuous independent and dependent variables

An independent variable is plotted on the x-axis, while a dependent variable is plotted on the y-axis. Each data point represents a unique observation, and the placement of the point indicates the value of the independent and dependent variables for that observation.

Bar chart

Categorical independent variable and a continuous dependent variable

The cause is located on the x-axis, and each category is displayed by a separate bar. The height of each bar indicates the mean value of the dependent variable for that category.

Box plot

Distribution of a continuous dependent variable for different levels of a categorical independent variable

An independent variable is displayed on the x-axis, and each group is represented by an individual box. The height of boxes indicates the interquartile range of your dependent variable for that category, and the whiskers show the range of data.

Line chart

2 Continuous variables over time

The predictor (usually time) is placed on the x-axis, while the outcome is depicted on the y-axis. Each data point demonstrates a measurement taken at a specific point in time, and the line connecting the points showcases the trend in data over time.

Let’s review this example to see how this works. 

Example of Independent and Dependent Variables on a Graph

Suppose we are conducting a study to determine whether higher education levels influence the income level in the United States. After organizing a cross-sectional survey and gathering data, you will need to put this information on a bar chart. In this case, our independent variable (education level) will fall into 4 categories: high school, bachelor's degree, master's degree, and doctoral degree. On a graph, each category will correspond to the income level on the y-axis.

Independent and Dependent Variables on a Graph

Bottom Line on Independent vs Dependent Variable

It's important to recognize the difference between independent and dependent variables when designing or analyzing experiments. Before collecting any data, you should determine how different factors interact with one another to generate the influence. It's essential that you properly identify a causal link, since this is a foundation of the whole scientific study which guarantees the validity of your research results.

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FAQ About Independent and Dependent Variables

1. What are the characteristics of an independent variable?

An independent variable is a type of predictor variable that has the following characteristics:

  • Can be modified or controlled
  • Must have at least 2 levels or values
  • Used to foresee or explain variation in the dependent variable
  • Precedes the dependent variable in time.

2. What are the characteristics of a dependent variable?

A dependent variable is a type of outcome variable that has such features:

  • Can't be altered or controlled
  • Reacts to changes in the independent variable
  • Follows the independent variable in time.

3. Which variable is measured in an experiment?

A dependent variable is what researchers measure as an outcome of an experiment. As a rule of thumb, this is a variable that is of most interest in a research study.

4. Which variable is manipulated in research?

In research, an independent variable is the one that is purposely manipulated or controlled by an experimenter. This is done in order to observe potential variations in a dependent variable.

5. Where does an independent variable go on a graph?

In a graph, an independent variable is typically shown on the x-axis.

6. Where does a dependent variable go on a graph?

A dependent variable is usually shown on the y-axis on a graph. It is typically used to illustrate variation in the data due to changes in the independent variable.

7. Why are independent and dependent variables significant?

Independent and dependent variables are important because they allow researchers to measure the relationship between two or more different factors. Having a clear understanding of how these variables influence one another can be useful in making predictions, and forming educated conclusions about how different phenomena work.

8. Can one variable be independent and dependent at the same time?

It's not typical for a single variable to be both independent and dependent at the same time within the context of a single research study. However, this can only be true if a reciprocal relationship occurs. In this case, two variables are directly linked to each other in such a way that changes in either one cause an immediate reaction in the other. For example, if temperature increases, air pressure decreases and vice versa. Both temperature and air pressure can simultaneously be dependent and independent variables. But usually, you will study one variable as independent and the other one as dependent at a time.

Article posted on:May 2, 2023
Article updated on:May 11, 2023


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