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• Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

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 cause-and-effect 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.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

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.

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)
• Right-hand-side variables (they appear on the right-hand 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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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.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

• A low-dose experimental group
• A high-dose experimental group
• A placebo group (to research a possible placebo effect )

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 quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

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)
• Left-hand-side variables (they appear on the left-hand 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.

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?

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Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

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.

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 x -axis (horizontal) and the dependent variable on the y -axis (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.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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.

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:

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.

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.

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!

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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What Is a Dependent Variable?

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

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• Independent vs. Dependent
• Selection Features

Frequently Asked Questions

The dependent variable is the variable that is being measured or tested in an experiment. For example, in a study looking at how tutoring impacts test scores, the dependent variable would be the participants' test scores since that is what is being measured.

This is different than the independent variable in an experiment, which is a variable that stands on its own. In the example above, the independent variable would be tutoring. The independent variable (tutoring) doesn't change based on other variables, but the dependent variable (test scores) may.

One way to help identify the dependent variable is to remember that it depends on the independent variable. When researchers make changes to the independent variable, they then measure any resulting changes to the dependent variable.

The dependent variable is called "dependent" because it is thought to depend, in some way, on the variations of the independent variable.

Independent vs. Dependent Variable

In a psychology experiment , researchers study how changes in one variable (the independent variable) change another variable (the dependent variable). Manipulating independent variables and measuring the effect on dependent variables allows researchers to draw conclusions about cause-and-effect relationships.

These experiments can range from simple to quite complicated, so it can sometimes be a bit confusing to know how to identify the independent vs. dependent variables. Here are a couple of questions to ask to help you learn which is which.

Which Variable Is the Experimenter Measuring?

Keep in mind that the dependent variable is the one being measured. So, if the experiment is trying to see how one variable affects another, the variable that is being affected is the dependent variable.

In many psychology experiments and studies, the dependent variable is a measure of a certain aspect of a participant's behavior . In an experiment looking at how sleep affects test performance, the dependent variable would be test performance.

Which Variable Does the Experimenter Manipulate?

The independent variable is "independent" because the experimenters are free to vary it as they need. This might mean changing the amount, duration, or type of variable that the participants in the study receive as a treatment or condition.

For example, it's common for treatment-based studies to have some subjects receive a certain treatment while others receive no treatment at all. In this case, the treatment is an independent variable because it is the one being manipulated or changed.

Variable being manipulated

Doesn't change based on other variables

Stands on its own

Variable being measured

May change based on other variables

Depends on other variables

How to Choose a Dependent Variable

How do researchers determine what will be a good dependent variable? There are a few key features that a scientist might consider.

Stability is often a good sign of a higher quality dependent variable. If the experiment is repeated with the same participants, conditions, and experimental manipulations, the effects on the dependent variable should be very close to what they were the first time around.

A researcher might also choose dependent variables based on the complexity of their study. While some studies only have one dependent variable and one independent variable, it is possible to have several of each type.

Researchers might also want to learn how changes in a single independent variable affect several dependent variables. For example, imagine an experiment where a researcher wants to learn how the messiness of a room influences people's creativity levels .

This research might also want to see how the messiness of a room might influence a person's mood. The messiness of a room would be the independent variable and the study would have two dependent variables: level of creativity and mood .

Ability to Operationalize

Operationalization is defined as "translating a construct into its manifestation." In simple terms, it refers to how a variable will be measured. So, a good dependent variable is one that you are able to measure.

If measuring burnout , for instance, researchers might decide to use the Maslach Burnout Inventory. If measuring depression, they could use the Patient Health Questionnaire-9 (PHQ-9).

Dependent Variable Examples

As you are learning to identify the dependent variables in an experiment, it can be helpful to look at examples. Here are just a few dependent variable examples in psychology research .

• How does the amount of time spent studying influence test scores? The test scores would be the dependent variable and the amount of studying would be the independent variable. The researcher could also change the independent variable by instead evaluating how age or gender influences test scores.
• How does stress influence memory? The dependent variable might be scores on a memory test and the independent variable might be exposure to a stressful task.
• How does a specific therapeutic technique influence the symptoms of psychological disorders ? In this case, the dependent variable might be defined as the severity of the symptoms a patient is experiencing, while the independent variable would be the use of a specific therapy method .
• Does listening to classical music help students perform better on a math exam? The scores on the math exams are the dependent variable and classical music is the independent variable.
• How long does it take people to respond to different sounds? The length of time it takes participants to respond to a sound is the dependent variable, while the sounds are the independent variable.
• Do first-born children learn to speak at a younger age than second-born children? In this example, the dependent variable is the age at which the child learns to speak and the independent variable is whether the child is first- or second-born.
• How does alcohol use influence reaction time while driving? The amount of alcohol a participant ingests is the independent variable, while their performance on the driving test is the dependent variable.

A Word From Verywell

Understanding what a dependent variable is and how it is used can be helpful for interpreting different types of research that you encounter in different settings. When you are trying to determine which variables are which, remember that the independent variables are the cause while the dependent variables are the effect.

The dependent variable depends on the independent variable. Thus, if the independent variable changes, the dependent variable would likely change too.

The dependent variable is placed on a graph's y-axis. This is the vertical line or the line that extends upward. The independent variable is placed on the graph's x-axis or the horizontal line.

The dependent variable is the one being measured. If looking at how a lack of sleep affects mental health , for instance, mental health is the dependent variable. In a study that seeks to find the effects of supplements on mood , the participants' mood is the dependent variable.

A controlled variable is a variable that doesn't change during the experiment. This enables researchers to assess the relationship between the dependent and independent variables more accurately. For example, if trying to assess the impact of drinking green tea on memory, researchers might ask subjects to drink it at the same time of day. This would be a controlled variable.

U.S. National Library of Medicine. Dependent and independent variables .

Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders .  Clin Interv Aging . 2015;10:1189-1199. doi:10.2147/CIA.S81868

Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size .  Indian Dermatol Online J . 2019;10(1):82-86. doi:10.4103/idoj.IDOJ_468_18

Flannelly LT, Flannelly KJ, Jankowski KR. Independent, dependent, and other variables in healthcare and chaplaincy research . J Health Care Chaplain . 2014;20(4):161-70. doi:10.1080/08854726.2014.959374

Weiten W.  Psychology: Themes and Variations . Cengage Learning.

Roediger HL, Elmes DG, Kantowitz BH. Experimental Psychology . Cengage Learning.

Vassar M, Matthew H. The retrospective chart review: important methodological considerations . J Educ Eval Health Prof . 2013;10:12. doi:10.3352/jeehp.2013.10.12

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable.

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

Need a helping hand?

What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations, so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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What is a dependent variable?

Last updated

4 March 2023

Reviewed by

Miroslav Damyanov

Admit it. The mere mention of the term "dependent variables" evokes vague memories of your math and science classes back in high school. If you're a science buff, you likely enjoyed those classes a lot. 

Fast forward to today, and that knowledge could've come in handy—except you don't remember the nitty-gritty of it all. Fret not; we've got you covered.

At the heart of every scientific experiment lies the dependent variable, and we cannot overstate its importance in understanding cause-and-effect relationships. 

In this definitive guide, we'll look at dependent variables, how they differ from their independent counterparts, how to choose one, examples, and everything in between. 

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• First things first, what is a variable?

A variable is an entity that can assume different values. In the simplest of terms, we can consider anything that can vary as a variable. 

For instance, height is a variable because we can assign a person's height a value. Other variables include income, age, country of birth, test scores, and so on.

• What exactly is a dependent variable?

Now, back to our topic of the day. A dependent variable varies when other factors influence it. Specifically, it changes as a result of the independent variable's influence. 

In an experimental study, the dependent variable is typically the one you're interested in measuring or monitoring to determine whether or not other variables affect it. 

In statistics, dependent variables use a few other names, including:

Outcome variables because you observe and measure them by changing independent variables

Response variables because they respond to changes in other variables

Left-hand-side variables because they appear on the left side of the equals sign in a regression equation

Y-variables because 'Y' usually represents them on a graph

Is it possible to define dependent variables in the context of cause-and-effect relationships? Absolutely! That's precisely why this phenomenon exists in the first place. 

While the independent variable is the "cause," the dependent variable is the "effect"—the affected variable. 

Naturally, you're itching to learn the difference between dependent and independent variables. Luckily for you, that's next.

• Dependent vs. independent variables: How are they different?

Let's first understand what an independent variable is. True to its name, an independent variable stands alone, and other variables don’t change or affect it. 

If the value of an independent variable changes at any time, that change happens at the researcher's discretion, not because of other variables. 

Typically, the researcher determines the independent variable. Its value is clear and well-known right at the beginning of the experiment, unlike the dependent variable. Those values only become clear after the experiment's conclusion.

Comprehending the difference between dependent and independent variables is vital for any research. Thankfully, getting it right the first time isn't difficult. 

The quickest way is to place both variables in the sentence below in a logical way:

"The IV causes changes to the DV. It is not possible that DV could cause any changes to IV."

Here's how that would reflect in our above example:

"Sleeping causes changes to test results. It is not possible that test results could cause any changes to sleeping."

When altering the independent variable during an experiment, your goal is to track and measure the changes it causes to dependent variables. Remember that changes in the dependent variable can only occur due to independent variable manipulation. 

To better understand the nuanced differences between dependent and independent variables, let's explore a few examples:

Example 1: What is the effect of green tea on blood pressure?

Independent variable: The amount of green tea consumed

Dependent variable: Blood pressure

Example 2: How does employee productivity affect business growth?

Independent variable: Hours spent doing productive work

Dependent variable: Business growth

Example 3: What is the impact of economic change on customer behavior?

Independent variable: Individual changes in the economy

Dependent variable: Customer behavior

On a broader level, here's what makes dependent and independent variables fundamentally different:

Dependent variables:

Depend on other variables

May change due to other variables

Are always the ones you’re measuring

Independent variables:

Stand on their own

Never change due to other variables

Undergo manipulation

• How to choose a good dependent variable

Pinpointing a good dependent variable is more complex than it sounds. You're often contending with several above-par variables, leaving you spoilt for choice. Other times, the research context is way too complex and gives nothing away. 

Fortunately for you, we've formulated a set of questions to streamline your selection process.

How stable is the variable?

A dependent variable is only half as good as the stability and consistency of its output. A high-quality variable yields the same outcome irrespective of how often you repeat the experiment. 

To arrive at accurate conclusions, you must maintain the same conditions, experimental manipulations, and participants from start to finish.

How complex is your study?

Choosing a dependent variable without first considering the complexity of your study is a recipe for failure. Some studies require more than just a single variable of either type. 

You must do your due diligence early in the process to ensure your final results are accurate and conclusive. 

You might also have a situation where you want to find out how changes in one independent variable impact a couple of dependent variables. In that case, it's crucial to pinpoint all of them correctly from the get-go.

For instance, say you want to investigate how low employee morale affects productivity. 

Obviously, the dependent variable here is productivity, while low employee morale is the independent variable. Upon further scrutiny, you'll realize there's an opportunity to test for a few more dependent variables, including employee turnover and profitability. 

So, it all boils down to how complex you want your study to be.

Is it possible to operationalize the variable?

In research, operationalization refers to the ability to measure a variable. A dependent variable is only good enough if you can measure it easily, accurately, and without hiccups.

In measuring individual test results, you may use the standard error of measurement (SEm). 

If measuring blood pressure, you could use a digital blood pressure monitor. SEm will tell you how much the repeated measures of the same person on the same digital pressure monitor tend to be spread around the person’s “true” score.

• Pitfalls to keep an eye on

We hate to break it to you, but dependent and independent variables aren't the only variables that may influence the outcome of your experiment. Several others can, too. 

Here are a few to be aware of:

Confounding variables

You can’t account for a confounding variable in a scientific experiment. It acts as an external force that can quickly change the effect of dependent and independent research variables, often yielding outcomes that differ completely from reality.

For example, a confounding variable may be responsible for the correlation between weight loss and weight loss. We’d expect that the more you exercise, the more likely you will lose weight.

However, a confounding variable may be eating habits: The more people eat, the more weight they gain, regardless of exercise.

It's best to account for confounding variables before your study starts to prevent them from wreaking havoc. Matching, restriction, and randomization are all reliable methods for keeping these wayward variables in check.

Extraneous variables

Sometimes, it's impossible to control a confounding variable. When that happens, it automatically becomes an extraneous variable .

One way to control extraneous variables is through elimination. Control by elimination means removing potential extraneous variables by holding them constant in all experimental conditions. Otherwise, you may draw inaccurate conclusions about the relationships between the independent and dependent variables.

• Examples of Dependent Variables

We've already highlighted several tangible examples of dependent variables. For clarity's sake, let's go a step further.

Here are additional dependent variables examples you might find helpful.

In organizations

A business wants to find out how the color of the office decor affects worker productivity. 

In this case, worker productivity would be the dependent variable, and the color of the office would be the independent variable. The business could also alter the independent variable by instead evaluating how work hours or low morale influence worker productivity.

In the workplace

A researcher wants to determine if giving workers more control over their extra shifts leads to increased job satisfaction. 

In an experiment, one group of employees gets to pick up shifts freely and without restriction, while the other group enjoys little freedom. Job satisfaction is the dependent variable in this example.

In psychology research

A researcher intends to investigate the effects of alcohol on the brain. 

Here, the dependent variable could be the scores on the PHQ-9 assessment tool, which provisionally diagnoses depression. The independent variable might be the amount of alcohol a participant ingests.

Of course, dependent variable examples abound. We couldn't possibly exhaust all of them. But with the information and slew of examples in this piece, you should be well-positioned to make your next experiment a resounding success.

• Final words

The role of dependent variables in shaping and grounding modern-day research experiments is undeniably important. 

Alongside independent variables, dependent variables make it easy for researchers and organizations to uncover the true impact of events. This speeds up the formulation of real and tangible solutions.

What are the three types of variables?

An experimental study has three types of variables:

Independent variable

Dependent variable

Controlled variable

A dependent variable is the one a researcher tests to get its values. 

An independent variable is what the researcher changes to test the dependent variable. 

The variable that the scientist intentionally holds constant throughout the research is a controlled variable. While it may not be part of the experiment, it's important because it can affect the results.

Is the control group the same as the dependent variable?

No. The control group serves as the standard of comparison in a specific experiment. In other words, this group isn't part of the actual experiment.

The opposite of a control group is an experimental group.

Meanwhile, the dependent variable is the factor that may change as a result of independent variable manipulation.

How do you identify a dependent variable?

The quickest way to identify a dependent variable is to ask yourself these three questions:

Does it depend on another variable in the experiment?

Does it change due to other variables?

Is it the one you’re measuring?

If your answer to all these questions is yes, that's a dependent variable.

If not, reexamine the above criteria to see if it’s an independent variable instead.

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When one variable causes another variable, we have what researchers call independent and dependent variables. In the example where gender was found to be causally linked to cell phone addiction, gender would be the independent variable (IV) and cell phone addiction would be the dependent variable (DV). An independent variable is one that causes another. A dependent variable is one that is caused by the other. Dependent variables depend on independent variables. If you are struggling to figure out which is the dependent and which is the independent variable, there is a little trick, as follows:

Ask yourself the following question: Is X dependent upon Y. Now substitute words for X and Y. For example, is the level of success in an online class dependent upon time spent online? Success in an online class is the dependent variable, because it is dependent upon something. In this case, we are asking if the level of success in an online class is dependent upon the time spent online. Time spent online is the independent variable.

Table 4.2 provides you with an opportunity to practice identifying the dependent and the independent variable.

Practice Exercise:  Practice choosing the dependent and independent variables. Identify the dependent and independent variables from the questions below.

• Dependent variable = success in online class; Independent variable = gender.
• Dependent variable = prevalence of PTSD in BC; Independent variable = level of funding for early intervention.
• Dependent variable = reporting of high school bullying; Independent variable = anti-bullying programs in high schools.
• Dependent variable = survival rate of female heart attack victims; Independent variable = hospital emergency room procedures.

Research Methods, Data Collection and Ethics Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Chapter 4: Measurement and Units of Analysis

4.5 Independent and Dependent Variables

When one variable causes another variable, we have what researchers call independent and dependent variables. In the example where gender was found to be causally linked to cell phone addiction, gender would be the independent variable (IV) and cell phone addiction would be the dependent variable (DV). An independent variable is one that causes another. A dependent variable is one that is caused by the other. Dependent variables depend on independent variables. If you are struggling to figure out which is the dependent and which is the independent variable, there is a little trick, as follows:

Ask yourself the following question: Is X dependent upon Y. Now substitute words for X and Y. For example, is the level of success in an online class dependent upon time spent online? Success in an online class is the dependent variable, because it is dependent upon something. In this case, we are asking if the level of success in an online class is dependent upon the time spent online. Time spent online is the independent variable.

Table 4.2 provides you with an opportunity to practice identifying the dependent and the independent variable.

Practice Exercise:  Practice choosing the dependent and independent variables. Identify the dependent and independent variables from the questions below.

• Dependent variable = success in online class; Independent variable = gender.
• Dependent variable = prevalence of PTSD in BC; Independent variable = level of funding for early intervention.
• Dependent variable = reporting of high school bullying; Independent variable = anti-bullying programs in high schools.
• Dependent variable = survival rate of female heart attack victims; Independent variable = hospital emergency room procedures.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Independent vs. Dependent Variables – Use & Examples

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Understanding independent vs. dependent variables of experimental design, statistical modelling, and hypothesis testing in various disciplines shapes a bedrock of methodology . An independent variable is the factor manipulated to observe its effect, whereas a dependent variable is the outcome or response that is measured. In essence, the independent variable causes a change, and the dependent variable is what is being affected. Distinguishing between independent and dependent variables is vital for organizing experiments, interpreting results, and drawing meaningful conclusions. Learn more in this article.

Inhaltsverzeichnis

• 1 Independent vs. Dependent Variables – In a Nutshell
• 2 Definition: Independent vs. dependent variables
• 3 Independent vs. dependent variables in research
• 4 Detecting independent vs. dependent variables
• 5 Illustrating independent vs. dependent variables

Independent vs. Dependent Variables – In a Nutshell

• An independent variable is one that is manipulated by the researcher.
• A dependent variable is one that is being tested in a research experiment.
• Scientific research assesses the effect on dependent variables based on the changes in independent variables.
• This article shows examples of how to illustrate dependent and independent variables in graphs.

Definition: Independent vs. dependent variables

To distinguish independent vs. dependent variables, it is simplest to define each type of variable individually:

• An independent variable is a value independent of other variables in scientific research. It is what the researcher changes to trigger an effect on other variables.
• A dependent variable is a value influenced by changes in the independent variable. Therefore, the difference between independent vs. dependent variables is that the former is the cause while the latter is the effect.

You design an experiment to determine whether changes in room lighting can affect test score results:

• Independent variable: Light in the room.

So, you can place participants in a room with different light levels. In contrast, your

• Dependent variable: The test scores.

So, you will assess the participants’ scores using a standard test to determine how their scores differ depending on the room lighting

Independent vs. dependent variables in research

Independent and dependent variables are applied in experimental and quasi-experimental research. Check out the following examples of research questions and their corresponding independent vs. dependent variables:

In experimental research, you analyze the results by visualizing your findings or using descriptive statistics . T-tests and ANOVAs are perfect for analyzing data and answering research questions. There are different types of independent vs. dependent variables in research. The following will delve into the various types of independent vs. dependent variables.

Types of independent variables

There are two key types of independent variables:

• Experimental independent variables

Subject variables

Experimental variables.

Researchers manipulate independent variables in experiments to evaluate and observe how they impact the dependent variables. For instance, you can apply only two levels to determine if the independent variable impacts the dependent variable.

You design an experiment to determine the impact of a novel medication on diabetic patients’ blood sugar levels. The independent variable will be the medication you vary between the groups.

You can have three independent variable levels applied to different groups:

• Low dose group

Subject variables are the values or qualities that vary across subjects that the researcher cannot manipulate, like age, gender, ethnicity, and race. A researcher cannot randomly assign subject variables to participants. Instead, you must create an experimental design where you can compare the outcomes of subject groups with specific characteristics. Subject variables are applicable in quasi-experimental designs with no random variable assignments. Unfortunately, non-random assignment risks research biases like sampling and selection bias.

You study whether age affects neural responses to digital technology. Your independent variable will be the subjects’ age, which is a subject variable. You can have ages 13 – 23, 24 – 24, and 35 and above.

Your dependent variable will be the neural responses to digital technology. So, you will record the brain activity using MRI scans when the participants hear of digital technology.

After gathering the data from your independent vs. dependent variables, you will check the statistical significance differences between the group.

Dependent variables

A distinction between independent vs. dependent variables is that dependent variables are the changes that occur from manipulations of the independent variable. The records that researchers note after manipulating an independent variable are the dependent variable.

Detecting independent vs. dependent variables

Identifying independent vs. dependent variables in an academic or complex research paper can be difficult because independent vs. dependent variables vary from one study to another. Below are guidelines for distinguishing independent vs. dependent variables.

Identifying independent variables

Ask yourself the questions below.

• Is the researcher manipulating or controlling the variable as a subject for grouping the subjects in the study?
• Does the variable precede the other variable (in time)?
• Is the researcher attempting to study if or how the variable affects another variable?

Identifying dependent variables

Ask the following questions.

• Is the variable measured as the research outcome?
• Does the variable rely on another variable in the experiment?
• Does the researcher measure the variable after altering another variable?
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Illustrating independent vs. dependent variables

Another important thing to learn is illustrating independent vs. dependent variables. You can use charts or graphs to illustrate independent vs. dependent variables in quantitative research .

The dependent variables usually go on the x-axis, whereas the independent variables are on the y-axis. The independent vs. dependent variables illustration type will depend on your formulated research question.

• Bar charts are perfect for categorical independent variables
• Line or scatter pot graphs are perfect where both your variables are quantitative

You collect data on the blood sugar levels of diabetic patients before and after a novel treatment over a specific period. Place the independent variable, the treatment level, on the x-axis, and the dependent variable, the blood sugar level, on the y-axis.

Plot bars for each treatment group before and after the treatment to illustrate the blood sugar difference. It will show a slight difference in blood sugar levels in the placebo and low-dose subject groups and substantial improvements in the high-dose group.

What are independent and dependent variables?

The difference between independent vs. dependent variables is that independent variables are manipulated in research, while the dependent variables are affected by the changes in other variables.

Can one variable be both independent and dependent?

No, there is a difference in independent vs. dependent variables. So, a value can be dependent or independent, but not both.

Can you use more than one independent or dependent variable in research?

Yes, despite the independent vs. dependent variables distinctions, you can use more than one of each in a complex study with multiple research questions.

What is the key distinction between independent vs. dependent variables?

The key difference between independent vs. dependent variables is that independent variables are the cause, while dependent variables are the effect.

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• Independent vs Dependent Variables | Definition & Examples

Independent vs Dependent Variables | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 17 October 2022.

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 cause-and-effect 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.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

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, visualising independent and dependent variables, frequently asked questions about independent and dependent variables.

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)
• Right-hand-side variables (they appear on the right-hand 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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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.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

• A low-dose experimental group
• A high-dose experimental group
• A placebo group

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 quasi-experimental design because there’s no random assignment.

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women, and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

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)
• Left-hand-side variables (they appear on the left-hand 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.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic 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.

Recognising 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?

Recognising 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 are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

For experimental data, you analyse your results by generating descriptive statistics and visualising 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 analyse your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualise the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualisation you use depends on the variable types in your research questions:

• A bar chart is ideal when you have a categorical independent variable.
• A scatterplot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

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.

• Right-hand-side variables (they appear on the right-hand side of a regression equation)

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:

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.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – 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 cola.

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.

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.

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What Is a Dependent Variable?

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A dependent variable is the variable that is tested and measured in a scientific experiment. It is sometimes called the responding variable.

The dependent variable gets its name from the fact that it depends on the independent variable. As the experimenter manipulates the independent variable, a change in the dependent variable is observed and recorded.

Dependent Variable Examples

Imagine that a scientist is testing the effect of light and dark on the behavior of moths by switching a light on and off. The independent variable is the amount of light, and the dependent variable is the moths' reaction. A change in the independent variable (amount of light) directly causes a change in the dependent variable (moth behavior).

Another example of a dependent variable is a test score. How well you perform on a test depends on other variables, such as how much you studied, the amount of sleep you had the night before, whether you had breakfast that morning, and so on. The manipulation of these independent variables has an effect on the dependent variable (the test score).

In general, if you are studying the effect of a certain factor or the outcome of an experiment, the effect or outcome is the dependent variable. If you measure the effect of temperature on flower color, temperature is the independent variable—the one you manipulate—while the color of the flower is the dependent variable.

Graphing a Dependent Variable

When independent and dependent variables are plotted on a graph, the independent variable is usually measured along the x-axis and the dependent variable along the y-axis. For example, if you were examining the effect of sleep on test scores, the number of hours participants slept would be plotted along the x-axis, while the test scores they earned would be plotted along the y-axis.

• What Are Independent and Dependent Variables?
• Dependent Variable Definition and Examples
• Scientific Variable
• The Differences Between Explanatory and Response Variables
• The Significance of Negative Slope
• What Are the Elements of a Good Hypothesis?
• What Is a Hypothesis? (Science)
• What Is an Experiment? Definition and Design
• Six Steps of the Scientific Method
• Independent Variable Definition and Examples
• How To Design a Science Fair Experiment
• Scientific Method Vocabulary Terms
• Difference Between Independent and Dependent Variables
• What Is a Bar Graph?
• Scientific Method Flow Chart
• What Is a Testable Hypothesis?

Research Methodology

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your research objectives. Methodology is the first step in planning a research project.

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Scientific method, qualitative research, experiments.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

Learn More: Steps of the Scientific Method

Variables apply to experimental investigations. The independent variable is the variable the experimenter manipulates or changes. The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable.

Learn More: Independent and Dependent Variables

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.

Learn More: P-Value and Statistical Significance

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data. Qualitative research can be used to gain a deep contextual understanding of the subjective social reality of individuals.

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups.

Learn More: How the Experimental Method Works in Psychology

Frequent Asked Questions

What does p-value of 0.05 mean?

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the results have occurred by random chance rather than a real effect. Therefore, we reject the null hypothesis and accept the alternative hypothesis.

However, it is important to note that the p-value is not the only factor that should be considered when interpreting the results of a hypothesis test. Other factors, such as effect size, should also be considered.

Learn More: What A p-Value Tells You About Statistical Significance

What does z-score tell you?

A  z-score  describes the position of a raw score in terms of its distance from the mean when measured in standard deviation units. It is also known as a standard score because it allows the comparison of scores on different variables by standardizing the distribution. The z-score is positive if the value lies above the mean and negative if it lies below the mean.

Learn More: Z-Score: Definition, Calculation, Formula, & Interpretation

What is an independent vs dependent variable?

The independent variable is the variable the experimenter manipulates or changes and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

Learn More : What are Independent and Dependent Variables?

What is the difference between qualitative and quantitative?

Quantitative data is numerical information about quantities and qualitative data is descriptive and regards phenomena that can be observed but not measured, such as language.

Learn More: What’s the difference between qualitative and quantitative research?

Explore Research Methodology

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Internal vs. External Validity In Psychology

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Double-Blind Experimental Study And Procedure Explained

Observer Bias: Definition, Examples & Prevention

Sampling Bias: Types, Examples & How to Avoid It

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What Is A Case Control Study?

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Control Group vs Experimental Group

Convenience Sampling: Definition, Method and Examples

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Ethnocentrism In Psychology: Examples, Disadvantages, & Cultural Relativism

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The Independent Variable vs. Dependent Variable in Research

In any scientific research, there are typically two variables of interest: independent variables and dependent variables. In forming the backbone of scientific experiments , they help scientists understand relationships, predict outcomes and, in general, make sense of the factors that they're investigating.

Understanding the independent variable vs. dependent variable is so fundamental to scientific research that you need to have a good handle on both if you want to design your own research study or interpret others' findings.

To grasp the distinction between the two, let's delve into their definitions and roles.

What Is an Independent Variable?

The independent variable, often denoted as X, is the variable that is manipulated or controlled by the researcher intentionally. It's the factor that researchers believe may have a causal effect on the dependent variable.

In simpler terms, the independent variable is the variable you change or vary in an experiment so you can observe its impact on the dependent variable.

What Is a Dependent Variable?

The dependent variable, often represented as Y, is the variable that is observed and measured to determine the outcome of the experiment.

In other words, the dependent variable is the variable that is affected by the changes in the independent variable. The values of the dependent variable always depend on the independent variable.

Research Study Example

Let's consider an example to illustrate these concepts. Imagine you're conducting a research study aiming to investigate the effect of studying techniques on test scores among students.

In this scenario, the independent variable manipulated would be the studying technique, which you could vary by employing different methods, such as spaced repetition, summarization or practice testing.

The dependent variable, in this case, would be the test scores of the students. As the researcher following the scientific method , you would manipulate the independent variable (the studying technique) and then measure its impact on the dependent variable (the test scores).

Predictor Variables vs. Outcome Variables

You can also categorize variables as predictor variables or outcome variables. Sometimes a researcher will refer to the independent variable as the predictor variable since they use it to predict or explain changes in the dependent variable, which is also known as the outcome variable.

Other Variables

When conducting an experiment or study, it's crucial to acknowledge the presence of other variables, or extraneous variables, which may influence the outcome of the experiment but are not the focus of study.

These variables can potentially confound the results if they aren't controlled. In the example from above, other variables might include the students' prior knowledge, level of motivation, time spent studying and preferred learning style.

As a researcher, it would be your goal to control these extraneous variables to ensure you can attribute any observed differences in the dependent variable to changes in the independent variable. In practice, however, it's not always possible to control every variable.

The Relationship Between Independent and Dependent Variables

The distinction between independent and dependent variables is essential for designing and conducting research studies and experiments effectively.

By manipulating the independent variable and measuring its impact on the dependent variable while controlling for other factors, researchers can gain insights into the factors that influence outcomes in their respective fields.

Whether investigating the effects of a new drug on blood pressure or studying the relationship between socioeconomic factors and academic performance, understanding the role of independent and dependent variables is essential for advancing knowledge and making informed decisions.

Correlation vs. Causation

Understanding the relationship between independent and dependent variables is essential for making sense of research findings. Depending on the nature of this relationship, researchers may identify correlations or infer causation between the variables.

Correlation implies that changes in one variable are associated with changes in another variable, while causation suggests that changes in the independent variable directly cause changes in the dependent variable.

Control and Intervention

In experimental research, the researcher has control over the independent variable, allowing them to manipulate it to observe its effects on the dependent variable. This controlled manipulation distinguishes experiments from other types of research designs.

For example, in observational studies, researchers merely observe variables without intervention, meaning they don't control or manipulate any variables.

Context and Analysis

Whether it's intentional or unintentional, independent, dependent and other variables can vary in different contexts, and their effects may differ based on various factors, such as age, characteristics of the participants, environmental influences and so on.

Researchers employ statistical analysis techniques to measure and analyze the relationships between these variables, helping them to draw meaningful conclusions from their data.

We created this article in conjunction with AI technology, then made sure it was fact-checked and edited by a HowStuffWorks editor.

Original article: The Independent Variable vs. Dependent Variable in Research

Copyright © 2024 HowStuffWorks, a division of InfoSpace Holdings, LLC, a System1 Company

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Home » Independent Variable – Definition, Types and Examples

Independent Variable – Definition, Types and Examples

Table of Contents

Independent Variable

Definition:

Independent variable is a variable that is manipulated or changed by the researcher to observe its effect on the dependent variable. It is also known as the predictor variable or explanatory variable

The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome. The relationship between the independent variable and the dependent variable is often analyzed using statistical methods to determine the strength and direction of the relationship.

Types of Independent Variables

Types of Independent Variables are as follows:

Categorical Independent Variables

These variables are categorical or nominal in nature and represent a group or category. Examples of categorical independent variables include gender, ethnicity, marital status, and educational level.

Continuous Independent Variables

These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure.

Discrete Independent Variables

These variables are discrete in nature and can only take on specific values. Examples of discrete independent variables include the number of siblings, the number of children in a family, and the number of pets owned.

Binary Independent Variables

These variables are dichotomous or binary in nature, meaning they can take on only two values. Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker.

Controlled Independent Variables

These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus.

Independent Variable and dependent variable Analysis Methods

Following analysis methods that can be used to examine the relationship between an independent variable and a dependent variable:

Correlation Analysis

This method is used to determine the strength and direction of the relationship between two continuous variables. Correlation coefficients such as Pearson’s r or Spearman’s rho are used to quantify the strength and direction of the relationship.

ANOVA (Analysis of Variance)

This method is used to compare the means of two or more groups for a continuous dependent variable. ANOVA can be used to test the effect of a categorical independent variable on a continuous dependent variable.

Regression Analysis

This method is used to examine the relationship between a dependent variable and one or more independent variables. Linear regression is a common type of regression analysis that can be used to predict the value of the dependent variable based on the value of one or more independent variables.

Chi-square Test

This method is used to test the association between two categorical variables. It can be used to examine the relationship between a categorical independent variable and a categorical dependent variable.

This method is used to compare the means of two groups for a continuous dependent variable. It can be used to test the effect of a binary independent variable on a continuous dependent variable.

Measuring Scales of Independent Variable

There are four commonly used Measuring Scales of Independent Variables:

• Nominal Scale : This scale is used for variables that can be categorized but have no inherent order or numerical value. Examples of nominal variables include gender, race, and occupation.
• Ordinal Scale : This scale is used for variables that can be categorized and have a natural order but no specific numerical value. Examples of ordinal variables include levels of education (e.g., high school, bachelor’s degree, master’s degree), socioeconomic status (e.g., low, middle, high), and Likert scales (e.g., strongly disagree, disagree, neutral, agree, strongly agree).
• I nterval Scale : This scale is used for variables that have a numerical value and a consistent unit of measurement but no true zero point. Examples of interval variables include temperature in Celsius or Fahrenheit, IQ scores, and time of day.
• Ratio Scale: This scale is used for variables that have a numerical value, a consistent unit of measurement, and a true zero point. Examples of ratio variables include height, weight, and income.

Independent Variable Examples

Here are some examples of independent variables:

• In a study examining the effects of a new medication on blood pressure, the independent variable would be the medication itself.
• In a study comparing the academic performance of male and female students, the independent variable would be gender.
• In a study investigating the effects of different types of exercise on weight loss, the independent variable would be the type of exercise performed.
• In a study examining the relationship between age and income, the independent variable would be age.
• In a study investigating the effects of different types of music on mood, the independent variable would be the type of music played.
• In a study examining the effects of different teaching strategies on student test scores, the independent variable would be the teaching strategy used.
• In a study investigating the effects of caffeine on reaction time, the independent variable would be the amount of caffeine consumed.
• In a study comparing the effects of two different fertilizers on plant growth, the independent variable would be the type of fertilizer used.

Independent variable vs Dependent variable

Applications of independent variable.

Applications of Independent Variable in different fields are as follows:

• Scientific experiments : Independent variables are commonly used in scientific experiments to study the cause-and-effect relationships between different variables. By controlling and manipulating the independent variable, scientists can observe how changes in that variable affect the dependent variable.
• Market research: Independent variables are also used in market research to study consumer behavior. For example, researchers may manipulate the price of a product (independent variable) to see how it affects consumer demand (dependent variable).
• Psychology: In psychology, independent variables are often used to study the effects of different treatments or therapies on mental health conditions. For example, researchers may manipulate the type of therapy (independent variable) to see how it affects a patient’s symptoms (dependent variable).
• Education: Independent variables are used in educational research to study the effects of different teaching methods or interventions on student learning outcomes. For example, researchers may manipulate the teaching method (independent variable) to see how it affects student performance on a test (dependent variable).

Purpose of Independent Variable

The purpose of an independent variable is to manipulate or control it in order to observe its effect on the dependent variable. In other words, the independent variable is the variable that is being tested or studied to see if it has an effect on the dependent variable.

The independent variable is often manipulated by the researcher in order to create different experimental conditions. By varying the independent variable, the researcher can observe how the dependent variable changes in response. For example, in a study of the effects of caffeine on memory, the independent variable would be the amount of caffeine consumed, while the dependent variable would be memory performance.

The main purpose of the independent variable is to determine causality. By manipulating the independent variable and observing its effect on the dependent variable, researchers can determine whether there is a causal relationship between the two variables. This is important for understanding how different variables affect each other and for making predictions about how changes in one variable will affect other variables.

When to use Independent Variable

Here are some situations when an independent variable may be used:

• When studying cause-and-effect relationships: Independent variables are often used in studies that aim to establish causal relationships between variables. By manipulating the independent variable and observing the effect on the dependent variable, researchers can determine whether there is a cause-and-effect relationship between the two variables.
• When comparing groups or conditions: Independent variables can also be used to compare groups or conditions. For example, a researcher might manipulate an independent variable (such as a treatment or intervention) and observe the effect on a dependent variable (such as a symptom or behavior) in two different groups of participants (such as a treatment group and a control group).
• When testing hypotheses: Independent variables are used to test hypotheses about how different variables are related. By manipulating the independent variable and observing the effect on the dependent variable, researchers can test whether their hypotheses are supported or not.

Characteristics of Independent Variable

Here are some of the characteristics of independent variables:

• Manipulation: The independent variable is manipulated by the researcher in order to create different experimental conditions. The researcher changes the level or value of the independent variable to observe how it affects the dependent variable.
• Control : The independent variable is controlled by the researcher to ensure that it is the only variable that is changing in the experiment. By controlling other variables that might affect the dependent variable, the researcher can isolate the effect of the independent variable on the dependent variable.
• Categorical or continuous: Independent variables can be either categorical or continuous. Categorical independent variables have distinct categories or levels that are not ordered (e.g., gender, ethnicity), while continuous independent variables are measured on a scale (e.g., age, temperature).
• Treatment : In some experiments, the independent variable represents a treatment or intervention that is being tested. For example, a researcher might manipulate the independent variable by giving participants a new medication or therapy.
• Random assignment : In order to control for extraneous variables and ensure that the independent variable is the only variable that is changing, participants are often randomly assigned to different levels of the independent variable. This helps to ensure that any differences between the groups are not due to pre-existing differences between the participants.

Advantages of Independent Variable

Independent variables have several advantages, including:

• Control : Independent variables allow researchers to control the variables being studied, which helps to establish cause-and-effect relationships. By manipulating the independent variable, researchers can see how changes in that variable affect the dependent variable.
• Replication : Manipulating independent variables allows researchers to replicate studies to confirm or refute previous findings. By controlling the independent variable, researchers can ensure that any differences in the dependent variable are due to the manipulation of the independent variable, rather than other factors.
• Predictive Powe r: Independent variables can be used to predict future outcomes. By examining how changes in the independent variable affect the dependent variable, researchers can make predictions about how the dependent variable will respond in the future.
• Precision : Independent variables can help to increase the precision of a study by allowing researchers to control for extraneous variables that might otherwise confound the results. This can lead to more accurate and reliable findings.
• Generalizability : Independent variables can help to increase the generalizability of a study by allowing researchers to manipulate variables in a way that reflects real-world conditions. This can help to ensure that findings are applicable to a wider range of situations and contexts.

Disadvantages of Independent Variable

Independent variables also have several disadvantages, including:

• Artificiality : In some cases, manipulating the independent variable in a study may create an artificial environment that does not reflect real-world conditions. This can limit the generalizability of the findings.
• Ethical concerns: Manipulating independent variables in some studies may raise ethical concerns, such as when human participants are subjected to potentially harmful or uncomfortable conditions.
• Limitations in measuring variables: Some variables may be difficult or impossible to manipulate in a study. For example, it may be difficult to manipulate someone’s age or gender, which can limit the researcher’s ability to study the effects of these variables.
• Complexity : Some variables may be very complex, making it difficult to determine which variables are independent and which are dependent. This can make it challenging to design a study that effectively examines the relationship between variables.
• Extraneous variables : Even when researchers manipulate the independent variable, other variables may still affect the results. These extraneous variables can confound the results, making it difficult to draw clear conclusions about the relationship between the independent and dependent variables.

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