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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.

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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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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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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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!

A control variable is intentionally held constant (it doesn't vary) to ensure it doesn’t have an influence on any other variables.

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.

Mediating variables can explain the relationship between the independent and dependent variable, including whether it's causal or not.

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!

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study.

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 .

Psst… there’s more (for free)

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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Independent and Dependent Variables Examples

The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.

Independent Variable

The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.

Dependent Variable

The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”

Examples of Independent and Dependent Variables

Here are several examples of independent and dependent variables in experiments:

In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
You want to know which brand of fertilizer is best for your plants. The brand of fertilizer is the independent variable. The health of the plants (height, amount and size of flowers and fruit, color) is the dependent variable.
You want to compare brands of paper towels, to see which holds the most liquid. The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel.
You suspect the amount of television a person watches is related to their age. Age is the independent variable. How many minutes or hours of television a person watches is the dependent variable.
You think rising sea temperatures might affect the amount of algae in the water. The water temperature is the independent variable. The mass of algae is the dependent variable.
In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed is the dependent variable.
If you want to know whether caffeine affects your appetite, the presence/absence or amount of caffeine is the independent variable. Appetite is the dependent variable.
You want to know which brand of microwave popcorn pops the best. The brand of popcorn is the independent variable. The number of popped kernels is the dependent variable. Of course, you could also measure the number of unpopped kernels instead.
You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and reproduces) is the dependent variable. A follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical is the independent variable and the rat health is the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).

This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.

How to Graph Independent and Dependent Variables

Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis

Babbie, Earl R. (2009). The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN 0-495-59841-0.
di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 978-0-521-29925-1.
Gauch, Hugh G. Jr. (2003). Scientific Method in Practice . Cambridge University Press. ISBN 978-0-521-01708-4.
Popper, Karl R. (2003). Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge. ISBN 0-415-28594-1.

Independent and Dependent Variables

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

Independent and Dependent Variables Examples

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

Organizing Your Social Sciences Research Paper: Independent and Dependent Variables

Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Reading Research Effectively
Primary Sources
Secondary Sources
Tiertiary Sources
What Is Scholarly vs. Popular?
Qualitative Methods
Quantitative Methods
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Annotated Bibliography
Dealing with Nervousness
Using Visual Aids
Grading Someone Else's Paper
Types of Structured Group Activities
Group Project Survival Skills
Multiple Book Review Essay
Reviewing Collected Essays
Writing a Case Study
About Informed Consent
Writing Field Notes
Writing a Policy Memo
Writing a Research Proposal
Bibliography

Definitions

Identifying Dependent and Indepent Variables

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.

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

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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Next: Glossary of Research Terms >>
Last Updated: Jan 17, 2023 10:50 AM
URL: https://libguides.pointloma.edu/ResearchPaper

Independent and Dependent Variables

This guide discusses how to identify independent and dependent variables effectively and incorporate their description within the body of a research paper.

A variable can be anything you might aim to measure in your study, whether in the form of numerical data or reflecting complex phenomena such as feelings or reactions. Dependent variables change due to the other factors measured, especially if a study employs an experimental or semi-experimental design. Independent variables are stable: they are both presumed causes and conditions in the environment or milieu being manipulated.

Identifying Independent and Dependent Variables

Even though the definitions of the terms independent and dependent variables may appear to be clear, in the process of analyzing data resulting from actual research, identifying the variables properly might be challenging. Here is a simple rule that you can apply at all times: the independent variable is what a researcher changes, whereas the dependent variable is affected by these changes. To illustrate the difference, a number of examples are provided below.

The purpose of Study 1 is to measure the impact of different plant fertilizers on how many fruits apple trees bear. Independent variable : plant fertilizers (chosen by researchers) Dependent variable : fruits that the trees bear (affected by choice of fertilizers)
The purpose of Study 2 is to find an association between living in close vicinity to hydraulic fracturing sites and respiratory diseases. Independent variable: proximity to hydraulic fracturing sites (a presumed cause and a condition of the environment) Dependent variable: the percentage/ likelihood of suffering from respiratory diseases

Confusion is possible in identifying independent and dependent variables in the social sciences. When considering psychological phenomena and human behavior, it can be difficult to distinguish between cause and effect. For example, the purpose of Study 3 is to establish how tactics for coping with stress are linked to the level of stress-resilience in college students. Even though it is feasible to speculate that these variables are interdependent, the following factors should be taken into account in order to clearly define which variable is dependent and which is interdependent.

The dependent variable is usually the objective of the research. In the study under examination, the levels of stress resilience are being investigated.
The independent variable precedes the dependent variable. The chosen stress-related coping techniques help to build resilience; thus, they occur earlier.

Writing Style and Structure

Usually, the variables are first described in the introduction of a research paper and then in the method section. No strict guidelines for approaching the subject exist; however, academic writing demands that the researcher make clear and concise statements. It is only reasonable not to leave readers guessing which of the variables is dependent and which is independent. The description should reflect the literature review, where both types of variables are identified in the context of the previous research. For instance, in the case of Study 3, a researcher would have to provide an explanation as to the meaning of stress resilience and coping tactics.

In properly organizing a research paper, it is essential to outline and operationalize the appropriate independent and dependent variables. Moreover, the paper should differentiate clearly between independent and dependent variables. Finding the dependent variable is typically the objective of a study, whereas independent variables reflect influencing factors that can be manipulated. Distinguishing between the two types of variables in social sciences may be somewhat challenging as it can be easy to confuse cause with effect. Academic format calls for the author to mention the variables in the introduction and then provide a detailed description in the method section.

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What is an independent variable?

Last updated

14 February 2023

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Independent variables are features or values fixed within the population or study under investigation. An example might be a subject's age within a study - other variables, such as what they eat, how long they sleep, and how much TV they watch wouldn't change the subject's age.

On the other hand, a dependent variable can be influenced by other factors or variables. For example, how well you perform on a series of tests (a dependent variable) could be influenced by how long you study or how much sleep you get before the night of the exam.

A better understanding of independent variables, specifically the types, how they function in research contexts, and how to distinguish them from dependent variables, will assist you in determining how to identify them in your studies.

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

Types of independent variables

Independent variables can be of several types, depending on the hypothesis and research. However, the most common types are experimental independent variables and subject variables.

Experimental independent variables

Experimental variables are those that can be directly manipulated in a study. In other words, these are independent variables that you can manipulate to discover how they influence your dependent variables.

For example, you may have two study groups split by independent variables: one receiving a new drug treatment and one receiving a placebo. These types of studies generally require the random assignment of research participants to different groups to observe how results vary based on the influence of different independent variables.

A proper experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so they don't affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the experimental independent variable manipulation.

Subject variables

Subject variables are independent variables that can't be changed in a study but can be used to categorize study participants. They are mostly features that differ between study subjects. For instance, as a social researcher, you can use gender identification, race, education level, or income as key independent variables to classify your research subjects.

Unlike experimental variables, subject variables necessitate a quasi-experimental approach because there is no random assignment. This type of independent variable comprises features and attributes inherent within study participants; therefore, they cannot be assigned randomly.

Instead, you can develop a research approach in which you evaluate the findings of different groups of participants based on their features. It is important to note that any research design that uses non-random assignment is vulnerable to study biases such as sampling and selection bias.

What is the importance of independent variables?

As noted previously, independent variables are critical in developing a study design. This is because they assist researchers in determining cause-and-effect relationships. Controlled experiments require minimal to no outside influence to make conclusions.

Identifying independent variables is one way to eliminate external influences and achieve greater certainty that research results are representative. By controlling for outside influences as much as possible, you can make meaningful inferences about the link between independent and dependent variables.

In most cases, changes in the independent variables cause changes in the dependent variables. For example, if you change an independent variable such as age, you might expect a dependent variable such as cognitive function or running speed to change if the age difference is large. However, there are situations when variations in the independent variables do not influence the dependent variable.

How can you choose an independent variable?

Choosing independent variables within your research will be driven by the objectives of your study. Start by formulating a hypothesis about the outcome you anticipate, and then choose independent variables that you believe will significantly influence the dependent variables.

Make sure you have experimental and control groups that have identical features. They should only differ based on the treatment they get for the independent variable. In this case, your control group will undergo no treatment or changes in the independent variable, versus the experimental group, which will receive the treatment or a wide variation of the independent variable.

How to include an independent variable in an experiment

The type of study or experiment greatly impacts the nature of an independent variable. If you are doing an experiment involving a control condition or group, you will need to monitor and define the values of the independent variables you are using within test condition groups.

In an observational experiment, the explanatory variables' values are not predetermined, but instead are observed in their natural surroundings.

Model specification is the process of deciding which independent variables to incorporate into a statistical model. It involves extensive study, numerous specific topics, and statistical aspects.

Including one independent variable in a regression model entails performing a simple regression, while for more than one independent variable, it is a multiple regression. The names might be different, but the analysis, interpretation, and assumptions are all the same.

What are some examples of independent variables?

To better understand the concept of independent variables, have a look at these few examples used in different contexts:

Mental health context: As a medical researcher, you may be interested in finding out whether a new type of treatment can reduce anxiety in people suffering from a social anxiety disorder. Your study can include three groups of patients. One group receives the new treatment, another gets a different treatment, and the last gets no treatment. The type of treatment is the independent variable.

Workplace context: In this case, you may want to know if giving employees greater control over how they perform their duties results in increased job satisfaction. Your study will involve two groups of employees, one with a lot of say over how they do their jobs and the other without. In this scenario, the independent variable is the amount of control the employees have over their job.

Educational context: You can conduct a study to see if after-school math tutoring improves student performance on standardized math tests. In this example, one group of students will attend an after-school tutoring session three times a week, whereas another group will not receive this extra help. The independent variable is the involvement in after-school math tutoring sessions.

Organization context: You may want to know if the color of an office affects work efficiency. Your research will consider a group of employees working in white or yellow rooms. The independent variable is the color of the office.

What is a dependent variable?

A dependent variable changes as a result of the manipulation of the independent variable. In a nutshell, it is what you test or measure in an experiment. It is also known as a response variable since it responds to changes in another variable, or known as an outcome variable because it represents the outcome you want to measure.

Statisticians also denote these as left-hand side variables because they are typically found on the left-hand side of a regression model. Typically, dependent variables are plotted on the y-axis of graphs.

For instance, in a study designed to evaluate how a certain treatment affects the symptoms of psychological disorders, the dependent variable might be identified as the severity of the symptoms a patient experiences. The treatment used would be the independent variable.

The results of an experiment are important because they can assist you in determining the extent to which changes in your independent variable cause variations in your dependent variable. They can also help forecast the degree to which your dependent variable will vary due to changes in the independent variable.

Identifying independent vs. dependent variables

It can be challenging to differentiate between independent and dependent variables, especially when designing comprehensive research. In some circumstances, a dependent variable from one research study will be used as an independent variable in another. The key is to pay close attention to the study design.

Recognizing independent variables

To recognize independent variables in research, focus on determining whether the variable causes variation in another variable. Independent variables are also manipulated variables whose values are determined by the researchers. In certain experiments, notably in medicine, they are described as risk factors; whereas in others, they are referred to as experimental factors.

Keep in mind that control groups and treatments are often independent variables. And studies that use this approach tend to classify independent variables as categorical grouping variables that establish the experimental groups.

The approaches used to identify independent variables in observational research differ slightly. In these studies, independent variables explain, predict, or correlate with variation in the dependent variable. The study results are also changed or regulated by a variable. If you see an estimated impact size, it is an independent variable, irrespective of the type of study you are reading or designing.

Recognizing dependent variables

To identify dependent variables, you must first determine if the variable is measurable within the research. Also, determine whether the variable relies on another variable in the experiment. If you discover that a variable is only subject to change or variability after other variables have been changed, it may be a dependent variable.

Independent and dependent variables in research

Both independent and dependent variables are mainly used in quasi-experimental and experimental studies. When conducting research, you can generate descriptive statistics to illustrate results. Following that, you would choose a suitable statistical test to validate your hypothesis.

The kind of variable, measurement level, and several independent variable levels will significantly influence your chosen test. Many studies use either the ANOVA or the t-test for data analysis and to obtain answers to research questions .

Other key variables

Other variables, in addition to independent and dependent variables, may have a major impact on a research outcome. Thus, it is vital to identify and take control of extraneous variables since they can cause variation in the relationship between the independent and dependent variables.

Some examples of extraneous variables include demand characteristics and experimenter effects. When these variables cannot be controlled in an experiment, they are usually called confounding variables .

Visualizing independent and dependent variables

You can use either a chart or a graph to visualize quantitative research results. Graphs have a typical display in which the independent variables lie on the horizontal x-axis and the dependent variables on the vertical y-axis. The presentation of data will depend on the nature of the variables in your research questions.

The lowdown

Having a working knowledge of independent and dependent variables is key to understanding how research projects work. There are various ways to think of independent variables. However, the best approach is to picture the independent variable as what you change and the dependent variable as what is influenced due to the variation.

In other words, consider the independent variable the cause and the dependent variable the effect. When visualizing these variables in a graph, place the independent variable on the x-axis and the dependent variable on the y-axis.

It is also essential to remember that there are other variables aside from the independent and dependent variables that might impact the outcome of an experiment. As a result, you should identify and control extraneous variables as much as possible to make a valid conclusion about the study findings.

What are the dependent and independent variables in research?

An independent variable in research or an experiment is what the researcher manipulates or changes. The dependent variable, on the other hand, is what is measured. In general, the independent variable is in charge of influencing the dependent variable.

What are the variables in research examples?

In research or an experiment, a variable refers to something that can be tested. You can use independent and dependent variables to design research .

Can a variable be both independent and dependent at the same time?

No, because a dependent variable is reliant on the independent variable. Thus, a variable in a study can only be the cause (independent) or the effect (dependent). However, there are also cases in which a dependent variable from one study is used as an independent variable in another.

Can a study have more than one independent or dependent variable?

Yes, however, a study must include various research questions for multiple independent and dependent variables to be effective.

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Independent Variables in Psychology

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

Amanda Tust is a fact-checker, researcher, and writer with a Master of Science in Journalism from Northwestern University's Medill School of Journalism.

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Identifying

Potential Pitfalls

The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment.

For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).

In general, experiments have these three types of variables: independent, dependent, and controlled.

Identifying the Independent Variable

If you are having trouble identifying the independent variables of an experiment, there are some questions that may help:

Is the variable one that is being manipulated by the experimenters?
Are researchers trying to identify how the variable influences another variable?
Is the variable something that cannot be changed but that is not dependent on other variables in the experiment?

Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants).

Below are the key differences when looking at an independent variable vs. dependent variable.

Expected to influence the dependent variable

Doesn't change as a result of the experiment

Can be manipulated by researchers in order to study the dependent variable

Expected to be affected by the independent variable

Expected to change as a result of the experiment

Not manipulated by researchers; its changes occur as a result of the independent variable

There can be all different types of independent variables. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating.

Independent variables also have different levels. In some experiments, there may only be one level of an IV. In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have.

In an experiment on the effects of the type of diet on weight loss, for example, researchers might look at several different types of diet. Each type of diet that the experimenters look at would be a different level of the independent variable while weight loss would always be the dependent variable.

To understand this concept, it's helpful to take a look at the independent variable in research examples.

In Organizations

A researcher wants to determine if the color of an office has any effect on worker productivity. In an experiment, one group of workers performs a task in a yellow room while another performs the same task in a blue room. In this example, the color of the office is the independent variable.

In the Workplace

A business wants to determine if giving employees more control over how to do their work leads to increased job satisfaction. In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not. The amount of input the workers have over their work is the independent variable in this example.

In Educational Research

Educators are interested in whether participating in after-school math tutoring can increase scores on standardized math exams. In an experiment, one group of students attends an after-school tutoring session twice a week while another group of students does not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable.

In Mental Health Research

Researchers want to determine if a new type of treatment will lead to a reduction in anxiety for patients living with social phobia. In an experiment, some volunteers receive the new treatment, another group receives a different treatment, and a third group receives no treatment. The independent variable in this example is the type of therapy .

Sometimes varying the independent variables will result in changes in the dependent variables. In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured.

At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV.

Choosing an Independent Variable

If you are designing an experiment, here are a few tips for choosing an independent variable (or variables):

Select independent variables that you think will cause changes in another variable. Come up with a hypothesis for what you expect to happen.
Look at other experiments for examples and identify different types of independent variables.
Keep your control group and experimental groups similar in other characteristics, but vary only the treatment they receive in terms of the independent variable. For example, your control group will receive either no treatment or no changes in the independent variable while your experimental group will receive the treatment or a different level of the independent variable.

It is also important to be aware that there may be other variables that might influence the results of an experiment. Two other kinds of variables that might influence the outcome include:

Extraneous variables : These are variables that might affect the relationships between the independent variable and the dependent variable; experimenters usually try to identify and control for these variables.
Confounding variables : When an extraneous variable cannot be controlled for in an experiment, it is known as a confounding variable.

Extraneous variables can also include demand characteristics (which are clues about how the participants should respond) and experimenter effects (which is when the researchers accidentally provide clues about how a participant will respond).

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

Weiten, W. Psychology: Themes and Variations, 10th ed . Boston, MA: Cengage Learning; 2017.

National Library of Medicine. Dependent and independent variables .

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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2.2: Concepts, Constructs, and Variables

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Anol Bhattacherjee
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We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behavior such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to “gravitate” to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualize. A construct is an abstract concept that is specifically chosen (or “created”) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary , syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ ( intelligence quotient ) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical (abstract) plane, while variables are operationalized and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled for in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as grade point average is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts is more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variable are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualize a nomological network linking these abstract constructs.

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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.

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

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.

Subject variables

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)

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:

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 .

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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Both the independent variable and dependent variable are examined in an experiment using the scientific method , so it's important to know what they are and how to use them. Here are the definitions for independent and dependent variables, examples of each variable, and the explanation for how to graph them.

Independent Variable

The independent variable is the condition that you change in an experiment. It is the variable you control. It is called independent because its value does not depend on and is not affected by the state of any other variable in the experiment. Sometimes you may hear this variable called the "controlled variable" because it is the one that is changed. Do not confuse it with a "control variable," which is a variable that is purposely held constant so that it can't affect the outcome of the experiment.

Dependent Variable

The dependent variable is the condition that you measure in an experiment. You are assessing how it responds to a change in the independent variable, so you can think of it as depending on the independent variable. Sometimes the dependent variable is called the "responding variable."

Independent and Dependent Variable Examples

In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
You want to compare brands of paper towels, to see which holds the most liquid. The independent variable in your experiment would be the brand of paper towel. The dependent variable would be the amount of liquid absorbed by the paper towel.
In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed (the response) is the dependent variable.
If you want to know whether caffeine affects your appetite, the presence/absence of a given amount of caffeine would be the independent variable. How hungry you are would be the dependent variable.
You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and can reproduce) is the dependent variable. If you determine the substance is necessary for proper nutrition, a follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical would be the independent variable and the rat health would be the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you are having a hard time identifying which variable is the independent variable and which is the dependent variable, remember the dependent variable is the one affected by a change in the independent variable. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won't make sense.

Independent variable causes an effect on the dependent variable.

Example : How long you sleep (independent variable) affects your test score (dependent variable).

This makes sense, but:

Example : Your test score affects how long you sleep.

This doesn't really make sense (unless you can't sleep because you are worried you failed a test, but that would be a different experiment).

How to Plot Variables on a Graph

There is a standard method for graphing the independent and dependent variable. The x-axis is the independent variable, while the y-axis is the dependent variable. You can use the DRY MIX acronym to help remember how to graph variables:

D = dependent variable R = responding variable Y = graph on the vertical or y-axis

M = manipulated variable I = independent variable X = graph on the horizontal or x-axis

Test your understanding with the scientific method quiz .

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

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What Is an Independent Variable in Quantitative Research?

What Are the Independent Variables for a Moldy Bread Experiment?

The foundations of quantitative research are variables and there are three main types: dependent, independent and controlled. The researcher will manipulate an independent variable in an effort to understand its effect on the dependent or controlled variable. In other cases when manipulation is not an option, the independent variable is presumed to have an effect on the dependent variable and is called a “status variable” but often treated as an independent variable. However, to draw precise conclusions about the effects of an independent variable, the scientist must use a controlled variable for consistency.

An independent variable is a variable in research that causes a change -- or is presumed will cause a change -- to other variables in the research conducted. Scientists can control the independent variable to monitor those changes or he can presume a change and look for evidence of those changes to the other variables.

How it Works

Let’s say a researcher wants to study the growth of coffee beans. The dependent variables of such study include the number of coffee beans used, the weight of the plants, height of the plant, the size of the leaves and time it takes for the plant to mature.

The independent variables will impact the results of the dependent variable. Those variables may include the amount of water present, the use of fertilizer, the amount of fertilizer used, and temperature; the amount of exposure to sunlight will also affect the dependent variables.

Controlled Variable Importance

If a scientist wants to monitor how two different types of fertilizer (independent variables) effect the growth of the coffee beans, he will need to control all other variables. First he must use the same kind of coffee beans and the same amount of fertilizer to grow both sets of plants. He will need to make to make sure both sets are exposed to the exactly the same amount of water, sunlight and temperatures. These are all controlled variables for the research.

Status Variable

In some situations a researcher cannot manipulate an independent variable, although it may have an effect on the dependent variable. As a technical term scientists may refer to this independent variable as a status variable, but still treat it as an independent variable to further research and record results.

For example, if a social scientist is attempting a quantitative study on cigarette smoking and lung cancer, he cannot manipulate ethnicity of gender of individual subjects; although he suspects both independent variables may affect the body’s reaction to cigarette smoking. These are labeled as status variables and the scientist may look for consistent effects in both gender and ethnicity, while comparing those results to other ethnicities and the opposite gender, to ascertain the impact of the independent variable.

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Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

What are constants & controls of a science project experiment.

15 Independent and Dependent Variable Examples

independent and dependent variables, explained below

An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).

By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.

This can provide very valuable information when studying just about any subject.

Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.

The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.

Definition of Independent and Dependent Variables

The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .

Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.

The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).

Other variations of an experiment might include having multiple levels of the independent variable.

If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.

Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.

These concepts should not be confused with predictor and outcome variables .

Examples of Independent and Dependent Variables

1. gatorade and improved athletic performance.

A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.

If they can back up that claim with hard scientific data, that would be great for sales.

So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.

All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.

In this example, the independent variable is Gatorade, and the dependent variable is heart rate.

2. Chemotherapy and Cancer

A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.

The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.

Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.

In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.

3. Interior Design Color and Eating Rate

A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.

So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.

Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.

The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.

4. Hair Color and Attraction

A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.

Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.

At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.

The independent variable is hair color, and the dependent variable is pupil dilation.

5. Mozart and Math

After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.

During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.

The researchers then compare the scores of the exams between the two groups of classrooms.

Although there are a lot of obvious limitations to this hypothetical, it is the first step.

The independent variable is Mozart, and the dependent variable is exam scores.

6. Essential Oils and Sleep

A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.

The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.

At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.

The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.

7. Teaching Style and Learning

A group of teachers is interested in which teaching method will work best for developing critical thinking skills.

So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.

At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.

The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.

8. Concrete Mix and Bridge Strength

A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.

So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.

In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.

9. Recipe and Consumer Preferences

People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.

The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.

Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.

The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.

10. Protein Supplements and Muscle Mass

A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.

The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.

They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.

At the end of three months, the muscle mass of all participants is measured.

The independent variable is the supplement, and the dependent variable is muscle mass.

11. Air Bags and Skull Fractures

In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.

In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.

The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.

The independent variable was the airbag and the dependent variable was the amount of skull damage.

12. Vitamins and Health

Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.

They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”

Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.

In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.

At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.

In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.

13. Meditation and Stress

Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.

All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.

Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.

How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.

In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).

14. Video Games and Aggression

When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.

Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.

Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.

So many studies have used so many different ways of measuring aggression.

In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.

15. Vehicle Exhaust and Cognitive Performance

Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.

One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.

After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.

One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.

In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.

Read Next: Extraneous Variables Examples

The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.

For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.

The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.

Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941

Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374

Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.

Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66. https://doi.org/10.1080/10790195.2012.10850354

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Positive Punishment Examples
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Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ Perception Checking: 15 Examples and Definition

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Chris Drew (PhD) #molongui-disabled-link 25 Positive Punishment Examples
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Application of Student's t -test, Analysis of Variance, and Covariance

Prabhaker mishra.

Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Uttam Singh

Chandra m pandey, priyadarshni mishra.

1 Department of Ophthalmology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Gaurav Pandey

2 Department of Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Student's t test ( t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value. A significant P value of the ANOVA test indicates for at least one pair, between which the mean difference was statistically significant. To identify that significant pair(s), we use multiple comparisons. In ANOVA, when using one categorical independent variable, it is called one-way ANOVA, whereas for two categorical independent variables, it is called two-way ANOVA. When using at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA. When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.

Introduction

Student's t test ( t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups. For these methods, testing variable (dependent variable) should be in continuous scale and approximate normally distributed. Mean is the representative measure for normally distributed continuous variable and statistical methods used to compare between the means are called parametric methods. For non-normal continuous variable, median is representative measure, and in this situation, comparison between the groups is performed using non-parametric methods. Most parametric test has an alternative nonparametric test.[ 1 , 2 , 3 ]

There are many statistical tests within Student's t test ( t test), ANOVA and ANCOVA, and each test has its own assumptions. Although not every method is popular, some of them can be managed from other available methods. The aim of the present article is to discuss the assumptions, application, and interpretation of the some popular T, ANOVA, and ANCOVA methods i.e., one sample t test, independent samples t test, paired samples t test, one-way ANOVA, two-ways ANOVA, one-way repeated measures ANOVA, two-ways repeated measures ANOVA, one-way ANCOVA, and One-way repeated measures ANCOVA. To understand the above statistical methods, an example [ Table 1 ] with a data set of 20 patients whose age groups, gender, body mass index (BMI), and diastolic blood pressure (DBP) measured at baseline (B/L), 30 min and 60 min are given below. Further, examples related to the above statistical methods are discussed from the given data.

Data of the 20 patients

Age groups: 1 (<30 years), 2 (30-50 years), 3 (>50 years). Gender: M=Male, F=Female, BMI=Body mass index, DBP=Diastolic blood pressure, B/L=Baseline, min=Minute

T test, ANOVA, and ANCOVA

Basic concepts.

The Student's t test (also called T test) is used to compare the means between two groups and there is no need of multiple comparisons as unique P value is observed, whereas ANOVA is used to compare the means among three or more groups.[ 4 , 5 ] In ANOVA, the first gets a common P value. A significant P value of ANOVA test indicates for at least one pair, between which the mean difference was statistically significant.[ 6 ] To identify that significant pair(s), post-hoc test (multiple comparisons) is used. In ANOVA test, when at least one covariate (continuous variable) is adjusted to remove the confounding effect from the result called ANCOVA. ANOVA test (F test) is called “Analysis of Variance” rather than “Analysis of Means” because inferences about means are made by analyzing variance.[ 7 , 8 , 9 ]

Steps in hypothesis testing

Hypothesis building.

Like other tests, there are two kinds of hypotheses; null hypothesis and alternative hypothesis. The alternative hypothesis assumes that there is a statistically significant difference exists between the means, whereas the null hypothesis assumes that there is no statistically significant difference exists between the means.

Computation of test statistics

In these test, first step is to calculate test statistics (called t value in student's t test and F value in ANOVA test) also called calculated value. It is calculated after putting inputs (from the samples) in statistical test formula. In student's t test, calculated t value is ratio of mean difference and standard error, whereas in the ANOVA test, calculated F value is ratio of the variability between groups with the variability of the observations within the groups.[ 1 , 4 ]

Tabulated value

At degree of freedom of the given observations and desired level of the confidence (usually at two-sided test, which is more powerful than one-sided test), corresponding tabulated value of the T test or F test is selected (from the statistical table).[ 1 , 4 ]

Comparison of calculated value with tabulated value and null hypothesis

If the calculated value is greater than the tabulated value, then reject the null hypothesis where null hypothesis states that means are statistically same between the groups.[ 1 , 4 ] As the sample size increases corresponding degree of freedom also increases. For a given level of confidence, higher degree of freedom has lower tabulated value. That's the reason, when the sample size increases, its significance level also improves (i.e., P value is decreasing).

It is one of the most popular statistical techniques used to test whether mean difference between two groups is statistically significant. Null hypothesis stated that both means are statistically equal, whereas alternative hypothesis stated that both means are not statistically equal i.e., they are statistically different to each other.[ 1 , 3 , 7 ] T test are three types i.e., one sample t test, independent samples t test, and paired samples t test.

One-sample t test

The one sample t test is a statistical procedure used to determine whether mean value of a sample is statistically same or different with mean value of its parent population from which sample was drawn. To apply this test, mean, standard deviation (SD), size of the sample (Test variable), and population mean or hypothetical mean value (Test value) are used. Sample should be continuous variable and normally distributed.[ 1 , 9 , 10 , 11 ] One-sample t test is used when sample size is <30. In case sample size is ≥30 used to prefer one sample z test over one sample t test although for one sample z test, population SD must be known. If population SD is not known, one sample t test can be used at any sample size. In one sample Z test, tabulated value is z value (instead of t value in one sample t test). To apply this test through popular statistical software i.e., statistical package for social sciences (SPSS), option can be found in the following menu [Analyze – compare means – one-sample t test].

Example : From Table 1 , BMI (mean ± SD) was given 24.45 ± 2.19, whereas population mean was assumed to be 25.5. One sample t test indicated that mean difference between sample mean and population mean was statistically significantly different to each other ( P = 0.045).

Independent samples t test

The independent t test, also called unpaired t test, is an inferential statistical test that determines whether there is a statistically significant difference between the means in two unrelated (independent) groups?

To apply this test, a continuous normally distributed variable (Test variable) and a categorical variable with two categories (Grouping variable) are used. Further mean, SD, and number of observations of the group 1 and group 2 would be used to compute significance level. In this procedure, first significance level of Levene's test is computed and when it is insignificant ( P > 0.05), equal variances otherwise ( P < 0.05), unequal variances are assumed between the groups and according P value is selected for independent samples t test.[ 1 , 10 , 11 , 12 ] In SPSS [Analyze – compare means – independent samples t test].

Example : From Table 1 , mean BMI of the male ( n = 10) and female ( n = 10) were 24.80 ± 2.20 and 24.10 ± 2.23, respectively. Levene's test ( p = 0.832) indicated that variances between the groups were statistically equal. At equal variances assumed, independent samples t test ( p = 0.489) indicated that mean BMI of the male and female was statistically equal.

Paired samples t test

The paired samples t test, sometimes called the dependent samples t -test, is used to determine whether the change in means between two paired observations is statistically significant? In this test, same subjects are measured at two time points or observed by two different methods.[ 4 ] To apply this test, paired variables (pre-post observations of same subjects) are used where paired variables should be continuous and normally distributed. Further mean and SD of the paired differences and sample size (i.e., no. of pairs) would be used to calculate significance level.[ 1 , 11 , 13 ] In SPSS [Analyze – compare means – paired samples t test].

Example : From Table 1 , DBP of the 20 patients (mean ± SD); at baseline, 30 min and paired differences (difference between baselines and 30 min) were 79.55 ± 4.87, 83.90 ± 5.58, and 4.35 ± 4.16. Paired samples t test indicated that mean difference of paired observations of DBP between baseline and 30 min was statistically significant ( P < 0.001).

ANOVA test (F test)

A statistical technique used to compare the means between three or more groups is known as ANOVA or F test. It is important that ANOVA is an omnibus test statistic. Its significant P value indicates that there is at least one pair in which the mean difference is statistically significant. To determine the specific pair's, post hoc tests (multiple comparisons) are used. There are various ANOVAs test, and their objectives are varying from one test to another. There are two main types of ANOVA i.e., one-way ANOVA and one-way repeated measures ANOVA. First is used for independent observations and later for dependent observations. When used one categorical independent variable called one-way ANOVA, whereas for two categorical independent variables called two-way ANOVA. When used at least one covariate to adjust with dependent variable, ANOVA becomes ANCOVA.[ 1 , 11 , 14 ]

Post-hoc test (multiple comparisons): Post hoc tests (pair-wise multiple comparisons) used to determine the significant pair(s) after ANOVA was found significant. Before applying post-hoc test (in between subjects factors), first need to test the homogeneity of the variances among the groups (Levene's test). If variances are homogeneous ( P ≥ 0.05), select any multiple comparison methods from least significant difference (LSD), Bonferroni, Tukey's, etc.[ 15 , 16 ] If variances are not homogeneous ( P < 0.05), used to select any multiple comparison methods from Games-Howell, Tamhane's T2, etc.[ 15 , 16 ] Bonferroni is a good method for equal variances, whereas Tamhane's T2 for unequal variances as both calculate significance level by controlling error rate. Similarly, for repeated measures ANOVA (RMA) (in within subjects factors), select any method from LSD, Boneferroni, Sidak although Bonferroni might be a better choice. The significance level of each of the multiple comparison method is varying from other methods as each used for a particular situation.

One-way ANOVA

The One-way ANOVA is extension of independent samples t test (In independent samples t test used to compare the means between two independent groups, whereas in one-way ANOVA, means are compared among three or more independent groups). A significant P value of this test refers to multiple comparisons test to identify the significant pair(s).[ 17 ] In this test, one continuous dependent variable and one categorical independent variable are used, where categorical variable has at least three categories. In SPSS [Analyze–compare means–one-way ANOVA].

Example : From Table 1 , 20 patient's DBP (at 30 min) are given. One-way ANOVA test was used to compare the mean DBP in three age groups (independent variable), which was found statistically significant ( p = 0.002). Levene test for homogeneity was insignificant ( p = 0.231), as a result Bonferroni test was used for multiple comparisons, which showed that DBP was significantly different between two pairs i.e., age group of <30 to 30–50 and <30 to >50 ( P < 0.05) but insignificant between one pair i.e., 30–50 to >50 ( P > 0.05).

Two-way ANOVA

The two-way ANOVA is extension of one-way ANOVA [In one-way ANOVA, only one independent variable, whereas in two-way ANOVA, two independent variables are used]. The primary purpose of a two-way ANOVA is to understand whether there is any interrelationship between two independent variables on a dependent variable.[ 18 ] In this test, a continuous dependent variable (approximately normally distributed) and two categorical independent variables are used. In SPSS [Analyze –General Linear Model –Univariate].

Example : From Table 1 , 20 patient's DBP (at 30 min) are given. Two-way ANOVA test was used to compare the mean DBP between age groups (independent variable_1) and gender (independent variable_2), which indicated that there was no significant interaction of DBP with age groups and gender (tests of Between-Subjects effects in age groups*gender; P = 0.626) with effect size (Partial Eta Squared) of 0.065. The result also showed that there was significant difference in estimated marginal means (adjusted mean) of DBP between age groups ( P = 0.005) but insignificant in gender ( P = 0.662), where sex and age groups was adjusted.

One-way repeated measures ANOVA

Repeated Measures ANOVA (RMA) is the extension of the paired t test. RMA is also referred to as within-subjects ANOVA or ANOVA for paired samples. Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or more than two time periods. (In paired samples t test, compared the means between two dependent groups, whereas in RMA, compared the means between three or more dependent groups). Before calculating the significance level, Mauchly's test is used to assess the homogeneity of the variance (also called sphericity) within all possible pairs. When P value of Mauchly's test is insignificant ( P ≥ 0.05), equal variances are assumed and P value for RMA would be taken from sphericity assumed test (Tests of Within-Subjects effects). In case variances are not homogeneous (Mauchly's test: P < 0.05), epsilon (ε) value (which shows the departure of the sphericity, 1 shows perfect sphericity) decides the statistical method to calculate P value for RMA. When ε≥0.75 Huynh-Feldt while for ε< 0.75, Greenhouse-Geisser method (univariate method) or Wilks' lambda (multivariate method) is used to calculate P value for the RMA.[ 19 ] When the RMA is significant, pair-wise comparison contains multiple paired t tests with a Bonferroni correction is used.[ 20 ] In SPSS [Analyze –General Linear Model – Repeated Measures ANOVA].

Example : From Table 1 , 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity indicated that variances were equal ( P = 0.099) between the pairs. RMA tests (i.e., Within-Subjects effects) was assessed using sphericity assumed test ( P value = 0.001), which indicated that change in DBP over the time was statistically significant. Bonferroni multiple comparisons indicated that mean difference was statistically significant between DBP_B/l to DBP_30 min and DBP_30 min to DBP_60 min ( P < 0.05) but insignificant between DBP_B/l to DBP_60 min ( P > 0.05).

Two-way repeated measures ANOVA

Two-way Repeated Measures ANOVA is combination of between-subject and within-subject factors. A two-way RMA (also known as a two-factor RMA or a two-way “Mixed ANOVA”) is extension of one-way RMA [In one-way RMA, use one dependent variable under repeated observations (normally distributed continuous variable) and one categorical independent variable (i.e., time points), whereas in two-way RMA; one additional categorical independent variable is used]. The primary purpose of two-way RMA is to understand if there is an interaction between these two categorical independent variables on the dependent variable (continuous variable). The distribution of the dependent variable in each combination of the related groups should be approximately normally distributed.[ 21 ] In SPSS [Analyze–General Linear Model – Repeated Measures], where second independent variable will be included as between subjects factor.

Example : From Table 1 , 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity ( P = 0.138) indicated that variances were equal between the pairs. Two-way RMA tests for interaction (i.e., Within-Subjects effects) were assessed using sphericity assumed test (DBP*gender: P value = 0.214), which indicated that there was no interaction of gender with time and associated change in DBP over the time was statistically insignificant.

One-way ANCOVA

One-way ANCOVA is extension of one-way ANOVA [In one-way ANOVA, do not adjust the covariate, whereas in the one-way ANCOVA; adjust at least one covariate]. Thus, the one-way ANCOVA tests find out whether the independent variable still influences the dependent variable after the influence of the covariate(s) has been removed (i.e., adjusted). In this test, one continuous dependent variable, one categorical independent variable, and at least one continuous covariate for removing its effect/adjustment are used.[ 8 , 22 ] In SPSS [Analyze - General Linear Model – Univariate].

Example : From Table 1 , 20 patient's DBP at 30 min are given. One-way ANCOVA test was used to compare the mean DBP in three age groups (independent variable) after adjusting the effect of baseline DBP, which was found to be statistically significant ( P = 0.021). As Levene test for homogeneity was insignificant ( P = 0.601), resultant Bonferroni test was used for multiple comparisons, which showed that DBP was significantly different between one pair i.e., age group of <30 to >50 ( P = 0.031) and insignificant between rest two pairs i.e., <30 to 30–50 and 30–50 to >50 ( P > 0.05).

One-way repeated measures ANOCOVA

One-way repeated measures ANCOVA is the extension of the One-way RMA. [In one-way RMA, we do not adjust the covariate, whereas in the one-way repeated measures ANCOVA, we adjust at least one covariate]. Thus, the One-way repeated Measures ANCOVA is used to test whether means are still statistically equal or different after adjusting the effect of the covariate(s).[ 23 , 24 ] In SPSS [Analyze –General Linear Model – Repeated Measures ANOVA].

Example : From Table 1 , 20 patient's DBP were at baseline (79.55 ± 4.87), at 30 min (83.90 ± 5.58), and at 60 min (79.25 ± 5.68). The Mauchly's test of sphericity indicated that variances were equal ( P = 0.093) between the pairs. RMA tests (i.e., Within-Subjects effects) were assessed using sphericity assumed test (DBP*BMI: P value = 0.011), which indicated that change in DBP over the time was statistically significant after adjusting BMI. Bonferroni multiple comparisons indicated that mean difference was statistically significant between DBP_B/l to DBP_30 min and DBP_30 min to DBP_60 min but insignificant between DBP_B/l to DBP_60 min after adjusting BMI.

Conclusions

Student's t test, ANOVA, and ANCOVA are the statistical methods frequently used to analyze the data. Two common things among these methods are dependent variable must be in continuous scale and normally distributed, and comparisons are made between the means. All above methods are parametric method.[ 2 ] When the size of the sample is small, mean is very much affected by the outliers, so it is necessary to keep sufficient sample size while using these methods.

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Conflicts of interest.

There are no conflicts of interest.

Acknowledgments

Authors would like to express their deep and sincere gratitude to Dr. Prabhat Tiwari, Professor, Department of Anaesthesiology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, for his encouragement to write this article. His critical reviews and suggestions were very useful for improvement in the article.

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Independent vs. Dependent Variables
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. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.
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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 ...
Independent Variable
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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 ...
Independent and Dependent Variables Examples
The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel. You suspect the amount of television a person watches is related to their age. Age is the independent variable. ... The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN -495-59841-. di Francia, G ...
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The independent variable precedes the dependent variable. The chosen stress-related coping techniques help to build resilience; thus, they occur earlier. Writing Style and Structure. Usually, the variables are first described in the introduction of a research paper and then in the method section.
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The independent variable is the involvement in after-school math tutoring sessions. Organization context: You may want to know if the color of an office affects work efficiency. Your research will consider a group of employees working in white or yellow rooms. The independent variable is the color of the office.
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The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant ... makes a specific prediction about a new phenomenon10 or a formal statement on the expected relationship between an independent variable and a dependent variable.3,11 It ...
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Application of Student's t-test, Analysis of Variance, and Covariance
Student's t test (t test), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) are statistical methods used in the testing of hypothesis for comparison of means between the groups.The Student's t test is used to compare the means between two groups, whereas ANOVA is used to compare the means among three or more groups. In ANOVA, first gets a common P value.

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Independent vs. Dependent Variables | Definition & Examples

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Recognizing independent variables

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Organizing Your Social Sciences Research Paper

Definitions

Identifying Dependent and Independent Variables

Structure and Writing Style

Independent Variable – Definition, Types and Examples

Independent Variable

Types of Independent Variables

Categorical Independent Variables

Continuous Independent Variables

Discrete Independent Variables

Binary Independent Variables

Controlled Independent Variables

Independent Variable and dependent variable Analysis Methods

Correlation Analysis

ANOVA (Analysis of Variance)

Regression Analysis

Chi-square Test

Measuring Scales of Independent Variable

Independent Variable Examples

Independent variable vs Dependent variable

Purpose of Independent Variable

When to use Independent Variable

Characteristics of Independent Variable

Advantages of Independent Variable

Disadvantages of Independent Variable

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Muhammad Hassan

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