in research what is an independent variable

• Privacy Policy

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Confounding Variable – Definition, Method and...

Qualitative Variable – Types and Examples

Quantitative Variable – Definition, Types and...

Intervening Variable – Definition, Types and...

Dichotomous Variable – Definition Types and...

Categorical Variable – Definition, Types and...

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

What Is an Independent Variable? Definition and Examples

The independent variable is the variable that is controlled or changed in a scientific experiment to test its effect on the dependent variable . It doesn’t depend on another variable and isn’t changed by any factors an experimenter is trying to measure. The independent variable is denoted by the letter  x  in an experiment or graph.

INDEPENDENT VARIABLE EXAMPLE

Two classic examples of independent variables are age and time. They may be measured, but not controlled. In experiments, even if measured time isn’t the variable, it may relate to duration or intensity.

For example, a scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light and the moth’s reaction is the dependent variable.

For another example, say you are measuring whether amount of sleep affects test scores. The hours of sleep would be the independent variable while the test scores would be dependent variable.

A change in the independent variable directly causes a change in the dependent variable. If you have a hypothesis written such that you’re looking at whether  x  affects  y , the  x  is always the independent variable and the  y  is the dependent variable.

GRAPHING THE INDEPENDENT VARIABLE

If the dependent and independent variables are plotted on a graph, the x-axis would be the independent variable and the y-axis would be the dependent variable. You can remember this using the DRY MIX acronym, where DRY means dependent or responsive variable is on the y-axis, while MIX means the manipulated or independent variable is on the x-axis.

Related Posts

Research Variables 101

Independent variables, dependent variables, control variables and more

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

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

Overview: Variables In Research

What (exactly) is a variable?

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

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

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

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

The “Big 3” Variables

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

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

What is an independent variable?

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

For example:

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

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

Need a helping hand?

What is a dependent variable?

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

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

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

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

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

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

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

What is a control variable?

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

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

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

• Temperature
• Time of day
• Noise or distractions

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

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

Other types of variables

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

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

Let’s jump into it…

What is a moderating variable?

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

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

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

What is a mediating variable?

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

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

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

What is a confounding variable?

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

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

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

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

What is a latent variable?

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

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

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

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

Let’s recap

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

To recap, we’ve explored:

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

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

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

Very informative, concise and helpful. Thank you

Helping information.Thanks

practical and well-demonstrated

Very helpful and insightful

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Educational resources and simple solutions for your research journey

Independent vs Dependent Variables: Definitions & Examples

A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.  

Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.  

Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .  

Table of Contents

What is a variable?  

Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.   

As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)  

What is an independent variable?  

Here is a list of the important characteristics of independent variables .( 2,3)  

• An independent variable is the factor that is being manipulated in an experiment.  
• In a research study, independent variables affect or influence dependent variables and cause them to change.  
• Independent variables help gather evidence and draw conclusions about the research subject.  
• They’re also called predictors, factors, treatment variables, explanatory variables, and input variables.  
• On graphs, independent variables are usually placed on the X-axis.  
• Example: In a study on the relationship between screen time and sleep problems, screen time is the independent variable because it influences sleep (the dependent variable).  
• In addition, some factors like age are independent variables because other variables such as a person’s income will not change their age.  

Types of independent variables  

Independent variables in research are of the following two types:( 4)  

Quantitative  

Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”  

Here are a few quantitative independent variables examples :  

• Differences in treatment dosages and frequencies: Useful in determining the appropriate dosage to get the desired outcome.  
• Varying salinities: Useful in determining the range of salinity that organisms can tolerate.  

Qualitative  

Qualitative independent variables are non-numerical variables.  

A few qualitative independent variables examples are listed below:  

• Different strains of a species: Useful in identifying the strain of a crop that is most resistant to a specific disease.  
• Varying methods of how a treatment is administered—oral or intravenous.  

A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.  

What is a dependent variable ?  

Here are a few characteristics of dependent variables: ( 3)  

• A dependent variable represents a quantity whose value depends on the independent variable and how it is changed.  
• The dependent variable is influenced by the independent variable under various circumstances.  
• It is also known as the response variable and outcome variable.  
• On graphs, dependent variables are placed on the Y-axis.  

Here are a few dependent variable examples :  

• In a study on the effect of exercise on mood, the dependent variable is mood because it may change with exercise.  
• In a study on the effect of pH on enzyme activity, the enzyme activity is the dependent variable because it changes with changing pH.   

Types of dependent variables  

Dependent variables are of two types:( 5)  

Continuous dependent variables

These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.  

Categorical or discrete dependent variables

These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.  

Differences between independent and dependent variables  

The following table compares independent vs dependent variables .  

Independent and dependent variable examples  

Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)

  Independent vs dependent variables in research  

Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)  

  Here are some more research questions and their corresponding independent and dependent variables. ( 6)  

  How to identify independent vs dependent variables  

In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)  

• Keep in mind that there are no specific words that will always describe dependent and independent variables.  
• If you’re given a paragraph, convert that into a question and identify specific words describing cause and effect.  
• The word representing the cause is the independent variable and that describing the effect is the dependent variable.  

Let’s try out these steps with an example.  

A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.  

To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.  

Visualizing independent vs dependent variables  

Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.  

Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.  

Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.  

Key takeaways   

Let’s summarize the key takeaways about independent vs dependent variables from this article:  

• A variable is any entity being measured in a study.  
• A dependent variable is often the focus of a research study and is the response or outcome. It depends on or varies with changes in other variables.  
• Independent variables cause changes in dependent variables and don’t depend on other variables.  
• An independent variable can influence a dependent variable, but a dependent variable cannot influence an independent variable.  
• An independent variable is the cause and dependent variable is the effect.  

Frequently asked questions  

• What are the different types of variables used in research?  

The following table lists the different types of variables used in research.( 10)  

 2. Why is it important to differentiate between independent vs dependent variables ?  

  Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.  

 3. How are independent and dependent variables used in non-experimental research?  

  So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.  

  4. Are there any advantages and disadvantages of using independent vs dependent variables ?

  Here are a few advantages and disadvantages of both independent and dependent variables.( 12)

Advantages: 

• Dependent variables are not liable to any form of bias because they cannot be manipulated by researchers or other external factors.  
• Independent variables are easily obtainable and don’t require complex mathematical procedures to be observed, like dependent variables. This is because researchers can easily manipulate these variables or collect the data from respondents.  
• Some independent variables are natural factors and cannot be manipulated. They are also easily obtainable because less time is required for data collection.

Disadvantages: 

• Obtaining dependent variables is a very expensive and effort- and time-intensive process because these variables are obtained from longitudinal research by solving complex equations.  
• Independent variables are prone to researcher and respondent bias because they can be manipulated, and this may affect the study results.  

We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.    

• Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J. 2019 Jan-Feb; 10(1): 82–86. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362742/  
• What Is an independent variable? (with uses and examples). Indeed website. Accessed March 11, 2024. https://www.indeed.com/career-advice/career-development/what-is-independent-variable  
• Independent and dependent variables: Differences & examples. Statistics by Jim website. Accessed March 10, 2024. https://statisticsbyjim.com/regression/independent-dependent-variables/  
• Independent variable. Biology online website. Accessed March 9, 2024. https://www.biologyonline.com/dictionary/independent-variable#:~:text=The%20independent%20variable%20in%20research,how%20many%20or%20how%20often .  
• Dependent variables: Definition and examples. Clubz Tutoring Services website. Accessed March 10, 2024. https://clubztutoring.com/ed-resources/math/dependent-variable-definitions-examples-6-7-2/  
• Research topics with independent and dependent variables. Good research topics website. Accessed March 12, 2024. https://goodresearchtopics.com/research-topics-with-independent-and-dependent-variables/  
• Levels of measurement and using the correct statistical test. Univariate quantitative methods. Accessed March 14, 2024. https://web.pdx.edu/~newsomj/uvclass/ho_levels.pdf  
• Easiest way to identify dependent and independent variables. Afidated website. Accessed March 15, 2024. https://www.afidated.com/2014/07/how-to-identify-dependent-and.html  
• Choosing data visualizations. Math for the people website. Accessed March 14, 2024. https://web.stevenson.edu/mbranson/m4tp/version1/environmental-racism-choosing-data-visualization.html  
• Trivedi C. Types of variables in scientific research. Concepts Hacked website. Accessed March 15, 2024. https://conceptshacked.com/variables-in-scientific-research/  
• Variables in experimental and non-experimental research. Statistics solutions website. Accessed March 14, 2024. https://www.statisticssolutions.com/variables-in-experimental-and-non-experimental-research/#:~:text=The%20independent%20variable%20would%20be,state%20instead%20of%20manipulating%20them .  
• Dependent vs independent variables: 11 key differences. Formplus website. Accessed March 15, 2024. https://www.formpl.us/blog/dependent-independent-variables

Researcher.Life is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Researcher.Life All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

Related Posts

Take Top AI Tools for Researchers for a Spin with the Editage All Access 7-Day Pass!

Thesis Defense: How to Ace this Crucial Step

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).

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.

Related Articles

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Independent Variables (Definition + 43 Examples)

Have you ever wondered how scientists make discoveries and how researchers come to understand the world around us? A crucial tool in their kit is the concept of the independent variable, which helps them delve into the mysteries of science and everyday life.

An independent variable is a condition or factor that researchers manipulate to observe its effect on another variable, known as the dependent variable. In simpler terms, it’s like adjusting the dials and watching what happens! By changing the independent variable, scientists can see if and how it causes changes in what they are measuring or observing, helping them make connections and draw conclusions.

In this article, we’ll explore the fascinating world of independent variables, journey through their history, examine theories, and look at a variety of examples from different fields.

History of the Independent Variable

Once upon a time, in a world thirsty for understanding, people observed the stars, the seas, and everything in between, seeking to unlock the mysteries of the universe.

The story of the independent variable begins with a quest for knowledge, a journey taken by thinkers and tinkerers who wanted to explain the wonders and strangeness of the world.

Origins of the Concept

The seeds of the idea of independent variables were sown by Sir Francis Galton , an English polymath, in the 19th century. Galton wore many hats—he was a psychologist, anthropologist, meteorologist, and a statistician!

It was his diverse interests that led him to explore the relationships between different factors and their effects. Galton was curious—how did one thing lead to another, and what could be learned from these connections?

As Galton delved into the world of statistical theories , the concept of independent variables started taking shape.

He was interested in understanding how characteristics, like height and intelligence, were passed down through generations.

Galton’s work laid the foundation for later thinkers to refine and expand the concept, turning it into an invaluable tool for scientific research.

Evolution over Time

After Galton’s pioneering work, the concept of the independent variable continued to evolve and grow. Scientists and researchers from various fields adopted and adapted it, finding new ways to use it to make sense of the world.

They discovered that by manipulating one factor (the independent variable), they could observe changes in another (the dependent variable), leading to groundbreaking insights and discoveries.

Through the years, the independent variable became a cornerstone in experimental design . Researchers in fields like physics, biology, psychology, and sociology used it to test hypotheses, develop theories, and uncover the laws that govern our universe.

The idea that originated from Galton’s curiosity had bloomed into a universal key, unlocking doors to knowledge across disciplines.

Importance in Scientific Research

Today, the independent variable stands tall as a pillar of scientific research. It helps scientists and researchers ask critical questions, test their ideas, and find answers. Without independent variables, we wouldn’t have many of the advancements and understandings that we take for granted today.

The independent variable plays a starring role in experiments, helping us learn about everything from the smallest particles to the vastness of space. It helps researchers create vaccines, understand social behaviors, explore ecological systems, and even develop new technologies.

In the upcoming sections, we’ll dive deeper into what independent variables are, how they work, and how they’re used in various fields.

Together, we’ll uncover the magic of this scientific concept and see how it continues to shape our understanding of the world around us.

What is an Independent Variable?

Embarking on the captivating journey of scientific exploration requires us to grasp the essential terms and ideas. It's akin to a treasure hunter mastering the use of a map and compass.

In our adventure through the realm of independent variables, we’ll delve deeper into some fundamental concepts and definitions to help us navigate this exciting world.

Variables in Research

In the grand tapestry of research, variables are the gems that researchers seek. They’re elements, characteristics, or behaviors that can shift or vary in different circumstances.

Picture them as the myriad of ingredients in a chef’s kitchen—each variable can be adjusted or modified to create a myriad of dishes, each with a unique flavor!

Understanding variables is essential as they form the core of every scientific experiment and observational study.

Types of Variables

Independent Variable The star of our story, the independent variable, is the one that researchers change or control to study its effects. It’s like a chef experimenting with different spices to see how each one alters the taste of the soup. The independent variable is the catalyst, the initial spark that sets the wheels of research in motion.

Dependent Variable The dependent variable is the outcome we observe and measure . It’s the altered flavor of the soup that results from the chef’s culinary experiments. This variable depends on the changes made to the independent variable, hence the name!

Observing how the dependent variable reacts to changes helps scientists draw conclusions and make discoveries.

Control Variable Control variables are the unsung heroes of scientific research. They’re the constants, the elements that researchers keep the same to ensure the integrity of the experiment.

Imagine if our chef used a different type of broth each time he experimented with spices—the results would be all over the place! Control variables keep the experiment grounded and help researchers be confident in their findings.

Confounding Variables Imagine a hidden rock in a stream, changing the water’s flow in unexpected ways. Confounding variables are similar—they are external factors that can sneak into experiments and influence the outcome , adding twists to our scientific story.

These variables can blur the relationship between the independent and dependent variables, making the results of the study a bit puzzly. Detecting and controlling these hidden elements helps researchers ensure the accuracy of their findings and reach true conclusions.

There are of course other types of variables, and different ways to manipulate them called " schedules of reinforcement ," but we won't get into that too much here.

Role of the Independent Variable

Manipulation When researchers manipulate the independent variable, they are orchestrating a symphony of cause and effect. They’re adjusting the strings, the brass, the percussion, observing how each change influences the melody—the dependent variable.

This manipulation is at the heart of experimental research. It allows scientists to explore relationships, unravel patterns, and unearth the secrets hidden within the fabric of our universe.

Observation With every tweak and adjustment made to the independent variable, researchers are like seasoned detectives, observing the dependent variable for changes, collecting clues, and piecing together the puzzle.

Observing the effects and changes that occur helps them deduce relationships, formulate theories, and expand our understanding of the world. Every observation is a step towards solving the mysteries of nature and human behavior.

Identifying Independent Variables

Characteristics Identifying an independent variable in the vast landscape of research can seem daunting, but fear not! Independent variables have distinctive characteristics that make them stand out.

They’re the elements that are deliberately changed or controlled in an experiment to study their effects on the dependent variable. Recognizing these characteristics is like learning to spot footprints in the sand—it leads us to the heart of the discovery!

In Different Types of Research The world of research is diverse and varied, and the independent variable dons many guises! In the field of medicine, it might manifest as the dosage of a drug administered to patients.

In psychology, it could take the form of different learning methods applied to study memory retention. In each field, identifying the independent variable correctly is the golden key that unlocks the treasure trove of knowledge and insights.

As we forge ahead on our enlightening journey, equipped with a deeper understanding of independent variables and their roles, we’re ready to delve into the intricate theories and diverse examples that underscore their significance.

Independent Variables in Research

Now that we’re acquainted with the basic concepts and have the tools to identify independent variables, let’s dive into the fascinating ocean of theories and frameworks.

These theories are like ancient scrolls, providing guidelines and blueprints that help scientists use independent variables to uncover the secrets of the universe.

Scientific Method

What is it and How Does it Work? The scientific method is like a super-helpful treasure map that scientists use to make discoveries. It has steps we follow: asking a question, researching, guessing what will happen (that's a hypothesis!), experimenting, checking the results, figuring out what they mean, and telling everyone about it.

Our hero, the independent variable, is the compass that helps this adventure go the right way!

How Independent Variables Lead the Way In the scientific method, the independent variable is like the captain of a ship, leading everyone through unknown waters.

Scientists change this variable to see what happens and to learn new things. It’s like having a compass that points us towards uncharted lands full of knowledge!

Experimental Design

The Basics of Building Constructing an experiment is like building a castle, and the independent variable is the cornerstone. It’s carefully chosen and manipulated to see how it affects the dependent variable. Researchers also identify control and confounding variables, ensuring the castle stands strong, and the results are reliable.

Keeping Everything in Check In every experiment, maintaining control is key to finding the treasure. Scientists use control variables to keep the conditions consistent, ensuring that any changes observed are truly due to the independent variable. It’s like ensuring the castle’s foundation is solid, supporting the structure as it reaches for the sky.

Hypothesis Testing

Making Educated Guesses Before they start experimenting, scientists make educated guesses called hypotheses . It’s like predicting which X marks the spot of the treasure! It often includes the independent variable and the expected effect on the dependent variable, guiding researchers as they navigate through the experiment.

Independent Variables in the Spotlight When testing these guesses, the independent variable is the star of the show! Scientists change and watch this variable to see if their guesses were right. It helps them figure out new stuff and learn more about the world around us!

Statistical Analysis

Figuring Out Relationships After the experimenting is done, it’s time for scientists to crack the code! They use statistics to understand how the independent and dependent variables are related and to uncover the hidden stories in the data.

Experimenters have to be careful about how they determine the validity of their findings, which is why they use statistics. Something called "experimenter bias" can get in the way of having true (valid) results, because it's basically when the experimenter influences the outcome based on what they believe to be true (or what they want to be true!).

How Important are the Discoveries? Through statistical analysis, scientists determine the significance of their findings. It’s like discovering if the treasure found is made of gold or just shiny rocks. The analysis helps researchers know if the independent variable truly had an effect, contributing to the rich tapestry of scientific knowledge.

As we uncover more about how theories and frameworks use independent variables, we start to see how awesome they are in helping us learn more about the world. But we’re not done yet!

Up next, we’ll look at tons of examples to see how independent variables work their magic in different areas.

Examples of Independent Variables

Independent variables take on many forms, showcasing their versatility in a range of experiments and studies. Let’s uncover how they act as the protagonists in numerous investigations and learning quests!

Science Experiments

1) plant growth.

Consider an experiment aiming to observe the effect of varying water amounts on plant height. In this scenario, the amount of water given to the plants is the independent variable!

2) Freezing Water

Suppose we are curious about the time it takes for water to freeze at different temperatures. The temperature of the freezer becomes the independent variable as we adjust it to observe the results!

3) Light and Shadow

Have you ever observed how shadows change? In an experiment, adjusting the light angle to observe its effect on an object’s shadow makes the angle of light the independent variable!

4) Medicine Dosage

In medical studies, determining how varying medicine dosages influence a patient’s recovery is essential. Here, the dosage of the medicine administered is the independent variable!

5) Exercise and Health

Researchers might examine the impact of different exercise forms on individuals’ health. The various exercise forms constitute the independent variable in this study!

6) Sleep and Wellness

Have you pondered how the sleep duration affects your well-being the following day? In such research, the hours of sleep serve as the independent variable!

7) Learning Methods

Psychologists might investigate how diverse study methods influence test outcomes. Here, the different study methods adopted by students are the independent variable!

8) Mood and Music

Have you experienced varied emotions with different music genres? The genre of music played becomes the independent variable when researching its influence on emotions!

9) Color and Feelings

Suppose researchers are exploring how room colors affect individuals’ emotions. In this case, the room colors act as the independent variable!

Environment

10) rainfall and plant life.

Environmental scientists may study the influence of varying rainfall levels on vegetation. In this instance, the amount of rainfall is the independent variable!

11) Temperature and Animal Behavior

Examining how temperature variations affect animal behavior is fascinating. Here, the varying temperatures serve as the independent variable!

12) Pollution and Air Quality

Investigating the effects of different pollution levels on air quality is crucial. In such studies, the pollution level is the independent variable!

13) Internet Speed and Productivity

Researchers might explore how varying internet speeds impact work productivity. In this exploration, the internet speed is the independent variable!

14) Device Type and User Experience

Examining how different devices affect user experience is interesting. Here, the type of device used is the independent variable!

15) Software Version and Performance

Suppose a study aims to determine how different software versions influence system performance. The software version becomes the independent variable!

16) Teaching Style and Student Engagement

Educators might investigate the effect of varied teaching styles on student engagement. In such a study, the teaching style is the independent variable!

17) Class Size and Learning Outcome

Researchers could explore how different class sizes influence students’ learning. Here, the class size is the independent variable!

18) Homework Frequency and Academic Achievement

Examining the relationship between the frequency of homework assignments and academic success is essential. The frequency of homework becomes the independent variable!

19) Telescope Type and Celestial Observation

Astronomers might study how different telescopes affect celestial observation. In this scenario, the telescope type is the independent variable!

20) Light Pollution and Star Visibility

Investigating the influence of varying light pollution levels on star visibility is intriguing. Here, the level of light pollution is the independent variable!

21) Observation Time and Astronomical Detail

Suppose a study explores how observation duration affects the detail captured in astronomical images. The duration of observation serves as the independent variable!

22) Community Size and Social Interaction

Sociologists may examine how the size of a community influences social interactions. In this research, the community size is the independent variable!

23) Cultural Exposure and Social Tolerance

Investigating the effect of diverse cultural exposure on social tolerance is vital. Here, the level of cultural exposure is the independent variable!

24) Economic Status and Educational Attainment

Researchers could explore how different economic statuses impact educational achievements. In such studies, economic status is the independent variable!

25) Training Intensity and Athletic Performance

Sports scientists might study how varying training intensities affect athletes’ performance. In this case, the training intensity is the independent variable!

26) Equipment Type and Player Safety

Examining the relationship between different sports equipment and player safety is crucial. Here, the type of equipment used is the independent variable!

27) Team Size and Game Strategy

Suppose researchers are investigating how the size of a sports team influences game strategy. The team size becomes the independent variable!

28) Diet Type and Health Outcome

Nutritionists may explore the impact of various diets on individuals’ health. In this exploration, the type of diet followed is the independent variable!

29) Caloric Intake and Weight Change

Investigating how different caloric intakes influence weight change is essential. In such a study, the caloric intake is the independent variable!

30) Food Variety and Nutrient Absorption

Researchers could examine how consuming a variety of foods affects nutrient absorption. Here, the variety of foods consumed is the independent variable!

Real-World Examples of Independent Variables

Isn't it fantastic how independent variables play such an essential part in so many studies? But the excitement doesn't stop there!

Now, let’s explore how findings from these studies, led by independent variables, make a big splash in the real world and improve our daily lives!

Healthcare Advancements

31) treatment optimization.

By studying different medicine dosages and treatment methods as independent variables, doctors can figure out the best ways to help patients recover quicker and feel better. This leads to more effective medicines and treatment plans!

32) Lifestyle Recommendations

Researching the effects of sleep, exercise, and diet helps health experts give us advice on living healthier lives. By changing these independent variables, scientists uncover the secrets to feeling good and staying well!

Technological Innovations

33) speeding up the internet.

When scientists explore how different internet speeds affect our online activities, they’re able to develop technologies to make the internet faster and more reliable. This means smoother video calls and quicker downloads!

34) Improving User Experience

By examining how we interact with various devices and software, researchers can design technology that’s easier and more enjoyable to use. This leads to cooler gadgets and more user-friendly apps!

Educational Strategies

35) enhancing learning.

Investigating different teaching styles, class sizes, and study methods helps educators discover what makes learning fun and effective. This research shapes classrooms, teaching methods, and even homework!

36) Tailoring Student Support

By studying how students with diverse needs respond to different support strategies, educators can create personalized learning experiences. This means every student gets the help they need to succeed!

Environmental Protection

37) conserving nature.

Researching how rainfall, temperature, and pollution affect the environment helps scientists suggest ways to protect our planet. By studying these independent variables, we learn how to keep nature healthy and thriving!

38) Combating Climate Change

Scientists studying the effects of pollution and human activities on climate change are leading the way in finding solutions. By exploring these independent variables, we can develop strategies to combat climate change and protect the Earth!

Social Development

39) building stronger communities.

Sociologists studying community size, cultural exposure, and economic status help us understand what makes communities happy and united. This knowledge guides the development of policies and programs for stronger societies!

40) Promoting Equality and Tolerance

By exploring how exposure to diverse cultures affects social tolerance, researchers contribute to fostering more inclusive and harmonious societies. This helps build a world where everyone is respected and valued!

Enhancing Sports Performance

41) optimizing athlete training.

Sports scientists studying training intensity, equipment type, and team size help athletes reach their full potential. This research leads to better training programs, safer equipment, and more exciting games!

42) Innovating Sports Strategies

By investigating how different game strategies are influenced by various team compositions, researchers contribute to the evolution of sports. This means more thrilling competitions and matches for us to enjoy!

Nutritional Well-Being

43) guiding healthy eating.

Nutritionists researching diet types, caloric intake, and food variety help us understand what foods are best for our bodies. This knowledge shapes dietary guidelines and helps us make tasty, yet nutritious, meal choices!

44) Promoting Nutritional Awareness

By studying the effects of different nutrients and diets, researchers educate us on maintaining a balanced diet. This fosters a greater awareness of nutritional well-being and encourages healthier eating habits!

As we journey through these real-world applications, we witness the incredible impact of studies featuring independent variables. The exploration doesn’t end here, though!

Let’s continue our adventure and see how we can identify independent variables in our own observations and inquiries! Keep your curiosity alive, and let’s delve deeper into the exciting realm of independent variables!

Identifying Independent Variables in Everyday Scenarios

So, we’ve seen how independent variables star in many studies, but how about spotting them in our everyday life?

Recognizing independent variables can be like a treasure hunt – you never know where you might find one! Let’s uncover some tips and tricks to identify these hidden gems in various situations.

1) Asking Questions

One of the best ways to spot an independent variable is by asking questions! If you’re curious about something, ask yourself, “What am I changing or manipulating in this situation?” The thing you’re changing is likely the independent variable!

For example, if you’re wondering whether the amount of sunlight affects how quickly your laundry dries, the sunlight amount is your independent variable!

2) Making Observations

Keep your eyes peeled and observe the world around you! By watching how changes in one thing (like the amount of rain) affect something else (like the height of grass), you can identify the independent variable.

In this case, the amount of rain is the independent variable because it’s what’s changing!

3) Conducting Experiments

Get hands-on and conduct your own experiments! By changing one thing and observing the results, you’re identifying the independent variable.

If you’re growing plants and decide to water each one differently to see the effects, the amount of water is your independent variable!

4) Everyday Scenarios

In everyday scenarios, independent variables are all around!

When you adjust the temperature of your oven to bake cookies, the oven temperature is the independent variable.

Or if you’re deciding how much time to spend studying for a test, the study time is your independent variable!

5) Being Curious

Keep being curious and asking “What if?” questions! By exploring different possibilities and wondering how changing one thing could affect another, you’re on your way to identifying independent variables.

If you’re curious about how the color of a room affects your mood, the room color is the independent variable!

6) Reviewing Past Studies

Don’t forget about the treasure trove of past studies and experiments! By reviewing what scientists and researchers have done before, you can learn how they identified independent variables in their work.

This can give you ideas and help you recognize independent variables in your own explorations!

Exercises for Identifying Independent Variables

Ready for some practice? Let’s put on our thinking caps and try to identify the independent variables in a few scenarios.

Remember, the independent variable is what’s being changed or manipulated to observe the effect on something else! (You can see the answers below)

Scenario One: Cooking Time

You’re cooking pasta for dinner and want to find out how the cooking time affects its texture. What is the independent variable?

Scenario Two: Exercise Routine

You decide to try different exercise routines each week to see which one makes you feel the most energetic. What is the independent variable?

Scenario Three: Plant Fertilizer

You’re growing tomatoes in your garden and decide to use different types of fertilizer to see which one helps them grow the best. What is the independent variable?

Scenario Four: Study Environment

You’re preparing for an important test and try studying in different environments (quiet room, coffee shop, library) to see where you concentrate best. What is the independent variable?

Scenario Five: Sleep Duration

You’re curious to see how the number of hours you sleep each night affects your mood the next day. What is the independent variable?

By practicing identifying independent variables in different scenarios, you’re becoming a true independent variable detective. Keep practicing, stay curious, and you’ll soon be spotting independent variables everywhere you go.

Independent Variable: The cooking time is the independent variable. You are changing the cooking time to observe its effect on the texture of the pasta.

Independent Variable: The type of exercise routine is the independent variable. You are trying out different exercise routines each week to see which one makes you feel the most energetic.

Independent Variable: The type of fertilizer is the independent variable. You are using different types of fertilizer to observe their effects on the growth of the tomatoes.

Independent Variable: The study environment is the independent variable. You are studying in different environments to see where you concentrate best.

Independent Variable: The number of hours you sleep is the independent variable. You are changing your sleep duration to see how it affects your mood the next day.

Whew, what a journey we’ve had exploring the world of independent variables! From understanding their definition and role to diving into a myriad of examples and real-world impacts, we’ve uncovered the treasures hidden in the realm of independent variables.

The beauty of independent variables lies in their ability to unlock new knowledge and insights, guiding us to discoveries that improve our lives and the world around us.

By identifying and studying these variables, we embark on exciting learning adventures, solving mysteries and answering questions about the universe we live in.

Remember, the joy of discovery doesn’t end here. The world is brimming with questions waiting to be answered and mysteries waiting to be solved.

Keep your curiosity alive, continue exploring, and who knows what incredible discoveries lie ahead.

Related posts:

• Confounding Variable in Psychology (Examples + Definition)
• 19+ Experimental Design Examples (Methods + Types)
• Variable Interval Reinforcement Schedule (Examples)
• Variable Ratio Reinforcement Schedule (Examples)
• State Dependent Memory + Learning (Definition and Examples)

Reference this article:

About The Author

Free Personality Test

Free Memory Test

Free IQ Test

PracticalPie.com is a participant in the Amazon Associates Program. As an Amazon Associate we earn from qualifying purchases.

Follow Us On:

Youtube Facebook Instagram X/Twitter

Psychology Resources

Developmental

Personality

Relationships

Psychologists

Serial Killers

Psychology Tests

Personality Quiz

Memory Test

Depression test

Type A/B Personality Test

© PracticalPsychology. All rights reserved

Privacy Policy | Terms of Use

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Independent and Dependent Variables: Differences & Examples

By Jim Frost 15 Comments

In this post, learn the definitions of independent and dependent variables, how to identify each type, how they differ between different types of studies, and see examples of them in use.

What is an Independent Variable?

Independent variables (IVs) are the ones that you include in the model to explain or predict changes in the dependent variable. The name helps you understand their role in statistical analysis. These variables are independent . In this context, independent indicates that they stand alone and other variables in the model do not influence them. The researchers are not seeking to understand what causes the independent variables to change.

Independent variables are also known as predictors, factors , treatment variables, explanatory variables, input variables, x-variables, and right-hand variables—because they appear on the right side of the equals sign in a regression equation. In notation, statisticians commonly denote them using Xs. On graphs, analysts place independent variables on the horizontal, or X, axis.

In machine learning, independent variables are known as features.

For example, in a plant growth study, the independent variables might be soil moisture (continuous) and type of fertilizer (categorical).

Statistical models will estimate effect sizes for the independent variables.

Relate post : Effect Sizes in Statistics

Including independent variables in studies

The nature of independent variables changes based on the type of experiment or study:

Controlled experiments : Researchers systematically control and set the values of the independent variables. In randomized experiments, relationships between independent and dependent variables tend to be causal. The independent variables cause changes in the dependent variable.

Observational studies : Researchers do not set the values of the explanatory variables but instead observe them in their natural environment. When the independent and dependent variables are correlated, those relationships might not be causal.

When you include one independent variable in a regression model, you are performing simple regression. For more than one independent variable, it is multiple regression. Despite the different names, it’s really the same analysis with the same interpretations and assumptions.

Determining which IVs to include in a statistical model is known as model specification. That process involves in-depth research and many subject-area, theoretical, and statistical considerations. At its most basic level, you’ll want to include the predictors you are specifically assessing in your study and confounding variables that will bias your results if you don’t add them—particularly for observational studies.

For more information about choosing independent variables, read my post about Specifying the Correct Regression Model .

Related posts : Randomized Experiments , Observational Studies , Covariates , and Confounding Variables

What is a Dependent Variable?

The dependent variable (DV) is what you want to use the model to explain or predict. The values of this variable depend on other variables. It is the outcome that you’re studying. It’s also known as the response variable, outcome variable, and left-hand variable. Statisticians commonly denote them using a Y. Traditionally, graphs place dependent variables on the vertical, or Y, axis.

For example, in the plant growth study example, a measure of plant growth is the dependent variable. That is the outcome of the experiment, and we want to determine what affects it.

How to Identify Independent and Dependent Variables

If you’re reading a study’s write-up, how do you distinguish independent variables from dependent variables? Here are some tips!

Identifying IVs

How statisticians discuss independent variables changes depending on the field of study and type of experiment.

In randomized experiments, look for the following descriptions to identify the independent variables:

• Independent variables cause changes in another variable.
• The researchers control the values of the independent variables. They are controlled or manipulated variables.
• Experiments often refer to them as factors or experimental factors. In areas such as medicine, they might be risk factors.
• Treatment and control groups are always independent variables. In this case, the independent variable is a categorical grouping variable that defines the experimental groups to which participants belong. Each group is a level of that variable.

In observational studies, independent variables are a bit different. While the researchers likely want to establish causation, that’s harder to do with this type of study, so they often won’t use the word “cause.” They also don’t set the values of the predictors. Some independent variables are the experiment’s focus, while others help keep the experimental results valid.

Here’s how to recognize independent variables in observational studies:

• IVs explain the variability, predict, or correlate with changes in the dependent variable.
• Researchers in observational studies must include confounding variables (i.e., confounders) to keep the statistical results valid even if they are not the primary interest of the study. For example, these might include the participants’ socio-economic status or other background information that the researchers aren’t focused on but can explain some of the dependent variable’s variability.
• The results are adjusted or controlled for by a variable.

Regardless of the study type, if you see an estimated effect size, it is an independent variable.

Identifying DVs

Dependent variables are the outcome. The IVs explain the variability or causes changes in the DV. Focus on the “depends” aspect. The value of the dependent variable depends on the IVs. If Y depends on X, then Y is the dependent variable. This aspect applies to both randomized experiments and observational studies.

In an observational study about the effects of smoking, the researchers observe the subjects’ smoking status (smoker/non-smoker) and their lung cancer rates. It’s an observational study because they cannot randomly assign subjects to either the smoking or non-smoking group. In this study, the researchers want to know whether lung cancer rates depend on smoking status. Therefore, the lung cancer rate is the dependent variable.

In a randomized COVID-19 vaccine experiment , the researchers randomly assign subjects to the treatment or control group. They want to determine whether COVID-19 infection rates depend on vaccination status. Hence, the infection rate is the DV.

Note that a variable can be an independent variable in one study but a dependent variable in another. It depends on the context.

For example, one study might assess how the amount of exercise (IV) affects health (DV). However, another study might study the factors (IVs) that influence how much someone exercises (DV). The amount of exercise is an independent variable in one study but a dependent variable in the other!

How Analyses Use IVs and DVs

Regression analysis and ANOVA mathematically describe the relationships between each independent variable and the dependent variable. Typically, you want to determine how changes in one or more predictors associate with changes in the dependent variable. These analyses estimate an effect size for each independent variable.

Suppose researchers study the relationship between wattage, several types of filaments, and the output from a light bulb. In this study, light output is the dependent variable because it depends on the other two variables. Wattage (continuous) and filament type (categorical) are the independent variables.

After performing the regression analysis, the researchers will understand the nature of the relationship between these variables. How much does the light output increase on average for each additional watt? Does the mean light output differ by filament types? They will also learn whether these effects are statistically significant.

Related post : When to Use Regression Analysis

Graphing Independent and Dependent Variables

As I mentioned earlier, graphs traditionally display the independent variables on the horizontal X-axis and the dependent variable on the vertical Y-axis. The type of graph depends on the nature of the variables. Here are a couple of examples.

Suppose you experiment to determine whether various teaching methods affect learning outcomes. Teaching method is a categorical predictor that defines the experimental groups. To display this type of data, you can use a boxplot, as shown below.

The groups are along the horizontal axis, while the dependent variable, learning outcomes, is on the vertical. From the graph, method 4 has the best results. A one-way ANOVA will tell you whether these results are statistically significant. Learn more about interpreting boxplots .

Now, imagine that you are studying people’s height and weight. Specifically, do height increases cause weight to increase? Consequently, height is the independent variable on the horizontal axis, and weight is the dependent variable on the vertical axis. You can use a scatterplot to display this type of data.

It appears that as height increases, weight tends to increase. Regression analysis will tell you if these results are statistically significant. Learn more about interpreting scatterplots .

Share this:

Reader Interactions

April 2, 2024 at 2:05 am

Hi again Jim

Thanks so much for taking an interest in New Zealand’s Equity Index.

Rather than me trying to explain what our Ministry of Education has done, here is a link to a fairly short paper. Scroll down to page 4 of this (if you have the inclination) – https://fyi.org.nz/request/21253/response/80708/attach/4/1301098%20Response%20and%20Appendix.pdf

The Equity Index is used to allocate only 4% of total school funding. The most advantaged 5% of schools get no “equity funding” and the other 95% get a share of the equity funding pool based on their index score. We are talking a maximum of around $1,000NZD per child per year for the most disadvantaged schools. The average amount is around $200-$300 per child per year.

My concern is that I thought the dependent variable is the thing you want to explain or predict using one or more independent variables. Choosing the form of dependent variable that gets a good fit seems to be answering the question “what can we predict well?” rather than “how do we best predict the factor of interest?” The factor is educational achievement and I think this should have been decided upon using theory rather than experimentation with the data.

As it turns out, the Ministry has chosen a measure of educational achievement that puts a heavy weight on achieving an “excellence” rating on a qualification and a much lower weight on simply gaining a qualification. My reading is that they have taken what our universities do when looking at which students to admit.

It doesn’t seem likely to me that a heavy weighting on excellent achievement is appropriate for targeting extra funding to schools with a lot of under-achieving students.

However, my stats knowledge isn’t extensive and it’s definitely rusty, so your thoughts are most helpful.

Regards Kathy Spencer

April 1, 2024 at 4:08 pm

Hi Jim, Great website, thank you.

I have been looking at New Zealand’s Equity Index which is used to allocate a small amount of extra funding to schools attended by children from disadvantaged backgrounds. The Index uses 37 socioeconomic measures relating to a child’s and their parents’ backgrounds that are found to be associated with educational achievement.

I was a bit surprised to read how they had decided on the dependent variable to be used as the measure of educational achievement, or dependent variable. Part of the process was as follows- “Each measure was tested to see the degree to which it could be predicted by the socioeconomic factors selected for the Equity Index.”

Any comment?

Many thanks Kathy Spencer

April 1, 2024 at 9:20 pm

That’s a very complex study and I don’t know much about it. So, that limits what I can say about it. But I’ll give you a few thoughts that come to mind.

This method is common in educational and social research, particularly when the goal is to understand or mitigate the impact of socioeconomic disparities on educational outcomes.

There are the usual concerns about not confusing correlation with causation. However, because this program seems to quantify barriers and then provide extra funding based on the index, I don’t think that’s a problem. They’re not attempting to adjust the socioeconomic measures so no worries about whether they’re directly causal or not.

I might have a small concern about cherry picking the model that happens to maximize the R-squared. Chasing the R-squared rather than having theory drive model selecting is often problematic. Chasing the best fit increases the likelihood that the model fits this specific dataset best by random chance rather than being truly the best. If so, it won’t perform as well outside the dataset used to fit the model. Hopefully, they validated the predicted ability of the model using other data.

However, I’m not sure if the extra funding is determined by the model? I don’t know if the index value is calculated separately outside the candidate models and then fed into the various models. Or does the choice of model affect how the index value is calculated? If it’s the former, then the funding doesn’t depend on a potentially cherry picked model. If the latter, it does.

So, I’m not really clear on the purpose of the model. I’m guessing they just want to validate their Equity Index. And maximizing the R-squared doesn’t really say it’s the best Index but it does at least show that it likely has some merit. I’d be curious how the took the 37 measures and combined them to one index. So, I have more questions than answers. I don’t mean that in a critical sense. Just that I know almost nothing about this program.

I’m curious, what was the outcome they picked? How high was the R-squared? And what were your concerns?

February 6, 2024 at 6:57 pm

Excellent explanation, thank you.

February 5, 2024 at 5:04 pm

Thank you for this insightful blog. Is it valid to use a dependent variable delivered from the mean of independent variables in multiple regression if you want to evaluate the influence of each unique independent variable on the dependent variables?

February 5, 2024 at 11:11 pm

It’s difficult to answer your question because I’m not sure what you mean that the DV is “delivered from the mean of IVs.” If you mean that multiple IVs explain changes in the DV’s mean, yes, that’s the standard use for multiple regression.

If you mean something else, please explain in further detail. Thanks!

February 6, 2024 at 6:32 am

What I meant is; the DV values used as parameters for multiple regression is basically calculated as the average of the IVs. For instance:

From 3 IVs (X1, X2, X3), Y is delivered as :

Y = (Sum of all IVs) / (3)

Then the resulting Y is used as the DV along with the initial IVs to compute the multiple regression.

February 6, 2024 at 2:17 pm

There are a couple of reasons why you shouldn’t do that.

For starters, Y-hat (the predicted value of the regression equation) is the mean of the DV given specific values of the IV. However, that mean is calculated by using the regression coefficients and constant in the regression equation. You don’t calculate the DV mean as the sum of the IVs divided by the number of IVs. Perhaps given a very specific subject-area context, using this approach might seem to make sense but there are other problems.

A critical problem is that the Y is now calculated using the IVs. Instead, the DVs should be measured outcomes and not calculated from IVs. This violates regression assumptions and produces questionable results.

Additionally, it complicates the interpretation. Because the DV is calculated from the IV, you know the regression analysis will find a relationship between them. But you have no idea if that relationship exists in the real world. This complication occurs because your results are based on forcing the DV to equal a function of the IVs and do not reflect real-world outcomes.

In short, DVs should be real-world outcomes that you measure! And be sure to keep your IVs and DV independent. Let the regression analysis estimate the regression equation from your data that contains measured DVs. Don’t use a function to force the DV to equal some function of the IVs because that’s the opposite direction of how regression works!

I hope that helps!

September 6, 2022 at 7:43 pm

Thank you for sharing.

March 3, 2022 at 1:59 am

Excellent explanation.

February 13, 2022 at 12:31 pm

Thanks a lot for creating this excellent blog. This is my go-to resource for Statistics.

I had been pondering over a question for sometime, it would be great if you could shed some light on this.

In linear and non-linear regression, should the distribution of independent and dependent variables be unskewed? When is there a need to transform the data (say, Box-Cox transformation), and do we transform the independent variables as well?

October 28, 2021 at 12:55 pm

If I use a independent variable (X) and it displays a low p-value <.05, why is it if I introduce another independent variable to regression the coefficient and p-value of Y that I used in first regression changes to look insignificant? The second variable that I introduced has a low p-value in regression.

October 29, 2021 at 11:22 pm

Keep in mind that the significance of each IV is calculated after accounting for the variance of all the other variables in the model, assuming you’re using the standard adjusted sums of squares rather than sequential sums of squares. The sums of squares (SS) is a measure of how much dependent variable variability that each IV accounts for. In the illustration below, I’ll assume you’re using the standard of adjusted SS.

So, let’s say that originally you have X1 in the model along with some other IVs. Your model estimates the significance of X1 after assessing the variability that the other IVs account for and finds that X1 is significant. Now, you add X2 to the model in addition to X1 and the other IVs. Now, when assessing X1, the model accounts for the variability of the IVs including the newly added X2. And apparently X2 explains a good portion of the variability. X1 is no longer able to account for that variability, which causes it to not be statistically significant.

In other words, X2 explains some of the variability that X1 previously explained. Because X1 no longer explains it, it is no longer significant.

Additionally, the significance of IVs is more likely to change when you add or remove IVs that are correlated. Correlated IVs is known as multicollinearity. Multicollinearity can be a problem when you have too much. Given the change in significance, I’d check your model for multicollinearity just to be safe! Click the link to read a post that wrote about that!

September 6, 2021 at 8:35 am

nice explanation

August 25, 2021 at 3:09 am

it is excellent explanation

Comments and Questions Cancel reply

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Independent and Dependent Variables
• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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.

• << Previous: Design Flaws to Avoid
• Next: Glossary of Research Terms >>
• Last Updated: Jun 18, 2024 10:45 AM
• URL: https://libguides.usc.edu/writingguide

Examples of Independent and Dependent Variables

What Are Independent and Dependent Variables?

• Chemical Laws
• Periodic Table
• Projects & Experiments
• Scientific Method
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

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.

In a scientific experiment, you'll ultimately be changing or controlling the independent variable and measuring the effect on the dependent variable. This distinction is critical in evaluating and proving hypotheses.

Below you'll find more about these two types of variables, along with examples of each in sample science experiments, and an explanation of how to graph them to help visualize your data.

What Is an Independent Variable?

An independent variable is the condition that you change in an experiment. In other words, 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.

• What Is a 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 the amount of time a student sleeps affects test scores, the independent variable is the amount 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 towels. 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 or 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 or 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 the chemical would be the independent variable, and the rat's health would be the dependent variable.

How Do You Tell Independent and Dependent Variables 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 independent and dependent variables. 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 .

Key Takeaways

• In scientific experiments, the independent variable is manipulated while the dependent variable is measured.
• The independent variable, controlled by the experimenter, influences the dependent variable, which responds to changes. This dynamic forms the basis of cause-and-effect relationships.
• Graphing independent and dependent variables follows a standard method in which the independent variable is plotted on the x-axis and the dependent variable on the y-axis.
• Difference Between Independent and Dependent Variables
• Null Hypothesis Examples
• Dependent Variable Definition and Examples
• Independent Variable Definition and Examples
• Scientific Variable
• Popular Math Terms and Definitions
• What Is a Variable in Science?
• DRY MIX Experiment Variables Acronym
• What Is an Experiment? Definition and Design
• Six Steps of the Scientific Method
• The Significance of Negative Slope
• The Differences Between Explanatory and Response Variables
• What Is a Hypothesis? (Science)
• What Are the Elements of a Good Hypothesis?
• Scientific Method Vocabulary Terms

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

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.

Adam Berry / Getty Images

• 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."

Dependent vs. Independent Variables in Research

Introduction

Independent and dependent variables in research, can qualitative data have independent and dependent variables.

Experiments rely on capturing the relationship between independent and dependent variables to understand causal patterns. Researchers can observe what happens when they change a condition in their experiment or if there is any effect at all.

It's important to understand the difference between the independent variable and dependent variable. We'll look at the notion of independent and dependent variables in this article. If you are conducting experimental research, defining the variables in your study is essential for realizing rigorous research .

In experimental research, a variable refers to the phenomenon, person, or thing that is being measured and observed by the researcher. A researcher conducts a study to see how one variable affects another and make assertions about the relationship between different variables.

A typical research question in an experimental study addresses a hypothesized relationship between the independent variable manipulated by the researcher and the dependent variable that is the outcome of interest presumably influenced by the researcher's manipulation.

Take a simple experiment on plants as an example. Suppose you have a control group of plants on one side of a garden and an experimental group of plants on the other side. All things such as sunlight, water, and fertilizer being equal, both plants should be expected to grow at the same rate.

Now imagine that the plants in the experimental group are given a new plant fertilizer under the assumption that they will grow faster. Then you will need to measure the difference in growth between the two groups in your study.

In this case, the independent variable is the type of fertilizer used on your plants while the dependent variable is the rate of growth among your plants. If there is a significant difference in growth between the two groups, then your study provides support to suggest that the fertilizer causes higher rates of plant growth.

What is the key difference between independent and dependent variables?

The independent variable is the element in your study that you intentionally change, which is why it can also be referred to as the manipulated variable.

You manipulate this variable to see how it might affect the other variables you observe, all other factors being equal. This means that you can observe the cause and effect relationships between one independent variable and one or multiple dependent variables.

Independent variables are directly manipulated by the researcher, while dependent variables are not. They are "dependent" because they are affected by the independent variable in the experiment. Researchers can thus study how manipulating the independent variable leads to changes in the main outcome of interest being measured as the dependent variable.

Note that while you can have multiple dependent variables, it is challenging to establish research rigor for multiple independent variables. If you are making so many changes in an experiment, how do you know which change is responsible for the outcome produced by the study? Studying more than one independent variable would require running an experiment for each independent variable to isolate its effects on the dependent variable.

This being said, it is certainly possible to employ a study design that involves multiple independent and dependent variables, as is the case with what is called a factorial experiment. For example, a psychological study examining the effects of sleep and stress levels on work productivity and social interaction would have two independent variables and two dependent variables, respectively.

Such a study would be complex and require careful planning to establish the necessary research rigor , however. If possible, consider narrowing your research to the examination of one independent variable to make it more manageable and easier to understand.

Independent variable examples

Let's consider an experiment in the social studies. Suppose you want to determine the effectiveness of a new textbook compared to current textbooks in a particular school.

The new textbook is supposed to be better, but how can you prove it? Besides all the selling points that the textbook publisher makes, how do you know if the new textbook is any good? A rigorous study examining the effects of the textbook on classroom outcomes is in order.

The textbook given to students makes up the independent variable in your experimental study. The shift from the existing textbooks to the new one represents the manipulation of the independent variable in this study.

Dependent variable examples

In any experiment, the dependent variable is observed to measure how it is affected by changes to the independent variable. Outcomes such as test scores and other performance metrics can make up the data for the dependent variable.

Now that we are changing the textbook in the experiment above, we should examine if there are any effects.

To do this, we will need two classrooms of students. As best as possible, the two sets of students should be of similar proficiency (or at least of similar backgrounds) and placed within similar conditions for teaching and learning (e.g., physical space, lesson planning).

The control group in our study will be one set of students using the existing textbook. By examining their performance, we can establish a baseline. The performance of the experimental group, which is the set of students using the new textbook, can then be compared with the baseline performance.

As a result, the change in the test scores make up the data for our dependent variable. We cannot directly affect how well students perform on the test, but we can conclude from our experiment whether the use of the new textbook might impact students' performance.

Turn data into valuable insights with ATLAS.ti

Rely on our powerful data analysis interface for your research, starting with a free trial.

How do you know if a variable is independent or dependent?

We can typically think of an independent variable as something a researcher can directly change. In the above example, we can change the textbook used by the teacher in class. If we're talking about plants, we can change the fertilizer.

Conversely, the dependent variable is something that we do not directly influence or manipulate. Strictly speaking, we cannot directly manipulate a student's performance on a test or the rate of growth of a plant, not without other factors such as new teaching methods or new fertilizer, respectively.

Understanding the distinction between a dependent variable and an independent variable is key to experimental research. Ultimately, the distinction can be reduced to which element in a study has been directly influenced by the researcher.

Other variables

Given the potential complexities encountered in research, there is essential terminology for other variables in any experimental study. You might employ this terminology or encounter them while reading other research.

A control variable is any factor that the researcher tries to keep constant as the independent variable changes. In the plant experiment described earlier in this article, the sunlight and water are each a controlled variable while the type of fertilizer used is the manipulated variable across control and experimental groups.

To ensure research rigor, the researcher needs to keep these control variables constant to dispel any concerns that differences in growth rate were being driven by sunlight or water, as opposed to the fertilizer being used.

Extraneous variables refer to any unwanted influence on the dependent variable that may confound the analysis of the study. For example, if bugs or animals ate the plants in your fertilizer study, this was greatly impact the rates of plant growth. This is why it would be important to control the environment and protect it from such threats.

Finally, independent variables can go by different names such as subject variables or predictor variables. Dependent variables can also be referred to as the responding variable or outcome variable. Whatever the language, they all serve the same role of influencing the dependent variable in an experiment.

The use of the word " variables " is typically associated with quantitative and confirmatory research. Naturalistic qualitative research typically does not employ experimental designs or establish causality. Qualitative research often draws on observations , interviews , focus groups , and other forms of data collection that are allow researchers to study the naturally occurring "messiness" of the social world, rather than controlling all variables to isolate a cause-and-effect relationship.

In limited circumstances, the idea of experimental variables can apply to participant observations in ethnography , where the researcher should be mindful of their influence on the environment they are observing.

However, the experimental paradigm is best left to quantitative studies and confirmatory research questions. Qualitative researchers in the social sciences are oftentimes more interested in observing and describing socially-constructed phenomena rather than testing hypotheses .

Nonetheless, the notion of independent and dependent variables does hold important lessons for qualitative researchers. Even if they don't employ variables in their study design, qualitative researchers often observe how one thing affects another. A theoretical or conceptual framework can then suggest potential cause-and-effect relationships in their study.

With ATLAS.ti, insightful data analysis is at your fingertips

Download a free trial of ATLAS.ti to see how you can make the most of your data.

Advertisement

The Independent Variable vs. Dependent Variable in Research

• Share Content on Facebook
• Share Content on LinkedIn
• Share Content on Flipboard
• Share Content on Reddit
• Share Content via Email

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

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

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

What Is an Independent Variable?

What is a dependent variable, research study example, predictor variables vs. outcome variables, other variables, the relationship between independent and dependent variables.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Correlation vs. Causation

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

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

Control and Intervention

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

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

Context and Analysis

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

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

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

Please copy/paste the following text to properly cite this HowStuffWorks.com article:

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Independent vs Dependent Variables | Definition & Examples

Independent vs Dependent Variables | Definition & Examples

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

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

• The independent variable is the cause. Its value is independent of other variables in your study.
• The dependent variable is the effect. Its value depends on changes in the independent variable.

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

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs dependent variables, independent and dependent variables in research, visualising independent and dependent variables, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

Prevent plagiarism, run a free check.

There are two main types of independent variables.

• Experimental independent variables can be directly manipulated by researchers.
• Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

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

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

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

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

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

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment.

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

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

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics , dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

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

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design.

Here are some tips for identifying each variable type.

Recognising independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

• Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
• Does this variable come before the other variable in time?
• Is the researcher trying to understand whether or how this variable affects another variable?

Recognising dependent variables

Check whether you’re dealing with a dependent variable:

• Is this variable measured as an outcome of the study?
• Is this variable dependent on another variable in the study?
• Does this variable get measured only after other variables are altered?

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

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

For experimental data, you analyse your results by generating descriptive statistics and visualising your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

• Your variable types
• Level of measurement
• Number of independent variable levels

You’ll often use t tests or ANOVAs to analyse your data and answer your research questions.

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

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

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

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

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

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

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

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

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

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 17). Independent vs Dependent Variables | Definition & Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/independent-vs-dependent-variables/

Is this article helpful?

Pritha Bhandari

Other students also liked, a quick guide to experimental design | 5 steps & examples, quasi-experimental design | definition, types & examples, types of variables in research | definitions & examples.

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

What is an independent variable?

Last updated

14 February 2023

Reviewed by

Short on time? Get an AI generated summary of this article instead

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.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 6 February 2023

Last updated: 6 October 2023

Last updated: 5 February 2023

Last updated: 16 April 2023

Last updated: 7 March 2023

Last updated: 9 March 2023

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free

Frequently asked questions

What’s the definition of an independent variable.

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.

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).

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

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:

• 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)

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

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.

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.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Ask our team

Want to contact us directly? No problem.  We  are always here for you.

• Email [email protected]
• Start live chat
• Call +1 (510) 822-8066
• WhatsApp +31 20 261 6040

Our team helps students graduate by offering:

• A world-class citation generator
• Plagiarism Checker software powered by Turnitin
• Innovative Citation Checker software
• Professional proofreading services
• Over 300 helpful articles about academic writing, citing sources, plagiarism, and more

Scribbr specializes in editing study-related documents . We proofread:

• PhD dissertations
• Research proposals
• Personal statements
• Admission essays
• Motivation letters
• Reflection papers
• Journal articles
• Capstone projects

Scribbr’s Plagiarism Checker is powered by elements of Turnitin’s Similarity Checker , namely the plagiarism detection software and the Internet Archive and Premium Scholarly Publications content databases .

The add-on AI detector is powered by Scribbr’s proprietary software.

The Scribbr Citation Generator is developed using the open-source Citation Style Language (CSL) project and Frank Bennett’s citeproc-js . It’s the same technology used by dozens of other popular citation tools, including Mendeley and Zotero.

You can find all the citation styles and locales used in the Scribbr Citation Generator in our publicly accessible repository on Github .

Choose Your Test

Sat / act prep online guides and tips, independent and dependent variables: which is which.

General Education

Independent and dependent variables are important for both math and science. If you don't understand what these two variables are and how they differ, you'll struggle to analyze an experiment or plot equations. Fortunately, we make learning these concepts easy!

In this guide, we break down what independent and dependent variables are , give examples of the variables in actual experiments, explain how to properly graph them, provide a quiz to test your skills, and discuss the one other important variable you need to know.

What Is an Independent Variable? What Is a Dependent Variable?

A variable is something you're trying to measure. It can be practically anything, such as objects, amounts of time, feelings, events, or ideas. If you're studying how people feel about different television shows, the variables in that experiment are television shows and feelings. If you're studying how different types of fertilizer affect how tall plants grow, the variables are type of fertilizer and plant height.

There are two key variables in every experiment: the independent variable and the dependent variable.

Independent variable: What the scientist changes or what changes on its own.

Dependent variable: What is being studied/measured.

The independent variable (sometimes known as the manipulated variable) is the variable whose change isn't affected by any other variable in the experiment. Either the scientist has to change the independent variable herself or it changes on its own; nothing else in the experiment affects or changes it. Two examples of common independent variables are age and time. There's nothing you or anything else can do to speed up or slow down time or increase or decrease age. They're independent of everything else.

The dependent variable (sometimes known as the responding variable) is what is being studied and measured in the experiment. It's what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).

An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

It can be a lot easier to understand the differences between these two variables with examples, so let's look at some sample experiments below.

Examples of Independent and Dependent Variables in Experiments

Below are overviews of three experiments, each with their independent and dependent variables identified.

Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels so you can get the most value for your money. You test different brands of popcorn to see which bag pops the most popcorn kernels.

• Independent Variable: Brand of popcorn bag (It's the independent variable because you are actually deciding the popcorn bag brands)
• Dependent Variable: Number of kernels popped (This is the dependent variable because it's what you measure for each popcorn brand)

Experiment 2 : You want to see which type of fertilizer helps plants grow fastest, so you add a different brand of fertilizer to each plant and see how tall they grow.

• Independent Variable: Type of fertilizer given to the plant
• Dependent Variable: Plant height

Experiment 3: You're interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures.

• Independent Variable: Ocean temperature
• Dependent Variable: The number of algae in the sample

For each of the independent variables above, it's clear that they can't be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.

Where Do You Put Independent and Dependent Variables on Graphs?

Independent and dependent variables always go on the same places in a graph. This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis.

Here's an example:

As you can see, this is a graph showing how the number of hours a student studies affects the score she got on an exam. From the graph, it looks like studying up to six hours helped her raise her score, but as she studied more than that her score dropped slightly.

The amount of time studied is the independent variable, because it's what she changed, so it's on the x-axis. The score she got on the exam is the dependent variable, because it's what changed as a result of the independent variable, and it's on the y-axis. It's common to put the units in parentheses next to the axis titles, which this graph does.

There are different ways to title a graph, but a common way is "[Independent Variable] vs. [Dependent Variable]" like this graph. Using a standard title like that also makes it easy for others to see what your independent and dependent variables are.

Are There Other Important Variables to Know?

Independent and dependent variables are the two most important variables to know and understand when conducting or studying an experiment, but there is one other type of variable that you should be aware of: constant variables.

Constant variables (also known as "constants") are simple to understand: they're what stay the same during the experiment. Most experiments usually only have one independent variable and one dependent variable, but they will all have multiple constant variables.

For example, in Experiment 2 above, some of the constant variables would be the type of plant being grown, the amount of fertilizer each plant is given, the amount of water each plant is given, when each plant is given fertilizer and water, the amount of sunlight the plants receive, the size of the container each plant is grown in, and more. The scientist is changing the type of fertilizer each plant gets which in turn changes how much each plant grows, but every other part of the experiment stays the same.

In experiments, you have to test one independent variable at a time in order to accurately understand how it impacts the dependent variable. Constant variables are important because they ensure that the dependent variable is changing because, and only because, of the independent variable so you can accurately measure the relationship between the dependent and independent variables.

If you didn't have any constant variables, you wouldn't be able to tell if the independent variable was what was really affecting the dependent variable. For example, in the example above, if there were no constants and you used different amounts of water, different types of plants, different amounts of fertilizer and put the plants in windows that got different amounts of sun, you wouldn't be able to say how fertilizer type affected plant growth because there would be so many other factors potentially affecting how the plants grew.

3 Experiments to Help You Understand Independent and Dependent Variables

If you're still having a hard time understanding the relationship between independent and dependent variable, it might help to see them in action. Here are three experiments you can try at home.

Experiment 1: Plant Growth Rates

One simple way to explore independent and dependent variables is to construct a biology experiment with seeds. Try growing some sunflowers and see how different factors affect their growth. For example, say you have ten sunflower seedlings, and you decide to give each a different amount of water each day to see if that affects their growth. The independent variable here would be the amount of water you give the plants, and the dependent variable is how tall the sunflowers grow.

Experiment 2: Chemical Reactions

Explore a wide range of chemical reactions with this chemistry kit . It includes 100+ ideas for experiments—pick one that interests you and analyze what the different variables are in the experiment!

Experiment 3: Simple Machines

Build and test a range of simple and complex machines with this K'nex kit . How does increasing a vehicle's mass affect its velocity? Can you lift more with a fixed or movable pulley? Remember, the independent variable is what you control/change, and the dependent variable is what changes because of that.

Quiz: Test Your Variable Knowledge

Can you identify the independent and dependent variables for each of the four scenarios below? The answers are at the bottom of the guide for you to check your work.

Scenario 1: You buy your dog multiple brands of food to see which one is her favorite.

Scenario 2: Your friends invite you to a party, and you decide to attend, but you're worried that staying out too long will affect how well you do on your geometry test tomorrow morning.

Scenario 3: Your dentist appointment will take 30 minutes from start to finish, but that doesn't include waiting in the lounge before you're called in. The total amount of time you spend in the dentist's office is the amount of time you wait before your appointment, plus the 30 minutes of the actual appointment

Scenario 4: You regularly babysit your little cousin who always throws a tantrum when he's asked to eat his vegetables. Over the course of the week, you ask him to eat vegetables four times.

Summary: Independent vs Dependent Variable

Knowing the independent variable definition and dependent variable definition is key to understanding how experiments work. The independent variable is what you change, and the dependent variable is what changes as a result of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

When graphing these variables, the independent variable should go on the x-axis (the horizontal axis), and the dependent variable goes on the y-axis (vertical axis).

Constant variables are also important to understand. They are what stay the same throughout the experiment so you can accurately measure the impact of the independent variable on the dependent variable.

What's Next?

Independent and dependent variables are commonly taught in high school science classes. Read our guide to learn which science classes high school students should be taking.

Scoring well on standardized tests is an important part of having a strong college application. Check out our guides on the best study tips for the SAT and ACT.

Interested in science? Science Olympiad is a great extracurricular to include on your college applications, and it can help you win big scholarships. Check out our complete guide to winning Science Olympiad competitions.

Quiz Answers

1: Independent: dog food brands; Dependent: how much you dog eats

2: Independent: how long you spend at the party; Dependent: your exam score

3: Independent: Amount of time you spend waiting; Dependent: Total time you're at the dentist (the 30 minutes of appointment time is the constant)

4: Independent: Number of times your cousin is asked to eat vegetables; Dependent: number of tantrums

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

Ask a Question Below

Have any questions about this article or other topics? Ask below and we'll reply!

Improve With Our Famous Guides

• For All Students

The 5 Strategies You Must Be Using to Improve 160+ SAT Points

How to Get a Perfect 1600, by a Perfect Scorer

Series: How to Get 800 on Each SAT Section:

Score 800 on SAT Math

Score 800 on SAT Reading

Score 800 on SAT Writing

Series: How to Get to 600 on Each SAT Section:

Score 600 on SAT Math

Score 600 on SAT Reading

Score 600 on SAT Writing

Free Complete Official SAT Practice Tests

What SAT Target Score Should You Be Aiming For?

15 Strategies to Improve Your SAT Essay

The 5 Strategies You Must Be Using to Improve 4+ ACT Points

How to Get a Perfect 36 ACT, by a Perfect Scorer

Series: How to Get 36 on Each ACT Section:

36 on ACT English

36 on ACT Math

36 on ACT Reading

36 on ACT Science

Series: How to Get to 24 on Each ACT Section:

24 on ACT English

24 on ACT Math

24 on ACT Reading

24 on ACT Science

What ACT target score should you be aiming for?

ACT Vocabulary You Must Know

ACT Writing: 15 Tips to Raise Your Essay Score

How to Get Into Harvard and the Ivy League

How to Get a Perfect 4.0 GPA

How to Write an Amazing College Essay

What Exactly Are Colleges Looking For?

Is the ACT easier than the SAT? A Comprehensive Guide

Should you retake your SAT or ACT?

When should you take the SAT or ACT?

Stay Informed

Get the latest articles and test prep tips!

Looking for Graduate School Test Prep?

Check out our top-rated graduate blogs here:

GRE Online Prep Blog

GMAT Online Prep Blog

TOEFL Online Prep Blog

Holly R. "I am absolutely overjoyed and cannot thank you enough for helping me!”

Organizing Your Social Sciences Research Paper: Independent and Dependent Variables

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

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 Indepent 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.

• << Previous: Design Flaws to Avoid
• Next: Choosing a Research Problem >>
• Last Updated: Sep 8, 2023 12:19 PM
• URL: https://guides.library.txstate.edu/socialscienceresearch

This paper is in the following e-collection/theme issue:

Published on 28.6.2024 in Vol 26 (2024)

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis

Authors of this article:

Original Paper

• Yuezhou Zhang 1 , PhD   ; 
• Amos A Folarin 1, 2, 3, 4, 5 , PhD   ; 
• Shaoxiong Sun 1, 6 , PhD   ; 
• Nicholas Cummins 1 , PhD   ; 
• Yatharth Ranjan 1 , MSc   ; 
• Zulqarnain Rashid 1 , PhD   ; 
• Callum Stewart 1 , PhD   ; 
• Pauline Conde 1 , BSc   ; 
• Heet Sankesara 1 , BSc   ; 
• Petroula Laiou 1 , PhD   ; 
• Faith Matcham 7, 8 , PhD   ; 
• Katie M White 7 , BSc   ; 
• Carolin Oetzmann 7 , MSc   ; 
• Femke Lamers 9, 10 , PhD   ; 
• Sara Siddi 11, 12, 13 , PhD   ; 
• Sara Simblett 14 , PhD   ; 
• Srinivasan Vairavan 15 , PhD   ; 
• Inez Myin-Germeys 16 , PhD   ; 
• David C Mohr 17 , PhD   ; 
• Til Wykes 14, 18 , PhD   ; 
• Josep Maria Haro 11, 12, 13 , PhD   ; 
• Peter Annas 19 , PhD   ; 
• Brenda WJH Penninx 9, 10 , PhD   ; 
• Vaibhav A Narayan 15, 20 , PhD   ; 
• Matthew Hotopf 3, 7, 18 , PhD   ; 
• Richard JB Dobson 1, 2, 3, 4, 5 , PhD   ; 
• RADAR-CNS consortium 21

1 Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom

2 Institute of Health Informatics, University College London, London, United Kingdom

3 NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, United Kingdom

4 NIHR Biomedical Research Centre at University College London Hospitals, NHS Foundation Trust, London, United Kingdom

5 Health Data Research UK London, University College London, London, United Kingdom

6 Department of Computer Science, University of Sheffield, Sheffield, United Kingdom

7 Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom

8 School of Psychology, University of Sussex, Falmer, United Kingdom

9 Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, Netherlands

10 Mental Health Program, Amsterdam Public Health Research Institute, Amsterdam, Netherlands

11 Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain

12 Teaching Research and Innovation Unit, Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain

13 Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain

14 Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom

15 Janssen Research and Development LLC, Titusville, NJ, United States

16 Department of Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium

17 Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, United States

18 South London and Maudsley NHS Foundation Trust, London, United Kingdom

19 H Lundbeck A/S, Copenhagen, Denmark

20 Davos Alzheimer’s Collaborative, Geneva, Switzerland

21 See Acknowledgments

Corresponding Author:

Yuezhou Zhang, PhD

Department of Biostatistics & Health Informatics

Institute of Psychiatry, Psychology and Neuroscience

King's College London

SGDP Centre, De Crespigny Park, Denmark Hill

London, SE5 8AF

United Kingdom

Phone: 44 7579856617

Email: [email protected]

Background: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.

Objective: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.

Methods: Data were sourced from a large longitudinal mHealth study, wherein participants’ depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants’ behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score.

Results: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=–93.61, P <.001), increased sleep variability (β=0.96, P <.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P =.001; sleep offset: β=1.12, P <.001; M10 onset: β=0.73, P =.003; HR acrophase: β=0.71, P =.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = –31.51, P =.002) and summer (β of PHQ-8 × summer = –42.61, P <.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P= .008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer.

Conclusions: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

Introduction

Depression is a globally prevalent mental disorder with various negative impacts, including reduced quality of life, disability, premature mortality, and an increased risk of suicide [ 1 - 6 ]. However, current depression diagnostic methods face several critical limitations: (1) reliance on individuals’ subjective recall of past behaviors, which introduces recall bias and neglects day-to-day fluctuations [ 7 , 8 ]; (2) dependence on skilled and experienced clinicians [ 9 , 10 ]; and (3) evaluations often delayed until the mental health issues have progressed to a more severe, difficult-to-treat stage [ 11 ]. Consequently, these limitations result in the underdiagnosis and delayed treatment for those with depression [ 12 , 13 ], underscoring the critical need for objective and timely methods for early detection.

Previous research has found significant links between circadian rhythms and depression [ 14 , 15 ]. Circadian rhythms are approximately 24-hour endogenous oscillations, controlled by the master clock in the suprachiasmatic nucleus of the hypothalamus, that regulate many aspects of human behavior and physiology, such as sleep-wake cycles, hormone secretion, and body temperature [ 16 - 18 ]. Disturbances in circadian rhythms have been associated with an increased risk of both physical and mental diseases [ 15 , 19 - 21 ]. Therefore, tracking human circadian rhythms is a potential objective method for early-stage depression identification. Traditional methods for circadian rhythm assessment involve tracking melatonin in bodily fluids (such as saliva, urine, or blood samples) in a constant light environment to prevent external light from altering melatonin production and biasing the assessment of the circadian phase [ 22 , 23 ]. However, this traditional approach is expensive, labor-intensive, and impractical for large cohort studies and long-term monitoring in real-world settings [ 24 , 25 ].

With the development of ubiquitous sensors and mobile technologies, wearable devices provide a convenient and cost-effective way to continuously monitor individuals’ daily behaviors and physiological signals in real-world settings [ 26 ]. Previous mobile health (mHealth) studies have explored the approximation of human circadian rhythms through wearable-measured patterns, including sleep-wake cycles, rest-activity patterns, and circadian rhythms in heart rate (HR) [ 24 , 27 - 32 ]. Higher depression severity has been found to be associated with later sleep onset and offset times, higher sleep variability, lower amplitude of activity, lower intradaily stability, and later acrophase of activity and HR [ 24 , 27 - 32 ].

However, previous mHealth studies have not fully accounted for seasonal variations, possibly due to their short study durations. Seasonal changes in sunlight and temperature are crucial environmental zeitgebers for the internal circadian clock, impacting human circadian rhythms [ 33 - 35 ]. Prior research has reported significant seasonal effects on sleep patterns and activity levels [ 36 - 39 ]. Ignoring these seasonal impacts may introduce bias into the associations between depression and wearable-measured circadian rhythms in real-world settings. Thus, examining the effects of seasonality on wearable-measured circadian rhythms and their connections to depression in a comprehensive longitudinal data set is needed.

The primary aim of this study was to explore the associations between depression severity and wearable-measured circadian rhythms, accounting for seasonal effects and investigating potential variations across seasons. Our secondary aim was to quantify the seasonal changes in wearable-measured circadian rhythms within a European mHealth study for depression [ 40 ].

Participants and Settings

The data analyzed in this study were sourced from the Remote Assessment of Disease and Relapse Major Depressive Disorder (RADAR-MDD) research program, which aimed to investigate the utility of remote technologies for monitoring depression and understanding factors that could help predict relapse in MDD [ 40 ]. A total of 623 participants were recruited from 3 study sites across 3 European countries (United Kingdom, Spain, and the Netherlands) and followed for up to 2 years [ 41 ]. The first participant was enrolled in November 2017 and the last participant was enrolled in June 2020, and the data collection was completed in April 2021. Due to this rolling enrollment process, participants’ involvement in the study varied from 11 months to 24 months [ 41 ]. The RADAR-MDD program used the RADAR-base open-source platform to concurrently gather both active (eg, questionnaires) and passive (eg, wearable) data [ 42 ]. Comprehensive details of the study protocol and data set have been documented in publications [ 40 ] and [ 41 ], respectively.

Patient Involvement

The RADAR-MDD protocol was co-developed with a patient advisory board who shared their opinions on several user-facing aspects of the study including the choice and frequency of survey measures, the usability of the study app, participant-facing documents, selection of optimal participation incentives, selection and deployment of wearable devices, and the data analysis plan.

Ethical Considerations

Ethical approvals were obtained from the Camberwell St. Giles Research Ethics Committee (17/LO/1154) in the United Kingdom, the Fundacio Sant Joan de Deu Clinical Research Ethics Committee (CI: PIC-128-17) in Spain, and the Medische Ethische Toetsingscommissie VUmc (2018.012- NL63557.029.17) in the Netherlands. All participants provided their written informed consent. Before the data collection, participants were assured of their privacy protection, informed of their right to withdraw at any time without needing to justify their decision, and allowed to request the deletion of all their collected data. The data from participants were pseudonymized and stored in a research database, adhering to the General Data Protection Regulation. Participants were compensated with £15 (US $19.09)/€20 (US $21.68) for enrollment, £5 (US $6.36)/€10 (US $10.84) for each clinical assessment conducted every 3 months, and an additional £10 (US $12.73)/€10 (US $10.84) for each qualitative interview they completed. No participant is identifiable in any images in the manuscript or the supplementary materials. Further details can be found in the study protocol and data description papers [ 40 , 41 ].

Depression Symptom Severity

Participants’ depression symptom severity was measured using the 8-item Patient Health Questionnaire (PHQ-8) [ 43 ] conducted via mobile phones every 2 weeks. The PHQ-8 comprises 8 questions, and the total score of PHQ-8 ranges from 0 to 24, indicating increasing severity [ 43 ].

Fitbit Data

Participants were asked to wear a Fitbit Charge 2/3 wrist-worn device throughout the whole study. Participants’ sleep, step count, and HR were continuously (24/7) measured and recorded.

• Sleep data: Fitbit provided sleep labels (“awake,” “light sleep,” “deep sleep,” and “rapid eye movement”) along with the corresponding local clock times every 30 seconds.
• Step data: Participants’ accumulated steps were counted every minute.
• HR data: Fitbit provided an estimate of HR every 5 seconds, using an embedded photoplethysmography sensor. However, technical issues resulted in the absence of some sample points. To obtain the robust HR trend and align with step data, we calculated the average HR over 1 minute.

The seasonal division used in this study was based on European Union astronomical seasons: spring (March 20 to June 20), summer (June 21 to September 22), autumn (September 23 to December 20), and winter (December 21 to March 19).

In accordance with findings from previous studies [ 44 - 46 ], we considered several covariates that could potentially influence participants’ circadian rhythms, including age, gender, and employment status. Because the COVID-19 pandemic and relevant restrictions had some significant impacts on individuals’ behavior [ 47 ], we introduced a covariate “lockdown” to indicate the presence of a national lockdown. Furthermore, as the experience of seasons can be different across countries, the study site was also considered as a covariate. These covariates were considered in our statistical analysis.

Feature Extraction

Phq-8 interval.

To link human circadian rhythms with depression severity, we extracted circadian rhythm features from each 14-day PHQ-8 interval—specifically, 14 days of Fitbit data preceding a completed PHQ-8. This feature window aligns with the PHQ-8’s purpose of evaluating depressive symptom severity over the past 2 weeks [ 43 ] and is consistent with both the existing literature in the mHealth field [ 48 - 50 ] and our previous studies [ 51 , 52 ]. Figure 1 shows an example of a participant’s processed HR, step, and sleep data in a 14-day PHQ-8 interval.

Data Inclusion Criteria

Some Fitbit data were missing in our data set for several reasons, including device damage, low battery level, and not being worn. Building on insights from our prior research [ 53 ], which discussed the sufficient days for stable feature calculation, we focused on 14-day PHQ-8 intervals with at least 8 days having more than 80% of step and HR data and a sleep recording in this study. Considering the potential impact of daylight saving time on individuals’ behaviors [ 54 ], we excluded the first 14-day PHQ-8 interval after the time switching [ 37 ]. We then extracted a total of 12 features for reflecting the circadian rhythms from sleep, step count, and HR data.

Sleep-Wake Rhythms

Fitbit shows promise in identifying sleep-wake status [ 55 , 56 ]. Therefore, to reflect the sleep-wake rhythms, we computed four features: (1) sleep duration (the mean total sleep time), (2) sleep variability (the SD of total sleep time), (3) sleep onset (the mean clock time of falling asleep), and (4) sleep offset (the mean clock time of wake-up) [ 51 ].

Rest-Activity Rhythms

We extracted five nonparametric features from the Fitbit step count recordings to characterize the stability, fragmentation, timing, and mean activity level of participants’ rest-activity rhythms, using the R package nparACT (R Foundation for Statistical Computing) [ 57 , 58 ]. These features include (1) intradaily variability of steps (step IV), quantifying the fragmentation in the rest-activity cycle; (2) interdaily stability of steps (step IS), quantifying the stability of rest-activity patterns over a 14-day PHQ-8 interval; (3) L5 onset, representing the onset of least active continuous 5-hour period; (4) M10 onset, representing the onset of the most active continuous 10-hour period; and (5) daily step, representing the mean of daily total steps in a 14-day PHQ-8 interval [ 57 , 58 ].

Circadian Rhythm in HR

For estimating circadian rhythms in HR, we used cosinor analysis—fitting a cosine wave to time series behavioral data through least-squares regression, which has been widely used in previous mHealth studies [ 59 - 61 ]. Using the R package cosinor , we performed the cosinor analysis on Fitbit HR data of each 14-day PHQ-8 interval and extracted the following parameters: (1) HR MESOR, the midline estimating statistic of the fitted cosine wave for HR; (2) HR amplitude, the difference between the peak value and MESOR of the fitted cosine wave for HR; and (3) HR acrophase, the timing of the HR peak [ 59 - 61 ].

Statistical Analysis

Given the longitudinal nature of our data set, that is, each participant had repeated measurements, we used the linear mixed-effects model [ 62 ] with a participant-specific random intercept in this study, implemented using the R package lmerTest . To investigate whether disregarding seasonal effects biases the associations between depression severity and circadian rhythms, we established and compared the following 3 models for each of circadian rhythm features.

• Model 1: A linear mixed-effects model was established to regress each circadian rhythm feature with only the PHQ-8 score as the independent variable.
• Model 2: Season was included as an independent variable in addition to the PHQ-8 score, considering seasonal effects on the circadian rhythms feature.
• Model 3: To further explore potential variations in the association between depression severity and circadian rhythms across seasons, an interaction term between the PHQ-8 score and season was added to the main-effects model (model 2).

All models were adjusted by covariates: age, gender, study site, lockdown, and employment status. The equations of these 3 models are outlined as follows:

• Model 1: Circadian rhythm = β 1 PHQ‑8 + COVs
• Model 2: Circadian rhythm = β 1 PHQ‑8 + β 2 season + COVs
• Model 3: Circadian rhythm = β 1 PHQ‑8 + β 2 season + β 3 PHQ‑8 × season + COVs

where COVs represents all covariates mentioned above and circadian rhythm is one of the wearable-measured circadian rhythm features.

Likelihood ratio tests were then performed to examine whether including more variables (season and the interaction term) can significantly improve the fitting of the regression model. The Benjamini-Hochberg method was used for the correction of multiple comparisons [ 63 ]. Despite the inclusion of lockdown as a covariate, we also repeated our analysis on the subset of data before the COVID-19 pandemic (before January 31, 2020).

Data Summary

In accordance with our data inclusion criteria (see the Methods section), we analyzed a total of 10,018 PHQ-8 records alongside corresponding Fitbit data from 543 participants, with an average of 16 recordings per participant. The cohort selected for this study had a median age of 48 (IQR 32-58) years, was predominantly female (n=414, 76.2%), and included 230 (42.4%) employed participants. The distribution of PHQ-8 records was approximately uniform across the seasons, with winter accounting for 26.9%, spring 23.6%, summer 25.5%, and autumn 23.9%. We observed that demographics and data collection varied across study sites, with participants at the CIBER (Centro de Investigación Biomédica en Red) site in Spain being older and more likely to be retired, whereas the King’s College London site in the United Kingdom had the highest number of participants and PHQ-8 records collected. Table 1 summarizes participant demographics and PHQ-8 records for the entire cohort, with site-specific comparisons. Figure 2 visualizes the variations of wearable-measured circadian rhythm features across a year.

a PHQ-8: 8-item Patient Health Questionnaire.

b Due to the private issue, participants’ specific geographic information was not collected.

c CIBER: Centro de Investigación Biomédica en Red.

d KCL: King’s College London.

e VUMC: Vrije Universiteit Medisch Centrum.

Associations Between the PHQ-8 score and Wearable-Measured Circadian Rhythm Features

The results of models 1 and 2 are displayed in Table 2 . The likelihood ratio tests revealed that incorporating seasonal effects (model 2) significantly improved the model fit for all 12 circadian rhythm features. Specifically, model 2 demonstrated significant positive associations of the PHQ-8 score with several features: sleep duration (β 1 =0.46, P <.001), sleep onset (β 1 =0.55, P =.001), sleep offset (β 1 =1.12, P <.001), sleep variability (β 1 =0.96, P <.001), L5 onset (β 1 =0.43, P =.04), M10 onset (β 1 =0.73, P =.003), and HR acrophase (β 1 =0.71, P =.001). Conversely, significant negative associations were observed with daily step (β 1 =–93.61, P <.001), step IS (β 1 =–0.001, P <.001), HR MESOR (β 1 =–0.03, P <.001), and HR amplitude (β 1 =–0.04, P <.001). However, the effect sizes of PHQ-8 on step IS, HR MESOR, and HR amplitude are small, according to their practical meanings.

b Model 1: Circadian rhythm = β 1 PHQ‑8 + COVs.

c Model 2: Circadian rhythm = β 1 PHQ‑8 + β 2 season + COVs, where COVs represents covariates mentioned in the Methods section. Note that winter is the reference season.

d The exact P values are reported in the main text.

e IV: intradaily variability.

f IS: interdaily stability.

g L5 onset: the onset of least active continuous 5-hour period.

h M10 onset: the onset of the most active continuous 10-hour period.

i HR: heart rate.

j HR MESOR: midline estimating statistic of the fitted cosine wave for heart rate.

* P <.05, ** P< .01, *** P <.001.

The results of model 3, along with the likelihood ratio comparison with model 2, are summarized in Table 3 . We found that the inclusion of the interaction term between PHQ-8 and seasonality significantly improved the model fit for daily step, step IV, step IS, M10 onset, HR MESOR, and HR amplitude. Notably, the negative association between the PHQ-8 score and daily step exhibited greater strength in spring (β 3 of PHQ-8 × spring = –31.51, P =.002) and summer (β 3 of PHQ-8 × summer = –42.61, P <.001) than in winter. Additionally, a significant positive association between M10 onset and PHQ-8 was observed exclusively in summer (β 3 of PHQ-8 × summer = 1.06, P =.008), with no significant associations observed in other seasons.

a The exact P values are reported in the main text.

b PHQ-8: 8-item Patient Health Questionnaire.

c Model 3: Circadian rhythm = β 1 PHQ‑8 + β 2 season + β 3 PHQ‑8 × season + COVs, where COVs represents covariates mentioned in the Methods section. Note that winter is the reference season.

d IV: intradaily variability.

e IS: interdaily stability.

f L5 onset: the onset of least active continuous 5-hour period.

g M10 onset: the onset of the most active continuous 10-hour period.

h HR: heart rate.

i HR MESOR: midline estimating statistic of the fitted cosine wave for heart rate.

Seasonal Effects on Wearable-Measured Circadian Rhythm Features

Our results reveal significant seasonal impacts on various circadian rhythms, as indicated by the seasonal coefficients in model 2 ( Table 2 ). Notably, HR acrophase varied significantly across seasons, delayed by 43.4 minutes in spring ( P <.001), 67.9 minutes in summer ( P <.001), and 24.1 minutes in autumn ( P <.001) compared with winter (the reference season).

In terms of rest-activity rhythms, compared with winter, summer was associated with 394.5 more daily steps ( P <.001) and 0.03 lower step IV ( P <.001), and M10 onset was 20.5 minutes later ( P <.001); autumn was associated with 186.2 more daily steps ( P =.002) and 0.02 lower step IV ( P <.001); and spring was associated M10 onset being 11.9 minutes later ( P <.001).

Regarding sleep-wake rhythms, compared with winter, we found that (1) sleep duration decreased by 6.5 minutes in spring ( P <.001), 16.6 minutes in summer ( P <.001), and 6.1 minutes in autumn ( P <.001); (2) sleep onset was 4.6 minutes earlier in autumn ( P =.004), 3.7 minutes later in spring ( P =0.02), and 7.2 minutes later in summer ( P <.001); (3) sleep offset was 6.9 minutes earlier in summer ( P <.001) and 8.3 minutes earlier in autumn ( P <.001); and (4) sleep variability was 8.5 minutes lower in spring ( P <.001), 5.3 minutes lower in summer ( P <.001), and 2.8 minutes lower in autumn ( P =.04).

Site-to-Site Analysis

Results of our subset analyses across 3 study sites are shown in Tables S1-S3 in Multimedia Appendix 1 . Despite a reduction in sample sizes leading to some nonsignificant coefficients, the overall direction of associations between the PHQ-8 and circadian rhythm features remained consistent with the results in the entire data set. Notably, the seasonal changes in sleep-related features and HR acrophase are larger in the Spain site than the sites in the United Kingdom and Netherlands. For instance, during summer compared with winter, participants from the Spain site experienced a significant decrease in sleep duration by 36.6 minutes, whereas participants from the sites in the United Kingdom and Netherlands experienced reductions in sleep duration by 18.9 and 8.5 minutes, respectively. Furthermore, compared with winter, the HR acrophase for participants in Spain was delayed by 123.2 minutes in summer, significantly more than the delays of 58.2 minutes in the UK site and 64.0 minutes in the Netherlands site during summer.

Pre–COVID-19 Subset Analysis

The pre–COVID-19 subset analysis ( Table 4 ) indicated that the directions and significances of associations between depression severity and circadian rhythm features were consistent with results in the entire data set. The seasonal effects on most circadian rhythm features (except for daily step) were similar between the entire data set and the pre–COVID-19 subset. Notably, daily step exhibited larger seasonal fluctuations in the pre–COVID-19 period. Specifically, compared with winter, participants in the pre–COVID-19 subset exhibited 657.2, 1096.4, and 321.6 more daily steps in spring, summer, and autumn, respectively.

c Model 3: Circadian rhythm = β 1 PHQ‑8 + β 2 season + β 3 PHQ‑8 × season + COVs, where COVs represents covariates mentioned in the Methods section.

* P <.05, ** P <.01, *** P< .001.

Primary Findings

Our study, derived from a large European longitudinal mHealth study focused on depression, revealed significant seasonal effects on wearable-measured circadian rhythms and their associations with depression.

One of our key findings is that the associations between depression severity and certain wearable-measured circadian rhythms varied across different seasons. Specifically, we found a stronger negative association between depression and daily step count during the warmer seasons of spring and summer compared with winter. Additionally, a significant positive association was identified between depression severity and the onset of the most active continuous 10-hour period exclusively in summer. This may be attributed to the more favorable weather conditions for outdoor activities in summer [ 64 ], which could increase the observable relationship between rest-activity rhythms and depression. Furthermore, we found that the inclusion of seasonal effects can significantly improve the model fit for all wearable-measured circadian rhythms. These findings highlight the critical importance of including seasonal effects in longitudinal mHealth studies.

On adjusting for seasonal effects, we found that higher depression severity was significantly associated with lower levels of physical activity (daily step), more irregular activities (sleep variability and step IS), and later circadian rhythm timings (sleep onset and offset, M10 onset, and HR acrophase). These relationships are consistent with prior research. The linkage between reduced physical activity levels and worsened depression severity was reported in both survey-based and mHealth studies [ 65 , 66 ]. The preventive and therapeutic roles of physical activity against depression have been well-documented [ 67 , 68 ]. Moreover, there is extensive evidence linking depression with more irregular daily behaviors, including increased sleep variability [ 51 , 69 - 71 ], less stable rest-activity rhythms [ 72 ], and irregular patterns in Bluetooth [ 52 ] and GPS [ 73 ] data. The delayed circadian rhythm timings have been found to be associated with higher depression severity in multiple data streams, such as sleep recordings [ 51 , 69 - 71 ], activity logs [ 61 ], and HR recordings [ 74 ]. These consistent findings highlight the close associations between circadian rhythms and depression severity.

This study also revealed significant seasonal impacts on circadian rhythms, most notably between summer and winter. The cohort demonstrated shorter and later sleep patterns, increased daily step counts, reduced intradaily step variability, and delayed circadian rhythm timings during summer compared with winter. Especially, the circadian phase (HR acrophase) was delayed by 67.9 minutes in summer. These findings mostly align with previous laboratory and survey-based studies, which suggested that people tend to sleep longer in winter, possibly due to the effects of light exposure on melatonin production [ 75 - 78 ]. Previous studies have similarly observed higher activity levels [ 36 , 38 , 39 ], lower intradaily variability [ 79 , 80 ], and delayed hormone secretion rhythm [ 81 , 82 ] in summer compared with winter, further supporting our findings.

Conducted on a large cohort over an extended period using 3 wearable data modalities, this study aligned with some previous survey and laboratory findings, highlighting the precision of mobile technology in tracking behavioral rhythms. Our findings suggest that wearable-measured circadian rhythms have the potential to be digital biomarkers for depression detection. Furthermore, this study could inform the future design of seasonal, context-sensitive mHealth interventions, such as tailored activity recommendations and light exposure therapy.

Limitations

This study has several limitations that may impact its findings and interpretations. First, the presence of missing data may introduce bias, as our previous study indicated data compliance is associated with depression severity and other personal traits (eg, age) [ 83 ]. Second, the specificity of our cohort, characterized by a history of depression and a predominantly female composition, may limit the generalizability of our findings, highlighting the need for validation in more general populations. Third, while we accounted for national lockdowns as a covariate in the regression models and conducted analysis on the pre–COVID-19 subset, the complexities and varied impacts of COVID-19 necessitate additional validations in the postpandemic data sets. Fourth, our study relied on PHQ-8 scores as depression labels, which may introduce subjective biases.

Lastly, while we adjusted for study sites (in different countries) as a covariate in our regression models and conducted the site-to-site analyses, the influence of geographical location on circadian rhythms and their relationship is still needed to be explored in future research, ideally through the randomized controlled trials.

Conclusions

Our analysis of longitudinal wearable data from a large cohort reveals the significant seasonal impact on circadian rhythms and their associations with depression, emphasizing the importance of accounting for seasonal variations in longitudinal mHealth research. Additionally, we found that wearable-measured circadian rhythms are significantly linked to depression severity, indicating their potential to be digital biomarkers for depression detection. These findings enrich our understanding of the mechanisms and pathology underlying depression and could inform the future design of mental health monitoring and interventions.

Acknowledgments

The Remote Assessment of Disease and Relapse–Central Nervous System (RADAR-CNS) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking under grant agreement No. 115902. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). This communication reflects the views of the RADAR-CNS consortium, and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. The funding bodies have not been involved in the design of the study, the collection or analysis of data, or the interpretation of data. This study represents independent research partly funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC) at South London, and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. We thank all the members of the RADAR-CNS patient advisory board for their contribution to the device selection procedures, and their invaluable advice throughout the study protocol design. This research was reviewed by a team with experience of mental health problems and their careers, who have been specially trained to advise on research proposals and documentation through Feasibility and Acceptability Support Team for Researchers (FAST-R), a free, confidential service in England provided by the NIHR Maudsley BRC via King’s College London and South London and Maudsley NHS Foundation Trust. We thank all GLAD Study volunteers for their participation and gratefully acknowledge the NIHR BioResource, NIHR BioResource centers, NHS Trusts, and staff for their contribution. We also acknowledge NIHR BRC, King’s College London, South London and Maudsley NHS Trust, and King’s Health Partners. We thank the NIHR, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Program. Participants in the CIBER site came from the following 4 clinical communities in Spain: Parc Sanitari Sant Joan de Déu Network services, Institut Català de la Salut, Institut Pere Mata, and Hospital Clínico San Carlos. Participant recruitment in Amsterdam was partially accomplished through Hersenonderzoek.nl, a Dutch online registry that facilitates participant recruitment for neuroscience studies. Hersenonderzoek.nl is funded by ZonMwMemorabel (project No. 73305095003), a project in the context of the Dutch Deltaplan Dementie, Gieskes-Strijbis Foundation, the Alzheimer’s Society in the Netherlands, and Brain Foundation Netherlands. RJBD is supported by the following: (1) NIHR BRC at South London and Maudsley NHS Foundation Trust and King’s College London; (2) Health Data Research UK, which is funded by the UK Medical Research Council (MRC), Engineering and Physical Sciences Research Council (EP/Y035216/1), Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart Consortium, funded by the IMI 2 Joint Undertaking (which receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA, partnering with 20 academic and industry partners and European Society of Cardiology); (4) the NIHR University College London Hospitals BRC; (5) the NIHR BRC at South London and Maudsley (related to attendance at the American Medical Informatics Association) NHS Foundation Trust, and King’s College London; (6) the UK Research and Innovation (UKRI) London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare (AI4VBH); and (7) the NIHR Applied Research Collaboration (ARC) South London at King’s College Hospital NHS Foundation Trust.

Conflicts of Interest

SV is an employee of Janssen Research and Development LLC. VAN was employed by Janssen Research and Development LLC during this study. PA was employed by the pharmaceutical company H. Lundbeck A/S during this study. DCM has accepted honoraria and consulting fees from Apple Inc, Otsuka Pharmaceuticals, Pear Therapeutics, and the One Mind Foundation; received royalties from Oxford Press; and an ownership interest in Adaptive Health Inc. MH is the principal investigator of the Remote Assessment of Disease and Relapse–Central Nervous System project, a private public precompetitive consortium that receives funding from Janssen, UCB, Lundbeck, MSD, and Biogen. CO is supported by the UK Medical Research Council (MR/N013700/1) and King’s College London, member of the MRC Doctoral Training Partnership in Biomedical Sciences. AAS holds shares in Alphabet, owner of FitBit devices. All other authors declare no competing interests.

Results of the subset analyses across 3 study sites.

• Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10(11):e1001547. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Depression and other common mental disorders: global health estimates. WHO. World Health Organization; 2017.
• Lenox-Smith A, Macdonald MTB, Reed C, Tylee A, Peveler R, Quail D, et al. Quality of life in depressed patients in UK primary care: the FINDER study. Neurol Ther. 2013;2(1-2):25-42. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lerner D, Adler DA, Chang H, Berndt ER, Irish JT, Lapitsky L, et al. The clinical and occupational correlates of work productivity loss among employed patients with depression. J Occup Environ Med. 2004;46(6 Suppl):S46-S55. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lewinsohn PM, Solomon A, Seeley JR, Zeiss A. Clinical implications of "subthreshold" depressive symptoms. J Abnorm Psychol. 2000;109(2):345-351. [ Medline ]
• Hawton K, Casañas I Comabella C, Haw C, Saunders K. Risk factors for suicide in individuals with depression: a systematic review. J Affect Disord. 2013;147(1-3):17-28. [ CrossRef ] [ Medline ]
• Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc. 2016;9:211-217. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Devaux M, Sassi F. Social disparities in hazardous alcohol use: self-report bias may lead to incorrect estimates. Eur J Public Health. 2016;26(1):129-134. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wells KB, Hays RD, Burnam MA, Rogers W, Greenfield S, Ware JE. Detection of depressive disorder for patients receiving prepaid or fee-for-service care. Results from the Medical Outcomes Study. JAMA. 1989;262(23):3298-3302. [ Medline ]
• Schulberg HC, Saul M, McClelland M, Ganguli M, Christy W, Frank R. Assessing depression in primary medical and psychiatric practices. Arch Gen Psychiatry. 1985;42(12):1164-1170. [ CrossRef ] [ Medline ]
• Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health. Psychiatr Rehabil J. 2015;38(3):218-226. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Harman JS, Edlund MJ, Fortney JC. Disparities in the adequacy of depression treatment in the United States. Psychiatr Serv. 2004;55(12):1379-1385. [ CrossRef ] [ Medline ]
• Young AS, Klap R, Sherbourne CD, Wells KB. The quality of care for depressive and anxiety disorders in the United States. Arch Gen Psychiatry. 2001;58(1):55-61. [ CrossRef ] [ Medline ]
• Carpenter JS, Crouse JJ, Scott EM, Naismith SL, Wilson C, Scott J, et al. Circadian depression: a mood disorder phenotype. Neurosci Biobehav Rev. 2021;126:79-101. [ CrossRef ] [ Medline ]
• Crouse JJ, Carpenter JS, Song YJC, Hockey SJ, Naismith SL, Grunstein RR, et al. Circadian rhythm sleep-wake disturbances and depression in young people: implications for prevention and early intervention. Lancet Psychiatry. 2021;8(9):813-823. [ CrossRef ] [ Medline ]
• Partch CL, Green CB, Takahashi JS. Molecular architecture of the mammalian circadian clock. Trends Cell Biol. 2014;24(2):90-99. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Foster RG, Kreitzman L. The rhythms of life: what your body clock means to you! Exp Physiol. 2013;99(4):599-606. [ CrossRef ] [ Medline ]
• Refinetti R. Circadian rhythmicity of body temperature and metabolism. Temperature (Austin). 2020;7(4):321-362. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vetter C, Devore EE, Wegrzyn LR, Massa J, Speizer FE, Kawachi I, et al. Association between rotating night shift work and risk of coronary heart disease among women. JAMA. 2016;315(16):1726-1734. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Knutsson A, Kempe A. Shift work and diabetes--a systematic review. Chronobiol Int. 2014;31(10):1146-1151. [ CrossRef ] [ Medline ]
• Musiek ES, Bhimasani M, Zangrilli MA, Morris JC, Holtzman DM, Ju YES. Circadian rest-activity pattern changes in aging and preclinical Alzheimer disease. JAMA Neurol. 2018;75(5):582-590. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Duffy JF, Dijk DJ. Getting through to circadian oscillators: why use constant routines? J Biol Rhythms. 2002;17(1):4-13. [ CrossRef ] [ Medline ]
• Keijzer H, Smits MG, Duffy JF, Curfs LMG. Why the dim light melatonin onset (DLMO) should be measured before treatment of patients with circadian rhythm sleep disorders. Sleep Med Rev. 2014;18(4):333-339. [ CrossRef ] [ Medline ]
• Bowman C, Huang Y, Walch OJ, Fang Y, Frank E, Tyler J, et al. A method for characterizing daily physiology from widely used wearables. Cell Rep Methods. 2021;1(4):100058. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cui S, Lin Q, Gui Y, Zhang Y, Lu H, Zhao H, et al. CARE as a wearable derived feature linking circadian amplitude to human cognitive functions. NPJ Digit Med. 2023;6(1):123. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zapata-Lamana R, Lalanza JF, Losilla JM, Parrado E, Capdevila L. mHealth technology for ecological momentary assessment in physical activity research: a systematic review. PeerJ. 2020;8:e8848. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Smagula SF, Krafty RT, Thayer JF, Buysse DJ, Hall MH. Rest-activity rhythm profiles associated with manic-hypomanic and depressive symptoms. J Psychiatr Res. 2018;102:238-244. [ FREE Full text ] [ CrossRef ] [ Medline ]
• White KH, Rumble ME, Benca RM. Sex differences in the relationship between depressive symptoms and actigraphic assessments of sleep and rest-activity rhythms in a population-based sample. Psychosom Med. 2017;79(4):479-484. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Slyepchenko A, Allega OR, Leng X, Minuzzi L, Eltayebani MM, Skelly M, et al. Association of functioning and quality of life with objective and subjective measures of sleep and biological rhythms in major depressive and bipolar disorder. Aust N Z J Psychiatry. 2019;53(7):683-696. [ CrossRef ] [ Medline ]
• Robillard R, Hermens DF, Naismith SL, White D, Rogers NL, Ip TKC, et al. Ambulatory sleep-wake patterns and variability in young people with emerging mental disorders. J Psychiatry Neurosc. 2015;40(1):28-37. [ CrossRef ] [ Medline ]
• Carr O, Saunders KEA, Bilderbeck AC, Tsanas A, Palmius N, Geddes JR, et al. Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder. Transl Psychiatry. 2018;8(1):79. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ávila Moraes C, Cambras T, Diez-Noguera A, Schimitt R, Dantas G, Levandovski R, et al. A new chronobiological approach to discriminate between acute and chronic depression using peripheral temperature, rest-activity, and light exposure parameters. BMC Psychiatry. 2013;13:77. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Wright KP, McHill AW, Birks BR, Griffin BR, Rusterholz T, Chinoy ED. Entrainment of the human circadian clock to the natural light-dark cycle. Curr Biol. 2013;23(16):1554-1558. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Khalsa SBS, Jewett ME, Cajochen C, Czeisler CA. A phase response curve to single bright light pulses in human subjects. J Physiol. 2003;549(3):945-952. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Minors DS, Waterhouse JM, Wirz-Justice A. A human phase-response curve to light. Neurosci Lett. 1991;133(1):36-40. [ CrossRef ] [ Medline ]
• Tucker P, Gilliland J. The effect of season and weather on physical activity: a systematic review. Public Health. 2007;121(12):909-922. [ CrossRef ] [ Medline ]
• Mattingly SM, Grover T, Martinez GJ, Aledavood T, Robles-Granda P, Nies K, et al. The effects of seasons and weather on sleep patterns measured through longitudinal multimodal sensing. NPJ Digit Med. 2021;4(1):76. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hamilton SL, Clemes SA, Griffiths PL. UK adults exhibit higher step counts in summer compared to winter months. Ann Hum Biol. 2008;35(2):154-169. [ CrossRef ] [ Medline ]
• Clemes SA, Hamilton SL, Griffiths PL. Summer to winter variability in the step counts of normal weight and overweight adults living in the UK. J Phys Act Health. 2011;8(1):36-44. [ CrossRef ] [ Medline ]
• Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, et al. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry. 2019;19(1):1-11. [ CrossRef ]
• Matcham F, Leightley D, Siddi S, Lamers F, White KM, Annas P, et al. RADAR-CNS consortium. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study. BMC Psychiatry. 2022;22(1):136. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ranjan Y, Rashid Z, Stewart C, Conde P, Begale M, Verbeeck D, Hyve, et al. RADAR-CNS Consortium. RADAR-Base: open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR Mhealth Uhealth. 2019;7(8):e11734. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1-3):163-173. [ CrossRef ] [ Medline ]
• Huang YL, Liu RY, Wang QS, Van Someren EJW, Xu H, Zhou JN. Age-associated difference in circadian sleep-wake and rest-activity rhythms. Physiol Behav. 2002;76(4-5):597-603. [ CrossRef ] [ Medline ]
• Duffy JF, Cain SW, Chang AM, Phillips AJK, Münch MY, Gronfier C, et al. Sex difference in the near-24-hour intrinsic period of the human circadian timing system. Proc Natl Acad Sci USA. 2011;108 Suppl 3(Suppl 3):15602-15608. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jones SH, Hare DJ, Evershed K. Actigraphic assessment of circadian activity and sleep patterns in bipolar disorder. Bipolar Disord. 2005;7(2):176-186. [ CrossRef ] [ Medline ]
• Sun S, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, et al. RADAR-CNS Consortium. Using smartphones and wearable devices to monitor behavioral changes during COVID-19. J Med Internet Res. 2020;22(9):e19992. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, et al. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. 2015;17(7):e175. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ. 2016;4:e2537. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Farhan AA, Yue C, Morillo R, Ware S, Lu J, Bi J, et al. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. IEEE; 2016. Presented at: 2016 IEEE Wireless Health (WH); October 25-27 2016; Bethesda, MD, USA. [ CrossRef ]
• Zhang Y, Folarin AA, Sun S, Cummins N, Bendayan R, Ranjan Y, et al. RADAR-CNS Consortium. Relationship between major depression symptom severity and sleep collected using a wristband wearable device: multicenter longitudinal observational study. JMIR Mhealth Uhealth. 2021;9(4):e24604. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, et al. Predicting depressive symptom severity through individuals' nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study. JMIR Mhealth Uhealth. 2021;9(7):e29840. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sun S, Folarin AA, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, et al. RADAR-CNS Consortium. Challenges in using mHealth data from smartphones and wearable devices to predict depression symptom severity: retrospective analysis. J Med Internet Res. 2023;25:e45233. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Harrison Y. The impact of daylight saving time on sleep and related behaviours. Sleep Med Rev. 2013;17(4):285-292. [ CrossRef ] [ Medline ]
• de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2™ compared with polysomnography in adults. Chronobiol Int. 2017;35(4):465-476. [ CrossRef ] [ Medline ]
• Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis. J Med Internet Res. 2019;21(11):e16273. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van Someren EJ, Hagebeuk EE, Lijzenga C, Scheltens P, de Rooij SE, Jonker C, et al. Circadian rest-activity rhythm disturbances in Alzheimer's disease. Biol Psychiatry. 1996;40(4):259-270. [ CrossRef ] [ Medline ]
• Van Someren EJW, Swaab DF, Colenda CC, Cohen W, McCall WV, Rosenquist PB. Bright light therapy: improved sensitivity to its effects on rest-activity rhythms in Alzheimer patients by application of nonparametric methods. Chronobiol Int. 1999;16(4):505-518. [ CrossRef ] [ Medline ]
• Cornelissen G. Cosinor-based rhythmometry. Theor Biol Med Model. 2014;11:16. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ali FZ, Parsey RV, Lin S, Schwartz J, DeLorenzo C. Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response. NPJ Digit Med. 2023;6(1):81. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Esaki Y, Obayashi K, Saeki K, Fujita K, Iwata N, Kitajima T. Association between circadian activity rhythms and mood episode relapse in bipolar disorder: a 12-month prospective cohort study. Transl Psychiatry. 2021;11(1):525. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38(4):963-974. [ Medline ]
• Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57(1):289-300. [ CrossRef ]
• Chen L, Ng E. Outdoor thermal comfort and outdoor activities: a review of research in the past decade. Cities. 2012;29(2):118-125. [ CrossRef ]
• Melo MCA, Daher EDF, Albuquerque SGC, de Bruin VMS. Exercise in bipolar patients: a systematic review. J Affect Disord. Jul 01, 2016;198:32-38. [ CrossRef ] [ Medline ]
• Melo MCA, Garcia RF, de Araújo CFC, Rangel DM, de Bruin PFC, de Bruin VMS. Physical activity as prognostic factor for bipolar disorder: an 18-month prospective study. J Affect Disord. May 15, 2019;251:100-106. [ CrossRef ] [ Medline ]
• Harvey SB, Øverland S, Hatch SL, Wessely S, Mykletun A, Hotopf M. Exercise and the prevention of depression: results of the HUNT Cohort Study. Am J Psychiatry. 2018;175(1):28-36. [ CrossRef ] [ Medline ]
• Cooney GM, Dwan K, Greig CA, Lawlor DA, Rimer J, Waugh FR, et al. Exercise for depression. Cochrane Database Syst Rev. 2013;2013(9):CD004366. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fang Y, Forger DB, Frank E, Sen S, Goldstein C. Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians. NPJ Digit Med. Feb 18, 2021;4(1):28. [ CrossRef ] [ Medline ]
• Ghandeharioun A, Fedor S, Sangermano L, Ionescu D, Alpert J, Dale C, et al. Objective assessment of depressive symptoms with machine learning and wearable sensors data. 2017. Presented at: International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII; October 23-26, 2017; San Antonio, TX. [ CrossRef ]
• Matcham F, Carr E, Meyer N, White KM. Wearable-derived sleep features predict relapse in major depressive disorder. ResearchGate. Mar 2023. URL: https:/​/www.​researchgate.net/​publication/​369514383_Wearable-derived_sleep_features_predict_relapse_in_Major_Depressive_Disorder [accessed 2024-05-22]
• Luik AI, Zuurbier LA, Direk N, Hofman A, Van Someren EJ, Tiemeier H. 24-hour activity rhythm and sleep disturbances in depression and anxiety: a population-based study of middle-aged and older persons. Depress Anxiety. 2015;32(9):684-692. [ CrossRef ] [ Medline ]
• Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Bendayan R, et al. RADAR-CNS consortium. Longitudinal relationships between depressive symptom severity and phone-measured mobility: dynamic structural equation modeling study. JMIR Ment Health. 2022;9(3):e34898. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Otsuka K, Mitsutake G, Yano S. Depression, quality of life, and lifestyle: chronoecological health watch in a community. Biomed Pharmacother. 2002;56 Suppl 2:231s-242s. [ CrossRef ] [ Medline ]
• Monsivais D, Ghosh A, Bhattacharya K, Dunbar RIM, Kaski K. Tracking urban human activity from mobile phone calling patterns. PLoS Comput Biol. 2017;13(11):e1005824. [ FREE Full text ] [ CrossRef ] [ Medline ]
• de la Iglesia HO, Fernández-Duque E, Golombek DA, Lanza N, Duffy JF, Czeisler CA, et al. Access to electric light is associated with shorter sleep duration in a Traditionally Hunter-Gatherer Community. J Biol Rhythms. 2015;30(4):342-350. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Yetish G, Kaplan H, Gurven M, Wood B, Pontzer H, Manger PR, et al. Natural sleep and its seasonal variations in three pre-industrial societies. Curr Biol. 2015;25(21):2862-2868. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cepeda M, Koolhaas CM, van Rooij FJA, Tiemeier H, Guxens M, Franco OH, et al. Seasonality of physical activity, sedentary behavior, and sleep in a middle-aged and elderly population: The Rotterdam study. Maturitas. 2018;110:41-50. [ CrossRef ] [ Medline ]
• Kume Y, Makabe S, Singha-Dong N, Vajamun P, Apikomonkon H, Griffiths J. Seasonal effects on the sleep-wake cycle, the rest-activity rhythm and quality of life for Japanese and Thai older people. Chronobiol Int. 2017;34(10):1377-1387. [ CrossRef ] [ Medline ]
• Smagula SF, DuPont CM, Miller MA, Krafty RT, Hasler BP, Franzen PL, et al. Rest-activity rhythms characteristics and seasonal changes in seasonal affective disorder. Chronobiol Int. 2018;35(11):1553-1559. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kanikowska D, Roszak M, Rutkowski R, Sato M, Sikorska D, Orzechowska Z, et al. Seasonal differences in rhythmicity of salivary cortisol in healthy adults. J Appl Physiol (1985). 2019;126(3):764-770. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hadlow NC, Brown S, Wardrop R, Henley D. The effects of season, daylight saving and time of sunrise on serum cortisol in a large population. Chronobiol Int. 2013;31(2):243-251. [ CrossRef ] [ Medline ]
• Zhang Y, Pratap A, Folarin AA, Sun S, Cummins N, Matcham F, et al. RADAR-CNS consortium. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. NPJ Digit Med. 2023;6(1):25. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T de Azevedo Cardoso, S Ma; submitted 08.12.23; peer-reviewed by A Tacchino, JC Shin, D Singh; comments to author 13.02.24; revised version received 22.02.24; accepted 29.03.24; published 28.06.24.

©Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Yatharth Ranjan, Zulqarnain Rashid, Callum Stewart, Pauline Conde, Heet Sankesara, Petroula Laiou, Faith Matcham, Katie M White, Carolin Oetzmann, Femke Lamers, Sara Siddi, Sara Simblett, Srinivasan Vairavan, Inez Myin-Germeys, David C Mohr, Til Wykes, Josep Maria Haro, Peter Annas, Brenda WJH Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard JB Dobson, RADAR-CNS consortium. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.