independent variable research meaning

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)

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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.

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Definitions

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

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

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

Identifying Dependent and Independent Variables

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

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

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

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

Structure and Writing Style

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

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

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

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

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

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

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Research Variables 101

Independent variables, dependent variables, control variables and more

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

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

Overview: Variables In Research

What (exactly) is a variable.

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

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

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

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

The “Big 3” Variables

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

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

What is an independent variable?

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

For example:

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

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

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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 ...

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What is an Independent Variable? Importance and Examples

Table of contents

What are independent variables, the significance of independent variables understanding independent variables is essential for several reasons:, examples of independent variables let’s explore a few examples of independent variables across different fields:.

In the realm of scientific research, understanding the relationship between variables is crucial. Independent variables play a fundamental role in experimental design and hypothesis testing. In this article, iLovePhD will delve into the concept of independent variables, explore their significance, and provide relevant examples to facilitate a clear understanding.

• The Significance of Independent Variables
• Examples of Independent Variables

Learn what independent variables are and why they are important in scientific research. Discover real-life examples that illustrate their role in experimental design and hypothesis testing. Gain a clear understanding of how manipulating independent variables can lead to meaningful conclusions.

Demystifying Independent Variables: Meaning, Importance, and Examples

The independent variable is a key component in scientific experiments. It refers to the factor or condition that researchers manipulate or change to observe its effect on the dependent variable. In other words, the independent variable is the cause, while the dependent variable is the effect being measured.

For example, in a study investigating the impact of sleep duration on cognitive performance, the independent variable would be the sleep duration. Researchers would manipulate the independent variable by assigning different groups of participants to various sleep durations, such as six, eight, or ten hours.

A. Control and Causality: By manipulating the independent variable, researchers can exercise control over the experiment and establish a cause-and-effect relationship between variables. This control helps eliminate confounding factors and ensures that any observed effects can be attributed to the independent variable.

B. Replicability: Independent variables are crucial for replicating experiments. When researchers manipulate the same independent variable in multiple experiments, they can examine whether the effects remain consistent. This process strengthens the validity and reliability of scientific findings.

C. Generalization: Independent variables aid in making generalizations about a broader population. By manipulating the independent variable, researchers can study how certain conditions or factors affect a range of individuals or objects, enabling broader insights into various phenomena.

A. Biology: In a study investigating the effect of fertilizer on plant growth, the independent variable would be the amount of fertilizer applied. Researchers would manipulate this variable by exposing different groups of plants to varying levels of fertilizer concentration.

B. Psychology: To explore the impact of music on mood, researchers may manipulate the independent variable by exposing participants to different genres of music (classical, rock, jazz) and measuring their mood changes using a standardized mood scale.

C. Physics: In an experiment studying the relationship between distance and time for an object in free fall, the independent variable would be the distance. Researchers would manipulate this variable by dropping the object from different heights and measuring the corresponding time it takes to fall.

Independent variables serve as a crucial component of experimental design, allowing researchers to investigate cause-and-effect relationships between variables. By manipulating the independent variable and observing its effects on the dependent variable, researchers can draw meaningful conclusions.

Understanding the role and significance of independent variables is vital for conducting rigorous scientific research and obtaining reliable results.

Also Read: Types of Research Variable in Research with Example

• cause and effect relationship
• control variables
• Data Analysis
• dependent variables
• experimental design
• hypothesis testing
• independent variables
• manipulating variables
• research conclusions
• research design
• research experiment
• research findings
• Research Methodology
• research reliability
• research validity
• research variables
• scientific research
• statistical analysis

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• Independent vs Dependent Variables | Definition & Examples

Independent vs Dependent Variables | Definition & Examples

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

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

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

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

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

Table of contents

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

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

Independent variables are also called:

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

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

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.

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Bhandari, P. (2022, October 17). Independent vs Dependent Variables | Definition & Examples. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/research-methods/independent-vs-dependent-variables/

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What Are Independent and Dependent Variables?

Recognize and Graph Independent and Dependent Variables

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

Independent Variable

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

Dependent Variable

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

Independent and Dependent Variable Examples

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

How to Tell the Independent and Dependent Variable Apart

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

Independent variable causes an effect on the dependent variable.

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

This makes sense, but:

Example : Your test score affects how long you sleep.

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

How to Plot Variables on a Graph

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

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

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

Test your understanding with the scientific method quiz .

• Difference Between Independent and Dependent Variables
• Dependent Variable Definition and Examples
• Independent Variable Definition and Examples
• What Is a Dependent Variable?
• Scientific Variable
• DRY MIX Experiment Variables Acronym
• What Is a Variable in Science?
• 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)
• How To Design a Science Fair Experiment
• What Are the Elements of a Good Hypothesis?
• Scientific Method Vocabulary Terms
• The Role of a Controlled Variable in an Experiment

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

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

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variable. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

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

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Roles of Independent and Dependent Variables in Research

Explore the essential roles of independent and dependent variables in research. This guide delves into their definitions, significance in experiments, and their critical relationship. Learn how these variables are the foundation of research design, influencing hypothesis testing, theory development, and statistical analysis, empowering researchers to understand and predict outcomes of research studies.

Table of Contents

Introduction.

At the very base of scientific inquiry and research design , variables act as the fundamental steps, guiding the rhythm and direction of research. This is particularly true in human behavior research, where the quest to understand the complexities of human actions and reactions hinges on the meticulous manipulation and observation of these variables. At the heart of this endeavor lie two different types of variables, namely: independent and dependent variables, whose roles and interplay are critical in scientific discovery.

Understanding the distinction between independent and dependent variables is not merely an academic exercise; it is essential for anyone venturing into the field of research. This article aims to demystify these concepts, offering clarity on their definitions, roles, and the nuances of their relationship in the study of human behavior, and in science generally. We will cover hypothesis testing and theory development, illuminating how these variables serve as the cornerstone of experimental design and statistical analysis.

The significance of grasping the difference between independent and dependent variables extends beyond the confines of academia. It empowers researchers to design robust studies, enables critical evaluation of research findings, and fosters an appreciation for the complexity of human behavior research. As we delve into this exploration, our objective is clear: to equip readers with a deep understanding of these fundamental concepts, enhancing their ability to contribute to the ever-evolving field of human behavior research.

Chapter 1: The Role of Independent Variables in Human Behavior Research

In the realm of human behavior research, independent variables are the keystones around which studies are designed and hypotheses are tested. Independent variables are the factors or conditions that researchers manipulate or observe to examine their effects on dependent variables, which typically reflect aspects of human behavior or psychological phenomena. Understanding the role of independent variables is crucial for designing robust research methodologies, ensuring the reliability and validity of findings.

Defining Independent Variables

Independent variables are those variables that are changed or controlled in a scientific experiment to test the effects on dependent variables. In studies focusing on human behavior, these can range from psychological interventions (e.g., cognitive-behavioral therapy), environmental adjustments (e.g., noise levels, lighting, smells, etc), to societal factors (e.g., social media use). For example, in an experiment investigating the impact of sleep on cognitive performance, the amount of sleep participants receive is the independent variable. 

Selection and Manipulation

Selecting an independent variable requires careful consideration of the research question and the theoretical framework guiding the study. Researchers must ensure that their chosen variable can be effectively, and consistently manipulated or measured and is ethically and practically feasible, particularly when dealing with human subjects.

Manipulating an independent variable involves creating different conditions (e.g., treatment vs. control groups) to observe how changes in the variable affect outcomes. For instance, researchers studying the effect of educational interventions on learning outcomes might vary the type of instructional material (digital vs. traditional) to assess differences in student performance.

Challenges in Human Behavior Research

Manipulating independent variables in human behavior research presents unique challenges. Ethical considerations are paramount, as interventions must not harm participants. For example, studies involving vulnerable populations or sensitive topics require rigorous ethical oversight to ensure that the manipulation of independent variables does not result in adverse effects.

Practical limitations also come into play, such as controlling for extraneous variables that could influence the outcomes. In the aforementioned example of sleep and cognitive performance, factors like caffeine consumption or stress levels could confound the results. Researchers employ various methodological strategies, such as random assignment and controlled environments, to mitigate these influences.

Chapter 2: Dependent Variables: Measuring Human Behavior

The dependent variable in human behavior research acts as a mirror, reflecting the outcomes or effects resulting from variations in the independent variable. It is the aspect of human experience or behavior that researchers aim to understand, predict, or change through their studies. This section explores how dependent variables are measured, the significance of their accurate measurement, and the inherent challenges in capturing the complexities of human behavior.

Defining Dependent Variables

Dependent variables are the responses or outcomes that researchers measure in an experiment, expecting them to vary as a direct result of changes in the independent variable. In the context of human behavior research, dependent variables could include measures of emotional well-being, cognitive performance, social interactions, or any other aspect of human behavior influenced by the experimental manipulation. For instance, in a study examining the effect of exercise on stress levels, stress level would be the dependent variable, measured through various psychological assessments or physiological markers.

Measurement Methods and Tools

Measuring dependent variables in human behavior research involves a diverse array of methodologies, ranging from self-reported questionnaires and interviews to physiological measurements and behavioral observations. The choice of measurement tool depends on the nature of the dependent variable and the objectives of the study.

• Self-reported Measures: Often used for assessing psychological states or subjective experiences, such as anxiety, satisfaction, or mood. These measures rely on participants’ introspection and honesty, posing challenges in terms of accuracy and bias.
• Behavioral Observations: Involve the direct observation and recording of participants’ behavior in natural or controlled settings. This method is used for behaviors that can be externally observed and quantified, such as social interactions or task performance.
• Physiological Measurements: Include the use of technology to measure physical responses that indicate psychological states, such as heart rate, cortisol levels, or brain activity. These measures can provide objective data about the physiological aspects of human behavior.

Reliability and Validity

The reliability and validity of the measurement of dependent variables are critical to the integrity of human behavior research.

• Reliability refers to the consistency of a measure; a reliable tool yields similar results under consistent conditions.
• Validity pertains to the accuracy of the measure; a valid tool accurately reflects the concept it aims to measure.

Ensuring reliability and validity often involves the use of established measurement instruments with proven track records, pilot testing new instruments, and applying rigorous statistical analyses to evaluate measurement properties.

Challenges in Measuring Human Behavior

Measuring human behavior presents challenges due to its complexity and the influence of multiple, often interrelated, variables. Researchers must contend with issues such as participant bias, environmental influences, and the subjective nature of many psychological constructs. Additionally, the dynamic nature of human behavior means that it can change over time, necessitating careful consideration of when and how measurements are taken.

Section 3: Relationship between Independent and Dependent Variables

Understanding the relationship between independent and dependent variables is at the core of research in human behavior. This relationship is what researchers aim to elucidate, whether they seek to explain, predict, or influence human actions and psychological states. This section explores the nature of this relationship, the means by which it is analyzed, and common misconceptions that may arise.

The Nature of the Relationship

The relationship between independent and dependent variables can manifest in various forms—direct, indirect, linear, nonlinear, and may be moderated or mediated by other variables. At its most basic, this relationship is often conceptualized as cause and effect: the independent variable (the cause) influences the dependent variable (the effect). For instance, increased physical activity (independent variable) may lead to decreased stress levels (dependent variable).

Analyzing the Relationship

Statistical analyses play a pivotal role in examining the relationship between independent and dependent variables. Techniques vary depending on the nature of the variables and the research design, ranging from simple correlation and regression analyses for quantifying the strength and form of relationships, to complex multivariate analyses for exploring relationships among multiple variables simultaneously.

• Correlation Analysis : Used to determine the degree to which two variables are related. However, it’s crucial to note that correlation does not imply causation.
• Regression Analysis : Goes a step further by not only assessing the strength of the relationship but also predicting the value of the dependent variable based on the independent variable.
• Experimental Design : Provides a more robust framework for inferring causality, where manipulation of the independent variable and control of confounding factors allow researchers to directly observe the impact on the dependent variable.

Causality vs. Correlation

A fundamental consideration in human behavior research is the distinction between causality and correlation. Causality implies that changes in the independent variable cause changes in the dependent variable. Correlation, on the other hand, indicates that two variables are related but does not establish a cause-effect relationship. Confounding variables may influence both, creating the appearance of a direct relationship where none exists. Understanding this distinction is crucial for accurate interpretation of research findings.

Common Misinterpretations

The complexity of human behavior and the myriad factors that influence it often lead to challenges in interpreting the relationship between independent and dependent variables. Researchers must be wary of:

• Overestimating the strength of causal relationships based on correlational data.
• Ignoring potential confounding variables that may influence the observed relationship.
• Assuming the directionality of the relationship without adequate evidence.

This exploration highlights the importance of understanding independent and dependent variables in human behavior research. Independent variables act as the initiating factors in experiments, influencing the observed behaviors, while dependent variables reflect the results of these influences, providing insights into human emotions and actions. 

Ethical and practical challenges arise, especially in experiments involving human participants, necessitating careful consideration to respect participants’ well-being. The measurement of these variables is critical for testing theories and validating hypotheses, with their relationship offering potential insights into causality and correlation within human behavior. 

Rigorous statistical analysis and cautious interpretation of findings are essential to avoid misconceptions. Overall, the study of these variables is fundamental to advancing human behavior research, guiding researchers towards deeper understanding and potential interventions to improve the human condition.

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What Is an Independent Variable in Quantitative Research?

What Are the Independent Variables for a Moldy Bread Experiment?

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

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

How it Works

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

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

Controlled Variable Importance

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

Status Variable

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

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

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A Student’s Guide to the Classification and Operationalization of Variables in the Conceptualization and Design of a Clinical Study: Part 1

Chittaranjan andrade.

1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

Students without prior research experience may not know how to conceptualize and design a study. This article explains how an understanding of the classification and operationalization of variables is the key to the process. Variables describe aspects of the sample that is under study; they are so called because they vary in value from subject to subject in the sample. Variables may be independent or dependent. Independent variables influence the value of other variables; dependent variables are influenced in value by other variables. A hypothesis states an expected relationship between variables. A significant relationship between an independent and dependent variable does not prove cause and effect; the relationship may partly or wholly be explained by one or more confounding variables. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. These and other concepts are explained with the help of clinically relevant examples.

Key Message:

This article explains the following concepts: Independent variables, dependent variables, confounding variables, operationalization of variables, and construction of hypotheses.

In any body of research, the subject of study requires to be described and understood. For example, if we wish to study predictors of response to antidepressant drugs (ADs) in patients with major depressive disorder (MDD), we might select patient age, sex, age at onset of MDD, number of previous episodes of depression, duration of current depressive episode, presence of psychotic symptoms, past history of response to ADs, and other patient and illness characteristics as potential predictors. These characteristics or descriptors are called variables. Whether or not the patient responds to AD treatment is also a variable. A solid understanding of variables is the cornerstone in the conceptualization and preparation of a research protocol, and in the framing of study hypotheses. This subject is presented in two parts. This article, Part 1, explains what independent and dependent variables are, how an understanding of these is important in framing hypotheses, and what operationalization of a variable entails.

Variables are defined as characteristics of the sample that are examined, measured, described, and interpreted. Variables are so called because they vary in value from subject to subject in the study. As an example, if we wish to examine the relationship between age and height in a sample of children, age and height are the variables of interest; their values vary from child to child. In the earlier example, patients vary in age, sex, duration of current depressive episode, and response to ADs. Variables are classified as dependent and independent variables and are usually analyzed as categorical or continuous variables.

Independent and Dependent Variables

Independent variables are defined as those the values of which influence other variables. For example, age, sex, current smoking, LDL cholesterol level, and blood pressure are independent variables because their values (e.g., greater age, positive for current smoking, and higher LDL cholesterol level) influence the risk of myocardial infarction. Dependent variables are defined as those the values of which are influenced by other variables. For example, the risk of myocardial infarction is a dependent variable the value of which is influenced by variables such as age, sex, current smoking, LDL cholesterol level, and blood pressure. The risk is higher in older persons, in men, in current smokers, and so on.

There may be a cause–effect relationship between independent and dependent variables. For example, consider a clinical trial with treatment (iron supplement vs placebo) as the independent variable and hemoglobin level as the dependent variable. In children with anemia, an iron supplement will raise the hemoglobin level to a greater extent than will placebo; this is a cause–effect relationship because iron is necessary for the synthesis of hemoglobin. However, consider the variables teeth and weight . An alien from outer space who has no knowledge of human physiology may study human children below the age of 5 years and find that, as the number of teeth increases, weight increases. Should the alien conclude that there is a cause–effect relationship here, and that growing teeth causes weight gain? No, because a third variable, age, is a confounding variable 1 – 3 that is responsible for both increase in the number of teeth and increase in weight. In general, therefore, it is more proper to state that independent variables are associated with variations in the values of the dependent variables rather than state that independent variables cause variations in the values of the dependent variables. For causality to be asserted, other criteria must be fulfilled; this is out of the scope of the present article, and interested readers may refer to Schunemann et al. 4

As a side note, here, whether a particular variable is independent or dependent will depend on the question that is being asked. For example, in a study of factors influencing patient satisfaction with outpatient department (OPD) services, patient satisfaction is the dependent variable. But, in a study of factors influencing OPD attendance at a hospital, OPD attendance is the dependent variable, and patient satisfaction is merely one of many possible independent variables that can influence OPD attendance.

Importance of Variables in Stating the Research Objectives

Students must have a clear idea about what they want to study in order to conceptualize and frame a research protocol. The first matters that they need to address are “What are my research questions?” and “What are my hypotheses?” Both questions can be answered only after choosing the dependent variables and then the independent variables for study.

In the case of a student who is interested in studying predictors of AD outcomes in patients with MDD, treatment response is the dependent variable and patient and clinical characteristics are possible independent variables. So, the selection of dependent and independent variables helps defines the objectives of the study:

• To determine whether sociodemographic variables, such as age and sex, predict the outcome of an episode of depression in MDD patients who are treated with an AD.
• To determine whether clinical variables, such as age at onset of depression, number of previous depressive episodes, duration of current depressive episode, and the presence of soft neurological signs, predict the outcome of an episode of depression in MDD patients who are treated with an AD.

Note that in a formal research protocol, the student will need to state all the independent variables and not merely list examples. The student may also choose to include additional independent variables, such as baseline biochemical, psychophysiological, and neuroradiological measures.

Importance of Variables in Framing Hypotheses

A hypothesis is a clear statement of what the researcher expects to find in the study. As an example, a researcher may hypothesize that longer duration of current depression is associated with poorer response to ADs. In this hypothesis, the duration of the current episode of depression is the independent variable and treatment response is the dependent variable. It should be obvious, now, that a hypothesis can also be defined as the statement of an expected relationship between an independent and a dependent variable . Or, expressed visually, (independent variable) (arrow) (dependent variable) = hypothesis.

It would be a waste of time and energy to do a study to examine only one question: whether duration of current depression predicts treatment response. So, it is usual for research protocols to include many independent variables and many dependent variables in the generation of many hypotheses, as shown in Table 1 . Pairing each variable in the “independent variable” column with each variable in the “dependent variable” column would result in the generation of these hypotheses. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1 . Importantly, the student must select one of these hypotheses as the primary hypothesis; the remaining hypotheses, no matter how many they are, would be secondary hypotheses. It is necessary to have only one hypothesis as the primary hypothesis in order to calculate the sample size necessary for an adequately powered study and to reduce the risk of false positive findings in the analysis. 5 In rare situations, two hypotheses may be considered equally important and may be stated as coprimary hypotheses.

Independent Variables and Dependent Variables in a Study on Sociodemographic and Clinical Prediction of Response of Major Depressive Disorder to Antidepressant Drug Treatment

Combinations of Age with Dependent Variables in the Generation of Hypotheses

Operationalization of Variables

In Table 1 , suicidality is listed as an independent variable and severity of depression, as a dependent variable. These variables need to be operationalized; that is, stated in a way that explains how they will be measured. Table 3 presents three ways in which suicidality can be measured and four ways in which (reduction in) the severity of depression can be measured. Now, each way of measurement in the “independent variable” column can be paired with a way of measurement in the “dependent variable” column, making a total of 12 possible hypotheses. In like manner, the many variables listed in Table 1 can each be operationalized in several different ways, resulting in the generation of a very large number of hypotheses. As already stated, the student must select only one hypothesis as the primary hypothesis.

Possible Ways of Operationalization of Suicidality and Depression

HAM-D: Hamilton Depression Rating Scale, MADRS: Montgomery–Asberg Depression Rating Scale.

Much thought should be given to the operationalization of variables because variables that are carelessly operationalized will be poorly measured; the data collected will then be of poor quality, and the study will yield unreliable results. For example, socioeconomic status may be operationalized as lower, middle, or upper class, depending on the patient’s monthly income, on the total monthly income of the family, or using a validated socioeconomic status assessment scale that takes into consideration income, education, occupation, and place of residence. The student must choose the method that would best suit the needs of the study, and the method that has the greatest scientific acceptability. However, it is also permissible to operationalize the same variable in many different ways and to include all these different operationalizations in the study, as shown in Table 3 . This is because conceptualizing variables in different ways can help understand the subject of the study in different ways.

Operationalization of variables requires a consideration of the reliability and validity of the method of operationalization; discussions on reliability and validity are out of the scope of this article. Operationalization of variables also requires specification of the scale of measurement: nominal, ordinal, interval, or ratio; this is also out of the scope of the present article. Finally, operationalization of variables can also specify details of the measurement procedure. As an example, in a study on the use of metformin to reduce olanzapine-associated weight gain, we may state that we will obtain the weight of the patient but fail to explain how we will do it. Better would be to state that the same weighing scale will be used. Still better would be to state that we will use a weighing instrument that works on the principle of moving weights on a levered arm, and that the same instrument will be used for all patients. And best would be to add that we will weigh patients, dressed in standard hospital gowns, after they have voided their bladder but before they have eaten breakfast. When the way in which a variable will be measured is defined, measurement of that variable becomes more objective and uniform

Concluding Notes

The next article, Part 2, will address what categorical and continuous variables are, why continuous variables should not be converted into categorical variables and when this rule can be broken, and what confounding variables are.

Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author received no financial support for the research, authorship, and/or publication of this article.