2. variables
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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:
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…
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:
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:
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.
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:
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.
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:
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!
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.
Let’s jump into it…
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.
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.
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:
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.
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:
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!
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:
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 .
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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Home » Dependent Variable – Definition, Types and Example
Table of Contents
Definition:
Dependent variable is a variable in a study or experiment that is being measured or observed and is affected by the independent variable. In other words, it is the variable that researchers are interested in understanding, predicting, or explaining based on the changes made to the independent variable.
Types of Dependent Variables are as follows:
Here are some examples of dependent variables in different fields:
The purpose of the dependent variable is to help researchers understand the relationship between the independent variable and the outcome they are studying. By measuring the changes in the dependent variable, researchers can determine the effects of different variables on the outcome of interest.
Following are some situations When to use Dependent Variable:
Some Characteristics of Dependent Variable are as follows:
Some Advantages of Dependent Variable are as follows:
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Dave Cornell (PhD)
Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.
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Chris Drew (PhD)
This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.
An independent variable (IV) is what is manipulated in a scientific experiment to determine its effect on the dependent variable (DV).
By varying the level of the independent variable and observing associated changes in the dependent variable, a researcher can conclude whether the independent variable affects the dependent variable or not.
This can provide very valuable information when studying just about any subject.
Because the researcher controls the level of the independent variable, it can be determined if the independent variable has a causal effect on the dependent variable.
The term causation is vitally important. Scientists want to know what causes changes in the dependent variable. The only way to do that is to manipulate the independent variable and observe any changes in the dependent variable.
The independent variable and dependent variable are used in a very specific type of scientific study called the experiment .
Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.
The research participants are randomly assigned to either receive the independent variable (called the treatment condition), or not receive the independent variable (called the control condition).
Other variations of an experiment might include having multiple levels of the independent variable.
If the independent variable affects the dependent variable, then it should be possible to observe changes in the dependent variable based on the presence or absence of the independent variable.
Of course, there are a lot of issues to consider when conducting an experiment, but these are the basic principles.
These concepts should not be confused with predictor and outcome variables .
1. gatorade and improved athletic performance.
A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.
If they can back up that claim with hard scientific data, that would be great for sales.
So, the researcher goes to a nearby university and randomly selects both male and female athletes from several sports: track and field, volleyball, basketball, and football. Each athlete will run on a treadmill for one hour while their heart rate is tracked.
All of the athletes are given the exact same amount of liquid to consume 30-minutes before and during their run. Half are given Gatorade, and the other half are given water, but no one knows what they are given because both liquids have been colored.
In this example, the independent variable is Gatorade, and the dependent variable is heart rate.
A hospital is investigating the effectiveness of a new type of chemotherapy on cancer. The researchers identified 120 patients with relatively similar types of cancerous tumors in both size and stage of progression.
The patients are randomly assigned to one of three groups: one group receives no chemotherapy, one group receives a low dose of chemotherapy, and one group receives a high dose of chemotherapy.
Each group receives chemotherapy treatment three times a week for two months, except for the no-treatment group. At the end of two months, the doctors measure the size of each patient’s tumor.
In this study, despite the ethical issues (remember this is just a hypothetical example), the independent variable is chemotherapy, and the dependent variable is tumor size.
A well-known fast-food corporation wants to know if the color of the interior of their restaurants will affect how fast people eat. Of course, they would prefer that consumers enter and exit quickly to increase sales volume and profit.
So, they rent space in a large shopping mall and create three different simulated restaurant interiors of different colors. One room is painted mostly white with red trim and seats; one room is painted mostly white with blue trim and seats; and one room is painted mostly white with off-white trim and seats.
Next, they randomly select shoppers on Saturdays and Sundays to eat for free in one of the three rooms. Each shopper is given a box of the same food and drink items and sent to one of the rooms. The researchers record how much time elapses from the moment they enter the room to the moment they leave.
The independent variable is the color of the room, and the dependent variable is the amount of time spent in the room eating.
A large multinational cosmetics company wants to know if the color of a woman’s hair affects the level of perceived attractiveness in males. So, they use Photoshop to manipulate the same image of a female by altering the color of her hair: blonde, brunette, red, and brown.
Next, they randomly select university males to enter their testing facilities. Each participant sits in front of a computer screen and responds to questions on a survey. At the end of the survey, the screen shows one of the photos of the female.
At the same time, software on the computer that utilizes the computer’s camera is measuring each male’s pupil dilation. The researchers believe that larger dilation indicates greater perceived attractiveness.
The independent variable is hair color, and the dependent variable is pupil dilation.
After many claims that listening to Mozart will make you smarter, a group of education specialists decides to put it to the test. So, first, they go to a nearby school in a middle-class neighborhood.
During the first three months of the academic year, they randomly select some 5th-grade classrooms to listen to Mozart during their lessons and exams. Other 5 th grade classrooms will not listen to any music during their lessons and exams.
The researchers then compare the scores of the exams between the two groups of classrooms.
Although there are a lot of obvious limitations to this hypothetical, it is the first step.
The independent variable is Mozart, and the dependent variable is exam scores.
A company that specializes in essential oils wants to examine the effects of lavender on sleep quality. They hire a sleep research lab to conduct the study. The researchers at the lab have their usual test volunteers sleep in individual rooms every night for one week.
The conditions of each room are all exactly the same, except that half of the rooms have lavender released into the rooms and half do not. While the study participants are sleeping, their heart rates and amount of time spent in deep sleep are recorded with high-tech equipment.
At the end of the study, the researchers compare the total amount of time spent in deep sleep of the lavender-room participants with the no lavender-room participants.
The independent variable in this sleep study is lavender, and the dependent variable is the total amount of time spent in deep sleep.
A group of teachers is interested in which teaching method will work best for developing critical thinking skills.
So, they train a group of teachers in three different teaching styles : teacher-centered, where the teacher tells the students all about critical thinking; student-centered, where the students practice critical thinking and receive teacher feedback; and AI-assisted teaching, where the teacher uses a special software program to teach critical thinking.
At the end of three months, all the students take the same test that assesses critical thinking skills. The teachers then compare the scores of each of the three groups of students.
The independent variable is the teaching method, and the dependent variable is performance on the critical thinking test.
A chemicals company has developed three different versions of their concrete mix. Each version contains a different blend of specially developed chemicals. The company wants to know which version is the strongest.
So, they create three bridge molds that are identical in every way. They fill each mold with one of the different concrete mixtures. Next, they test the strength of each bridge by placing progressively more weight on its center until the bridge collapses.
In this study, the independent variable is the concrete mixture, and the dependent variable is the amount of weight at collapse.
People in the pizza business know that the crust is key. Many companies, large and small, will keep their recipe a top secret. Before rolling out a new type of crust, the company decides to conduct some research on consumer preferences.
The company has prepared three versions of their crust that vary in crunchiness, they are: a little crunchy, very crunchy, and super crunchy. They already have a pool of consumers that fit their customer profile and they often use them for testing.
Each participant sits in a booth and takes a bite of one version of the crust. They then indicate how much they liked it by pressing one of 5 buttons: didn’t like at all, liked, somewhat liked, liked very much, loved it.
The independent variable is the level of crust crunchiness, and the dependent variable is how much it was liked.
A large food company is considering entering the health and nutrition sector. Their R&D food scientists have developed a protein supplement that is designed to help build muscle mass for people that work out regularly.
The company approaches several gyms near its headquarters. They enlist the cooperation of over 120 gym rats that work out 5 days a week. Their muscle mass is measured, and only those with a lower level are selected for the study, leaving a total of 80 study participants.
They randomly assign half of the participants to take the recommended dosage of their supplement every day for three months after each workout. The other half takes the same amount of something that looks the same but actually does nothing to the body.
At the end of three months, the muscle mass of all participants is measured.
The independent variable is the supplement, and the dependent variable is muscle mass.
In the early days of airbags , automobile companies conducted a great deal of testing. At first, many people in the industry didn’t think airbags would be effective at all. Fortunately, there was a way to test this theory objectively.
In a representative example: Several crash cars were outfitted with an airbag, and an equal number were not. All crash cars were of the same make, year, and model. Then the crash experts rammed each car into a crash wall at the same speed. Sensors on the crash dummy skulls allowed for a scientific analysis of how much damage a human skull would incur.
The amount of skull damage of dummies in cars with airbags was then compared with those without airbags.
The independent variable was the airbag and the dependent variable was the amount of skull damage.
Some people take vitamins every day. A group of health scientists decides to conduct a study to determine if taking vitamins improves health.
They randomly select 1,000 people that are relatively similar in terms of their physical health. The key word here is “similar.”
Because the scientists have an unlimited budget (and because this is a hypothetical example, all of the participants have the same meals delivered to their homes (breakfast, lunch, and dinner), every day for one year.
In addition, the scientists randomly assign half of the participants to take a set of vitamins, supplied by the researchers every day for 1 year. The other half do not take the vitamins.
At the end of one year, the health of all participants is assessed, using blood pressure and cholesterol level as the key measurements.
In this highly unrealistic study, the independent variable is vitamins, and the dependent variable is health, as measured by blood pressure and cholesterol levels.
Does practicing meditation reduce stress? If you have ever wondered if this is true or not, then you are in luck because there is a way to know one way or the other.
All we have to do is find 90 people that are similar in age, stress levels, diet and exercise, and as many other factors as we can think of.
Next, we randomly assign each person to either practice meditation every day, three days a week, or not at all. After three months, we measure the stress levels of each person and compare the groups.
How should we measure stress? Well, there are a lot of ways. We could measure blood pressure, or the amount of the stress hormone cortisol in their blood, or by using a paper and pencil measure such as a questionnaire that asks them how much stress they feel.
In this study, the independent variable is meditation and the dependent variable is the amount of stress (however it is measured).
When video games started to become increasingly graphic, it was a huge concern in many countries in the world. Educators, social scientists, and parents were shocked at how graphic games were becoming.
Since then, there have been hundreds of studies conducted by psychologists and other researchers. A lot of those studies used an experimental design that involved males of various ages randomly assigned to play a graphic or non-graphic video game.
Afterward, their level of aggression was measured via a wide range of methods, including direct observations of their behavior, their actions when given the opportunity to be aggressive, or a variety of other measures.
So many studies have used so many different ways of measuring aggression.
In these experimental studies, the independent variable was graphic video games, and the dependent variable was observed level of aggression.
Car pollution is a concern for a lot of reasons. In addition to being bad for the environment, car exhaust may cause damage to the brain and impair cognitive performance.
One way to examine this possibility would be to conduct an animal study. The research would look something like this: laboratory rats would be raised in three different rooms that varied in the degree of car exhaust circulating in the room: no exhaust, little exhaust, or a lot of exhaust.
After a certain period of time, perhaps several months, the effects on cognitive performance could be measured.
One common way of assessing cognitive performance in laboratory rats is by measuring the amount of time it takes to run a maze successfully. It would also be possible to examine the physical effects of car exhaust on the brain by conducting an autopsy.
In this animal study, the independent variable would be car exhaust and the dependent variable would be amount of time to run a maze.
Read Next: Extraneous Variables Examples
The experiment is an incredibly valuable way to answer scientific questions regarding the cause and effect of certain variables. By manipulating the level of an independent variable and observing corresponding changes in a dependent variable, scientists can gain an understanding of many phenomena.
For example, scientists can learn if graphic video games make people more aggressive, if mediation reduces stress, if Gatorade improves athletic performance, and even if certain medical treatments can cure cancer.
The determination of causality is the key benefit of manipulating the independent variable and them observing changes in the dependent variable. Other research methodologies can reveal factors that are related to the dependent variable or associated with the dependent variable, but only when the independent variable is controlled by the researcher can causality be determined.
Ferguson, C. J. (2010). Blazing Angels or Resident Evil? Can graphic video games be a force for good? Review of General Psychology, 14 (2), 68-81. https://doi.org/10.1037/a0018941
Flannelly, L. T., Flannelly, K. J., & Jankowski, K. R. (2014). Independent, dependent, and other variables in healthcare and chaplaincy research. Journal of Health Care Chaplaincy , 20 (4), 161–170. https://doi.org/10.1080/08854726.2014.959374
Manocha, R., Black, D., Sarris, J., & Stough, C.(2011). A randomized, controlled trial of meditation for work stress, anxiety and depressed mood in full-time workers. Evidence-Based Complementary and Alternative Medicine , vol. 2011, Article ID 960583. https://doi.org/10.1155/2011/960583
Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work (Reading, Mass.) , 22 (3), 255–260.
Taylor, J. M., & Rowe, B. J. (2012). The “Mozart Effect” and the mathematical connection, Journal of College Reading and Learning, 42 (2), 51-66. https://doi.org/10.1080/10790195.2012.10850354
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Statistics By Jim
Making statistics intuitive
By Jim Frost 15 Comments
In this post, learn the definitions of independent and dependent variables, how to identify each type, how they differ between different types of studies, and see examples of them in use.
Independent variables (IVs) are the ones that you include in the model to explain or predict changes in the dependent variable. The name helps you understand their role in statistical analysis. These variables are independent . In this context, independent indicates that they stand alone and other variables in the model do not influence them. The researchers are not seeking to understand what causes the independent variables to change.
Independent variables are also known as predictors, factors , treatment variables, explanatory variables, input variables, x-variables, and right-hand variables—because they appear on the right side of the equals sign in a regression equation. In notation, statisticians commonly denote them using Xs. On graphs, analysts place independent variables on the horizontal, or X, axis.
In machine learning, independent variables are known as features.
For example, in a plant growth study, the independent variables might be soil moisture (continuous) and type of fertilizer (categorical).
Statistical models will estimate effect sizes for the independent variables.
Relate post : Effect Sizes in Statistics
The nature of independent variables changes based on the type of experiment or study:
Controlled experiments : Researchers systematically control and set the values of the independent variables. In randomized experiments, relationships between independent and dependent variables tend to be causal. The independent variables cause changes in the dependent variable.
Observational studies : Researchers do not set the values of the explanatory variables but instead observe them in their natural environment. When the independent and dependent variables are correlated, those relationships might not be causal.
When you include one independent variable in a regression model, you are performing simple regression. For more than one independent variable, it is multiple regression. Despite the different names, it’s really the same analysis with the same interpretations and assumptions.
Determining which IVs to include in a statistical model is known as model specification. That process involves in-depth research and many subject-area, theoretical, and statistical considerations. At its most basic level, you’ll want to include the predictors you are specifically assessing in your study and confounding variables that will bias your results if you don’t add them—particularly for observational studies.
For more information about choosing independent variables, read my post about Specifying the Correct Regression Model .
Related posts : Randomized Experiments , Observational Studies , Covariates , and Confounding Variables
The dependent variable (DV) is what you want to use the model to explain or predict. The values of this variable depend on other variables. It is the outcome that you’re studying. It’s also known as the response variable, outcome variable, and left-hand variable. Statisticians commonly denote them using a Y. Traditionally, graphs place dependent variables on the vertical, or Y, axis.
For example, in the plant growth study example, a measure of plant growth is the dependent variable. That is the outcome of the experiment, and we want to determine what affects it.
If you’re reading a study’s write-up, how do you distinguish independent variables from dependent variables? Here are some tips!
How statisticians discuss independent variables changes depending on the field of study and type of experiment.
In randomized experiments, look for the following descriptions to identify the independent variables:
In observational studies, independent variables are a bit different. While the researchers likely want to establish causation, that’s harder to do with this type of study, so they often won’t use the word “cause.” They also don’t set the values of the predictors. Some independent variables are the experiment’s focus, while others help keep the experimental results valid.
Here’s how to recognize independent variables in observational studies:
Regardless of the study type, if you see an estimated effect size, it is an independent variable.
Dependent variables are the outcome. The IVs explain the variability or causes changes in the DV. Focus on the “depends” aspect. The value of the dependent variable depends on the IVs. If Y depends on X, then Y is the dependent variable. This aspect applies to both randomized experiments and observational studies.
In an observational study about the effects of smoking, the researchers observe the subjects’ smoking status (smoker/non-smoker) and their lung cancer rates. It’s an observational study because they cannot randomly assign subjects to either the smoking or non-smoking group. In this study, the researchers want to know whether lung cancer rates depend on smoking status. Therefore, the lung cancer rate is the dependent variable.
In a randomized COVID-19 vaccine experiment , the researchers randomly assign subjects to the treatment or control group. They want to determine whether COVID-19 infection rates depend on vaccination status. Hence, the infection rate is the DV.
Note that a variable can be an independent variable in one study but a dependent variable in another. It depends on the context.
For example, one study might assess how the amount of exercise (IV) affects health (DV). However, another study might study the factors (IVs) that influence how much someone exercises (DV). The amount of exercise is an independent variable in one study but a dependent variable in the other!
Regression analysis and ANOVA mathematically describe the relationships between each independent variable and the dependent variable. Typically, you want to determine how changes in one or more predictors associate with changes in the dependent variable. These analyses estimate an effect size for each independent variable.
Suppose researchers study the relationship between wattage, several types of filaments, and the output from a light bulb. In this study, light output is the dependent variable because it depends on the other two variables. Wattage (continuous) and filament type (categorical) are the independent variables.
After performing the regression analysis, the researchers will understand the nature of the relationship between these variables. How much does the light output increase on average for each additional watt? Does the mean light output differ by filament types? They will also learn whether these effects are statistically significant.
Related post : When to Use Regression Analysis
As I mentioned earlier, graphs traditionally display the independent variables on the horizontal X-axis and the dependent variable on the vertical Y-axis. The type of graph depends on the nature of the variables. Here are a couple of examples.
Suppose you experiment to determine whether various teaching methods affect learning outcomes. Teaching method is a categorical predictor that defines the experimental groups. To display this type of data, you can use a boxplot, as shown below.
The groups are along the horizontal axis, while the dependent variable, learning outcomes, is on the vertical. From the graph, method 4 has the best results. A one-way ANOVA will tell you whether these results are statistically significant. Learn more about interpreting boxplots .
Now, imagine that you are studying people’s height and weight. Specifically, do height increases cause weight to increase? Consequently, height is the independent variable on the horizontal axis, and weight is the dependent variable on the vertical axis. You can use a scatterplot to display this type of data.
It appears that as height increases, weight tends to increase. Regression analysis will tell you if these results are statistically significant. Learn more about interpreting scatterplots .
April 2, 2024 at 2:05 am
Hi again Jim
Thanks so much for taking an interest in New Zealand’s Equity Index.
Rather than me trying to explain what our Ministry of Education has done, here is a link to a fairly short paper. Scroll down to page 4 of this (if you have the inclination) – https://fyi.org.nz/request/21253/response/80708/attach/4/1301098%20Response%20and%20Appendix.pdf
The Equity Index is used to allocate only 4% of total school funding. The most advantaged 5% of schools get no “equity funding” and the other 95% get a share of the equity funding pool based on their index score. We are talking a maximum of around $1,000NZD per child per year for the most disadvantaged schools. The average amount is around $200-$300 per child per year.
My concern is that I thought the dependent variable is the thing you want to explain or predict using one or more independent variables. Choosing the form of dependent variable that gets a good fit seems to be answering the question “what can we predict well?” rather than “how do we best predict the factor of interest?” The factor is educational achievement and I think this should have been decided upon using theory rather than experimentation with the data.
As it turns out, the Ministry has chosen a measure of educational achievement that puts a heavy weight on achieving an “excellence” rating on a qualification and a much lower weight on simply gaining a qualification. My reading is that they have taken what our universities do when looking at which students to admit.
It doesn’t seem likely to me that a heavy weighting on excellent achievement is appropriate for targeting extra funding to schools with a lot of under-achieving students.
However, my stats knowledge isn’t extensive and it’s definitely rusty, so your thoughts are most helpful.
Regards Kathy Spencer
April 1, 2024 at 4:08 pm
Hi Jim, Great website, thank you.
I have been looking at New Zealand’s Equity Index which is used to allocate a small amount of extra funding to schools attended by children from disadvantaged backgrounds. The Index uses 37 socioeconomic measures relating to a child’s and their parents’ backgrounds that are found to be associated with educational achievement.
I was a bit surprised to read how they had decided on the dependent variable to be used as the measure of educational achievement, or dependent variable. Part of the process was as follows- “Each measure was tested to see the degree to which it could be predicted by the socioeconomic factors selected for the Equity Index.”
Any comment?
Many thanks Kathy Spencer
April 1, 2024 at 9:20 pm
That’s a very complex study and I don’t know much about it. So, that limits what I can say about it. But I’ll give you a few thoughts that come to mind.
This method is common in educational and social research, particularly when the goal is to understand or mitigate the impact of socioeconomic disparities on educational outcomes.
There are the usual concerns about not confusing correlation with causation. However, because this program seems to quantify barriers and then provide extra funding based on the index, I don’t think that’s a problem. They’re not attempting to adjust the socioeconomic measures so no worries about whether they’re directly causal or not.
I might have a small concern about cherry picking the model that happens to maximize the R-squared. Chasing the R-squared rather than having theory drive model selecting is often problematic. Chasing the best fit increases the likelihood that the model fits this specific dataset best by random chance rather than being truly the best. If so, it won’t perform as well outside the dataset used to fit the model. Hopefully, they validated the predicted ability of the model using other data.
However, I’m not sure if the extra funding is determined by the model? I don’t know if the index value is calculated separately outside the candidate models and then fed into the various models. Or does the choice of model affect how the index value is calculated? If it’s the former, then the funding doesn’t depend on a potentially cherry picked model. If the latter, it does.
So, I’m not really clear on the purpose of the model. I’m guessing they just want to validate their Equity Index. And maximizing the R-squared doesn’t really say it’s the best Index but it does at least show that it likely has some merit. I’d be curious how the took the 37 measures and combined them to one index. So, I have more questions than answers. I don’t mean that in a critical sense. Just that I know almost nothing about this program.
I’m curious, what was the outcome they picked? How high was the R-squared? And what were your concerns?
February 6, 2024 at 6:57 pm
Excellent explanation, thank you.
February 5, 2024 at 5:04 pm
Thank you for this insightful blog. Is it valid to use a dependent variable delivered from the mean of independent variables in multiple regression if you want to evaluate the influence of each unique independent variable on the dependent variables?
February 5, 2024 at 11:11 pm
It’s difficult to answer your question because I’m not sure what you mean that the DV is “delivered from the mean of IVs.” If you mean that multiple IVs explain changes in the DV’s mean, yes, that’s the standard use for multiple regression.
If you mean something else, please explain in further detail. Thanks!
February 6, 2024 at 6:32 am
What I meant is; the DV values used as parameters for multiple regression is basically calculated as the average of the IVs. For instance:
From 3 IVs (X1, X2, X3), Y is delivered as :
Y = (Sum of all IVs) / (3)
Then the resulting Y is used as the DV along with the initial IVs to compute the multiple regression.
February 6, 2024 at 2:17 pm
There are a couple of reasons why you shouldn’t do that.
For starters, Y-hat (the predicted value of the regression equation) is the mean of the DV given specific values of the IV. However, that mean is calculated by using the regression coefficients and constant in the regression equation. You don’t calculate the DV mean as the sum of the IVs divided by the number of IVs. Perhaps given a very specific subject-area context, using this approach might seem to make sense but there are other problems.
A critical problem is that the Y is now calculated using the IVs. Instead, the DVs should be measured outcomes and not calculated from IVs. This violates regression assumptions and produces questionable results.
Additionally, it complicates the interpretation. Because the DV is calculated from the IV, you know the regression analysis will find a relationship between them. But you have no idea if that relationship exists in the real world. This complication occurs because your results are based on forcing the DV to equal a function of the IVs and do not reflect real-world outcomes.
In short, DVs should be real-world outcomes that you measure! And be sure to keep your IVs and DV independent. Let the regression analysis estimate the regression equation from your data that contains measured DVs. Don’t use a function to force the DV to equal some function of the IVs because that’s the opposite direction of how regression works!
I hope that helps!
September 6, 2022 at 7:43 pm
Thank you for sharing.
March 3, 2022 at 1:59 am
Excellent explanation.
February 13, 2022 at 12:31 pm
Thanks a lot for creating this excellent blog. This is my go-to resource for Statistics.
I had been pondering over a question for sometime, it would be great if you could shed some light on this.
In linear and non-linear regression, should the distribution of independent and dependent variables be unskewed? When is there a need to transform the data (say, Box-Cox transformation), and do we transform the independent variables as well?
October 28, 2021 at 12:55 pm
If I use a independent variable (X) and it displays a low p-value <.05, why is it if I introduce another independent variable to regression the coefficient and p-value of Y that I used in first regression changes to look insignificant? The second variable that I introduced has a low p-value in regression.
October 29, 2021 at 11:22 pm
Keep in mind that the significance of each IV is calculated after accounting for the variance of all the other variables in the model, assuming you’re using the standard adjusted sums of squares rather than sequential sums of squares. The sums of squares (SS) is a measure of how much dependent variable variability that each IV accounts for. In the illustration below, I’ll assume you’re using the standard of adjusted SS.
So, let’s say that originally you have X1 in the model along with some other IVs. Your model estimates the significance of X1 after assessing the variability that the other IVs account for and finds that X1 is significant. Now, you add X2 to the model in addition to X1 and the other IVs. Now, when assessing X1, the model accounts for the variability of the IVs including the newly added X2. And apparently X2 explains a good portion of the variability. X1 is no longer able to account for that variability, which causes it to not be statistically significant.
In other words, X2 explains some of the variability that X1 previously explained. Because X1 no longer explains it, it is no longer significant.
Additionally, the significance of IVs is more likely to change when you add or remove IVs that are correlated. Correlated IVs is known as multicollinearity. Multicollinearity can be a problem when you have too much. Given the change in significance, I’d check your model for multicollinearity just to be safe! Click the link to read a post that wrote about that!
September 6, 2021 at 8:35 am
nice explanation
August 25, 2021 at 3:09 am
it is excellent explanation
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.
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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.
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.
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).
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.
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.
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.
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.
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.
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.
Educational resources and simple solutions for your research journey
A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.
Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.
Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .
Table of Contents
Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.
As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)
Here is a list of the important characteristics of independent variables .( 2,3)
Independent variables in research are of the following two types:( 4)
Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”
Here are a few quantitative independent variables examples :
Qualitative independent variables are non-numerical variables.
A few qualitative independent variables examples are listed below:
A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.
Here are a few characteristics of dependent variables: ( 3)
Here are a few dependent variable examples :
Dependent variables are of two types:( 5)
These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.
These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.
The following table compares independent vs dependent variables .
How to identify | Manipulated or controlled | Observed or measured |
Purpose | Cause or predictor variable | Outcome or response variable |
Relationship | Independent of other variables | Influenced by the independent variable |
Control | Manipulated or assigned by researcher | Measured or observed during experiments |
Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)
Genetics | What is the relationship between genetics and susceptibility to diseases? | genetic factors | susceptibility to diseases |
History | How do historical events influence national identity? | historical events | national identity |
Political science | What is the effect of political campaign advertisements on voter behavior? | political campaign advertisements | voter behavior |
Sociology | How does social media influence cultural awareness? | social media exposure | cultural awareness |
Economics | What is the impact of economic policies on unemployment rates? | economic policies | unemployment rates |
Literature | How does literary criticism affect book sales? | literary criticism | book sales |
Geology | How do a region’s geological features influence the magnitude of earthquakes? | geological features | earthquake magnitudes |
Environment | How do changes in climate affect wildlife migration patterns? | climate changes | wildlife migration patterns |
Gender studies | What is the effect of gender bias in the workplace on job satisfaction? | gender bias | job satisfaction |
Film studies | What is the relationship between cinematographic techniques and viewer engagement? | cinematographic techniques | viewer engagement |
Archaeology | How does archaeological tourism affect local communities? | archaeological techniques | local community development |
Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)
Chi-Square | t-test | ||
Logistic regression | ANOVA | ||
Phi | Regression | ||
Cramer’s V | Point-biserial correlation | ||
Logistic regression | Regression | ||
Point-biserial correlation | Correlation |
Here are some more research questions and their corresponding independent and dependent variables. ( 6)
What is the impact of online learning platforms on academic performance? | type of learning | academic performance |
What is the association between exercise frequency and mental health? | exercise frequency | mental health |
How does smartphone use affect productivity? | smartphone use | productivity levels |
Does family structure influence adolescent behavior? | family structure | adolescent behavior |
What is the impact of nonverbal communication on job interviews? | nonverbal communication | job interviews |
In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)
Let’s try out these steps with an example.
A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.
To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.
Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.
Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.
Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.
Let’s summarize the key takeaways about independent vs dependent variables from this article:
The following table lists the different types of variables used in research.( 10)
Categorical | Measures a construct that has different categories | gender, race, religious affiliation, political affiliation |
Quantitative | Measures constructs that vary by degree of the amount | weight, height, age, intelligence scores |
Independent (IV) | Measures constructs considered to be the cause | Higher education (IV) leads to higher income (DV) |
Dependent (DV) | Measures constructs that are considered the effect | Exercise (IV) will reduce anxiety levels (DV) |
Intervening or mediating (MV) | Measures constructs that intervene or stand in between the cause and effect | Incarcerated individuals are more likely to have psychiatric disorder (MV), which leads to disability in social roles |
Confounding (CV) | “Rival explanations” that explain the cause-and-effect relationship | Age (CV) explains the relationship between increased shoe size and increase in intelligence in children |
Control variable | Extraneous variables whose influence can be controlled or eliminated | Demographic data such as gender, socioeconomic status, age |
2. Why is it important to differentiate between independent vs dependent variables ?
Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.
3. How are independent and dependent variables used in non-experimental research?
So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.
4. Are there any advantages and disadvantages of using independent vs dependent variables ?
Here are a few advantages and disadvantages of both independent and dependent variables.( 12)
Advantages:
Disadvantages:
We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.
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The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.
The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.
The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”
Here are several examples of independent and dependent variables in experiments:
If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).
This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.
Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis
A dependent variable is the variable being tested in a scientific experiment.
The dependent variable is "dependent" on the independent variable. As the experimenter changes the independent variable, the change in the dependent variable is observed and recorded. When you take data in an experiment, the dependent variable is the one being measured.
Common Misspellings: Dependant variable
Sometimes it's easy to tell the two types of variables apart , but if you get confused, here are tips to help keep scientific variables straight:
When you graph data, the independent variable is on the X-axis, while the dependent variable is on the Y-axis. You can use the DRY MIX acronym to remember this:
D - dependent variable R - responds to change Y - Y-axis
M - manipulated variable (one you change) I - independent variable X - X-axis
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.
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:
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;
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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The dependent variable is the variable that is being measured or tested in an experiment. This is different than the independent variable , which is a variable that stands on its own. For example, in a study looking at how tutoring impacts test scores, the dependent variable would be the participants' test scores since that is what is being measured and the independent variable would be tutoring.
Learn how to tell the difference between dependent and independent variables . We also share how dependent variables are selected in research and a few examples to increase your understanding of how these variables are used in real-life studies.
The dependent variable is called "dependent" because it is thought to depend, in some way, on the variations of the independent variable.
In a psychology experiment , researchers study how changes in one variable (the independent variable) change another variable (the dependent variable). Manipulating independent variables and measuring the effect on dependent variables allows researchers to draw conclusions about cause-and-effect relationships.
These experiments can range from simple to quite complicated, so it can sometimes be a bit confusing to know how to identify the independent vs. dependent variables. Here are a couple of questions to ask to help you learn which is which.
Keep in mind that the dependent variable is the one being measured. So, if the experiment is trying to see how one variable affects another, the variable that is being affected is the dependent variable.
In many psychology experiments and studies, the dependent variable is a measure of a certain aspect of a participant's behavior . In an experiment looking at how sleep affects test performance, for instance, the dependent variable would be test performance.
One way to help identify the dependent variable is to remember that it depends on the independent variable. When researchers make changes to the independent variable, they then measure any changes to the dependent variable.
The independent variable is "independent" because the experimenters are free to vary it as they need. This might mean changing the amount, duration, or type of variable that the participants in the study receive as a treatment or condition.
For example, it's common for treatment-based studies to have some subjects receive a certain treatment while others receive no treatment at all (often called a sham or placebo treatment ). In this case, the treatment is an independent variable because it is the one being manipulated or changed.
Variable being manipulated
Doesn't change based on other variables
Stands on its own
Variable being measured
May change based on other variables
Depends on other variables
How do researchers determine what will be a good dependent variable? There are a few key features a scientist might consider.
Stability is often a good sign of a higher-quality dependent variable. If the experiment is repeated with the same participants, conditions, and experimental manipulations, the effects on the dependent variable should be very close to what they were the first time around.
A researcher might also choose dependent variables based on the complexity of their study. While some studies only have one dependent variable and one independent variable, it is possible to have several of each type.
Researchers might also want to learn how changes in a single independent variable affect several dependent variables. For example, imagine an experiment where a researcher wants to learn how the messiness of a room influences people's creativity levels .
This research might also want to see how the messiness of a room might influence a person's mood. The messiness of a room would be the independent variable and the study would have two dependent variables: level of creativity and mood.
Operationalization is defined as "translating a construct into its manifestation." In simple terms, it refers to how a variable will be measured. So, a good dependent variable is one that you are able to measure.
If measuring burnout , for instance, researchers might decide to use the Maslach Burnout Inventory. If measuring depression, they could use the Patient Health Questionnaire-9 (PHQ-9).
When learning to identify the dependent variables in an experiment, it can be helpful to look at examples. Here are just a few dependent variable examples in psychology research .
Understanding what a dependent variable is and how it is used can be helpful for interpreting different types of research that you encounter in different settings. When trying to determine which variables are which, remember that the independent variables are the cause while the dependent variables are the effect.
The dependent variable depends on the independent variable. Thus, if the independent variable changes, the dependent variable would likely change too.
The dependent variable is placed on a graph's y-axis. This is the vertical line or the line that extends upward. The independent variable is placed on the graph's x-axis or the horizontal line.
The dependent variable is the one being measured. If looking at how a lack of sleep affects mental health , for instance, mental health is the dependent variable. In a study that seeks to find the effects of supplements on mood , the participants' mood is the dependent variable.
A controlled variable is a variable that doesn't change during the experiment. This enables researchers to assess the relationship between the dependent and independent variables more accurately. For example, if trying to assess the impact of drinking green tea on memory, researchers might ask subjects to drink it at the same time of day. This would be a controlled variable.
U.S. National Library of Medicine. Dependent and independent variables .
Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders . Clin Interv Aging . 2015;10:1189-1199. doi:10.2147/CIA.S81868
Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size . Indian Dermatol Online J . 2019;10(1):82-86. doi:10.4103/idoj.IDOJ_468_18
Flannelly LT, Flannelly KJ, Jankowski KR. Independent, dependent, and other variables in healthcare and chaplaincy research . J Health Care Chaplain . 2014;20(4):161-70. doi:10.1080/08854726.2014.959374
Weiten W. Psychology: themes and variations .
Kantowitz BH, Roediger HL, Elmes DG. Experimental psychology .
Vassar M, Matthew H. The retrospective chart review: important methodological considerations . J Educ Eval Health Prof . 2013;10:12. doi:10.3352/jeehp.2013.10.12
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
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Methodology
Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.
In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .
If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .
You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.
You can usually identify the type of variable by asking two questions:
Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.
Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:
A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.
When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .
Type of variable | What does the data represent? | Examples |
---|---|---|
Discrete variables (aka integer variables) | Counts of individual items or values. | |
Continuous variables (aka ratio variables) | Measurements of continuous or non-finite values. |
Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.
There are three types of categorical variables: binary , nominal , and ordinal variables .
Type of variable | What does the data represent? | Examples |
---|---|---|
Binary variables (aka dichotomous variables) | Yes or no outcomes. | |
Nominal variables | Groups with no rank or order between them. | |
Ordinal variables | Groups that are ranked in a specific order. | * |
*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.
To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.
To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .
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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.
You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.
You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.
Type of variable | Definition | Example (salt tolerance experiment) |
---|---|---|
Independent variables (aka treatment variables) | Variables you manipulate in order to affect the outcome of an experiment. | The amount of salt added to each plant’s water. |
Dependent variables (aka ) | Variables that represent the outcome of the experiment. | Any measurement of plant health and growth: in this case, plant height and wilting. |
Control variables | Variables that are held constant throughout the experiment. | The temperature and light in the room the plants are kept in, and the volume of water given to each plant. |
In this experiment, we have one independent and three dependent variables.
The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.
When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).
However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .
Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .
But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.
Type of variable | Definition | Example (salt tolerance experiment) |
---|---|---|
A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven’t controlled it in your experiment. Be careful with these, because confounding variables run a high risk of introducing a variety of to your work, particularly . | Pot size and soil type might affect plant survival as much or more than salt additions. In an experiment you would control these potential confounders by holding them constant. | |
Latent variables | A variable that can’t be directly measured, but that you represent via a proxy. | Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment. |
Composite variables | A variable that is made by combining multiple variables in an experiment. These variables are created when you analyze data, not when you measure it. | The three plant health variables could be combined into a single plant-health score to make it easier to present your findings. |
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
Discrete and continuous variables are two types of quantitative variables :
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Bevans, R. (2023, June 21). Types of Variables in Research & Statistics | Examples. Scribbr. Retrieved August 19, 2024, from https://www.scribbr.com/methodology/types-of-variables/
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Admit it. The mere mention of the term "dependent variables" evokes vague memories of your math and science classes back in high school. If you're a science buff, you likely enjoyed those classes a lot.
Fast forward to today, and that knowledge could've come in handy—except you don't remember the nitty-gritty of it all. Fret not; we've got you covered.
At the heart of every scientific experiment lies the dependent variable, and we cannot overstate its importance in understanding cause-and-effect relationships.
In this definitive guide, we'll look at dependent variables, how they differ from their independent counterparts, how to choose one, examples, and everything in between.
Dovetail streamlines research to help you uncover and share actionable insights
A variable is an entity that can assume different values. In the simplest of terms, we can consider anything that can vary as a variable.
For instance, height is a variable because we can assign a person's height a value. Other variables include income, age, country of birth, test scores, and so on.
Now, back to our topic of the day. A dependent variable varies when other factors influence it. Specifically, it changes as a result of the independent variable's influence.
In an experimental study, the dependent variable is typically the one you're interested in measuring or monitoring to determine whether or not other variables affect it.
In statistics, dependent variables use a few other names, including:
Outcome variables because you observe and measure them by changing independent variables
Response variables because they respond to changes in other variables
Left-hand-side variables because they appear on the left side of the equals sign in a regression equation
Y-variables because 'Y' usually represents them on a graph
Is it possible to define dependent variables in the context of cause-and-effect relationships? Absolutely! That's precisely why this phenomenon exists in the first place.
While the independent variable is the "cause," the dependent variable is the "effect"—the affected variable.
Naturally, you're itching to learn the difference between dependent and independent variables. Luckily for you, that's next.
Let's first understand what an independent variable is. True to its name, an independent variable stands alone, and other variables don’t change or affect it.
If the value of an independent variable changes at any time, that change happens at the researcher's discretion, not because of other variables.
Typically, the researcher determines the independent variable. Its value is clear and well-known right at the beginning of the experiment, unlike the dependent variable. Those values only become clear after the experiment's conclusion.
Comprehending the difference between dependent and independent variables is vital for any research. Thankfully, getting it right the first time isn't difficult.
The quickest way is to place both variables in the sentence below in a logical way:
"The IV causes changes to the DV. It is not possible that DV could cause any changes to IV."
Here's how that would reflect in our above example:
"Sleeping causes changes to test results. It is not possible that test results could cause any changes to sleeping."
When altering the independent variable during an experiment, your goal is to track and measure the changes it causes to dependent variables. Remember that changes in the dependent variable can only occur due to independent variable manipulation.
To better understand the nuanced differences between dependent and independent variables, let's explore a few examples:
Independent variable: The amount of green tea consumed
Dependent variable: Blood pressure
Independent variable: Hours spent doing productive work
Dependent variable: Business growth
Independent variable: Individual changes in the economy
Dependent variable: Customer behavior
On a broader level, here's what makes dependent and independent variables fundamentally different:
Dependent variables:
Depend on other variables
May change due to other variables
Are always the ones you’re measuring
Independent variables:
Stand on their own
Never change due to other variables
Undergo manipulation
Pinpointing a good dependent variable is more complex than it sounds. You're often contending with several above-par variables, leaving you spoilt for choice. Other times, the research context is way too complex and gives nothing away.
Fortunately for you, we've formulated a set of questions to streamline your selection process.
A dependent variable is only half as good as the stability and consistency of its output. A high-quality variable yields the same outcome irrespective of how often you repeat the experiment.
To arrive at accurate conclusions, you must maintain the same conditions, experimental manipulations, and participants from start to finish.
Choosing a dependent variable without first considering the complexity of your study is a recipe for failure. Some studies require more than just a single variable of either type.
You must do your due diligence early in the process to ensure your final results are accurate and conclusive.
You might also have a situation where you want to find out how changes in one independent variable impact a couple of dependent variables. In that case, it's crucial to pinpoint all of them correctly from the get-go.
For instance, say you want to investigate how low employee morale affects productivity.
Obviously, the dependent variable here is productivity, while low employee morale is the independent variable. Upon further scrutiny, you'll realize there's an opportunity to test for a few more dependent variables, including employee turnover and profitability.
So, it all boils down to how complex you want your study to be.
In research, operationalization refers to the ability to measure a variable. A dependent variable is only good enough if you can measure it easily, accurately, and without hiccups.
In measuring individual test results, you may use the standard error of measurement (SEm).
If measuring blood pressure, you could use a digital blood pressure monitor. SEm will tell you how much the repeated measures of the same person on the same digital pressure monitor tend to be spread around the person’s “true” score.
We hate to break it to you, but dependent and independent variables aren't the only variables that may influence the outcome of your experiment. Several others can, too.
Here are a few to be aware of:
You can’t account for a confounding variable in a scientific experiment. It acts as an external force that can quickly change the effect of dependent and independent research variables, often yielding outcomes that differ completely from reality.
For example, a confounding variable may be responsible for the correlation between weight loss and weight loss. We’d expect that the more you exercise, the more likely you will lose weight.
However, a confounding variable may be eating habits: The more people eat, the more weight they gain, regardless of exercise.
It's best to account for confounding variables before your study starts to prevent them from wreaking havoc. Matching, restriction, and randomization are all reliable methods for keeping these wayward variables in check.
Sometimes, it's impossible to control a confounding variable. When that happens, it automatically becomes an extraneous variable .
One way to control extraneous variables is through elimination. Control by elimination means removing potential extraneous variables by holding them constant in all experimental conditions. Otherwise, you may draw inaccurate conclusions about the relationships between the independent and dependent variables.
We've already highlighted several tangible examples of dependent variables. For clarity's sake, let's go a step further.
Here are additional dependent variables examples you might find helpful.
A business wants to find out how the color of the office decor affects worker productivity.
In this case, worker productivity would be the dependent variable, and the color of the office would be the independent variable. The business could also alter the independent variable by instead evaluating how work hours or low morale influence worker productivity.
A researcher wants to determine if giving workers more control over their extra shifts leads to increased job satisfaction.
In an experiment, one group of employees gets to pick up shifts freely and without restriction, while the other group enjoys little freedom. Job satisfaction is the dependent variable in this example.
A researcher intends to investigate the effects of alcohol on the brain.
Here, the dependent variable could be the scores on the PHQ-9 assessment tool, which provisionally diagnoses depression. The independent variable might be the amount of alcohol a participant ingests.
Of course, dependent variable examples abound. We couldn't possibly exhaust all of them. But with the information and slew of examples in this piece, you should be well-positioned to make your next experiment a resounding success.
The role of dependent variables in shaping and grounding modern-day research experiments is undeniably important.
Alongside independent variables, dependent variables make it easy for researchers and organizations to uncover the true impact of events. This speeds up the formulation of real and tangible solutions.
An experimental study has three types of variables:
Independent variable
Dependent variable
Controlled variable
A dependent variable is the one a researcher tests to get its values.
An independent variable is what the researcher changes to test the dependent variable.
The variable that the scientist intentionally holds constant throughout the research is a controlled variable. While it may not be part of the experiment, it's important because it can affect the results.
No. The control group serves as the standard of comparison in a specific experiment. In other words, this group isn't part of the actual experiment.
The opposite of a control group is an experimental group.
Meanwhile, the dependent variable is the factor that may change as a result of independent variable manipulation.
The quickest way to identify a dependent variable is to ask yourself these three questions:
Does it depend on another variable in the experiment?
Does it change due to other variables?
Is it the one you’re measuring?
If your answer to all these questions is yes, that's a dependent variable.
If not, reexamine the above criteria to see if it’s an independent variable instead.
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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 .
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:
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;
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.
Difference between dependent and independent variables.
Home » Difference Between Dependent and Independent Variables
In research, understanding variable types is essential for designing and interpreting experiments effectively. Different types of variables can significantly influence research outcomes. Among these, independent and dependent variables play crucial roles in shaping study results and analyses. Knowing how these variables interact can clarify relationships and cause-effect dynamics.
Independent variables are the conditions or factors manipulated by a researcher. Conversely, dependent variables are those that respond to changes in independent variables. Recognizing these distinctions is vital, as it directly impacts how you formulate hypotheses, conduct experiments, and analyze data. As you explore further, the relationship between these variable types will become clearer, enhancing your research proficiency.
In understanding variable types, it's essential to differentiate between dependent and independent variables. Independent variables are those that are manipulated or changed to observe their effect on another variable. For instance, in a study exploring the impact of study time on exam performance, the study time is the independent variable. It is the factor that researchers control to see how it affects the results.
On the other hand, dependent variables are the outcomes that are measured in response to changes made in independent variables. Continuing with the previous example, exam performance becomes the dependent variable, as it depends on the amount of study time. Understanding these variable types is crucial for conducting experiments and interpreting data effectively, providing clarity in research objectives and methodologies.
Independent variables are key elements in research and experimentation. They represent the factors that researchers manipulate to observe their effects on dependent variables. Essentially, an independent variable is a variable that stands alone and isn’t influenced by other variables in the study. For example, if you're studying the impact of varying temperatures on plant growth, the temperature settings would be the independent variable.
Understanding independent variables is crucial in differentiating between variable types. They play a vital role in establishing cause-and-effect relationships. Researchers often change these variables to determine their effects on dependent variables, which respond to the independent variables' alterations. This interaction is fundamental in many fields, including science, social studies, and economics. By carefully controlling and manipulating independent variables, researchers can derive meaningful conclusions from their studies.
Dependent variables are the outcomes or responses that researchers measure in an experiment. These variables are influenced by changes made to independent variables, effectively serving as indicators of what occurs as a result of those changes. For example, in a study examining the impact of study hours on test scores, the test score is the dependent variable.
Understanding dependent variables is crucial because they provide valuable insights into the relationship between variable types. When researchers manipulate an independent variable, they observe the effects on a dependent variable to draw conclusions about correlations and causations. This relationship helps identify patterns and trends, contributing to advancements in fields like psychology, economics, and health sciences. In summary, dependent variables tell us how outcomes are affected by specific conditions, making them essential for effective data analysis and interpretation.
In understanding the key differences between variable types, it is essential to distinguish between independent and dependent variables. Independent variables are those that researchers manipulate or control during an experiment. They serve as the potential cause, influencing the outcome in question. In contrast, dependent variables are the effects or outcomes that are observed and measured. Essentially, these variables respond to the changes made by the independent ones.
Another vital aspect of variable types is their role in establishing relationships within research. The independent variable can be thought of as the factor that initiates changes, while the dependent variable provides valuable insights into the results of those changes. Understanding this dynamic allows researchers to create hypotheses and analyze the data effectively. By mastering these differences, one can conduct and interpret experiments more accurately, enhancing the quality of research outcomes.
In research, control and manipulation are essential for understanding variable types. Researchers aim to isolate the influence of independent variables on dependent variables, allowing for accurate results. Control involves establishing conditions that prevent external factors from affecting the outcome, ensuring data reliability. By manipulating independent variables, researchers can observe the changes in dependent variables, creating a clearer picture of cause-and-effect relationships.
To effectively control and manipulate variables, researchers should focus on the following aspects:
Randomization : This technique minimizes selection bias, distributing participants across groups fairly. By random assignment, the influence of confounding variables is reduced, enhancing the clarity of observed effects.
Standardization : Maintaining consistent procedures for all participants ensures that any changes in dependent variables can be attributed to the manipulated independent variables. This consistency strengthens the validity of the research findings.
Replication : Repeating experiments verifies results and confirms that findings are not due to chance. Replicated studies enhance the credibility of the relationships between variable types identified in research.
By thoughtfully controlling and manipulating variables, researchers gain deeper insights into how changes can impact outcomes, ultimately contributing to the reliability of their conclusions.
Understanding the cause and effect relationship between dependent and independent variables is essential in research. This dynamic illustrates how varying one variable can influence another, giving insights into patterns and trends. For example, in an experiment, an independent variable may be manipulated to observe its impact on a dependent variable. This relationship forms the foundation of scientific inquiry, allowing researchers to establish connections and draw conclusions based on observable data.
In practical terms, the cause and effect relationship helps in formulating hypotheses and making predictions. Identifying and understanding these variable types enables researchers to isolate factors that contribute to specific outcomes. For instance, if one studies the impact of temperature on plant growth, the temperature acts as the independent variable while the plant growth is the dependent variable. By analyzing these interactions, researchers gain valuable knowledge to further their inquiries and refine their methods.
In conclusion, understanding variable types is essential when conducting research. Differentiating between dependent and independent variables lays the foundation for accurate analysis and interpretation of data. Independent variables serve as the predictors or influencers, while dependent variables are the outcomes affected by these predictors. Recognizing their roles can significantly enhance the quality of research outcomes.
Effectively identifying and manipulating these variable types is crucial for sound conclusions in any research study. Researchers must grasp how changes in independent variables can lead to variations in dependent variables. This understanding not only strengthens the research design but also ensures that findings are reliable and valid for informed decision-making.
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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!
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.
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!
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.
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!
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!
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!
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!
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!
Researchers might examine the impact of different exercise forms on individuals’ health. The various exercise forms constitute the independent variable in this study!
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!
Psychologists might investigate how diverse study methods influence test outcomes. Here, the different study methods adopted by students are the independent variable!
Have you experienced varied emotions with different music genres? The genre of music played becomes the independent variable when researching its influence on emotions!
Suppose researchers are exploring how room colors affect individuals’ emotions. In this case, the room colors act as the independent variable!
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!
Examining how temperature variations affect animal behavior is fascinating. Here, the varying temperatures serve as the independent variable!
Investigating the effects of different pollution levels on air quality is crucial. In such studies, the pollution level is the independent variable!
Researchers might explore how varying internet speeds impact work productivity. In this exploration, the internet speed is the independent variable!
Examining how different devices affect user experience is interesting. Here, the type of device used is the independent variable!
Suppose a study aims to determine how different software versions influence system performance. The software version becomes the independent variable!
Educators might investigate the effect of varied teaching styles on student engagement. In such a study, the teaching style is the independent variable!
Researchers could explore how different class sizes influence students’ learning. Here, the class size is the independent variable!
Examining the relationship between the frequency of homework assignments and academic success is essential. The frequency of homework becomes the independent variable!
Astronomers might study how different telescopes affect celestial observation. In this scenario, the telescope type is the independent variable!
Investigating the influence of varying light pollution levels on star visibility is intriguing. Here, the level of light pollution is the independent variable!
Suppose a study explores how observation duration affects the detail captured in astronomical images. The duration of observation serves as the independent variable!
Sociologists may examine how the size of a community influences social interactions. In this research, the community size is the independent variable!
Investigating the effect of diverse cultural exposure on social tolerance is vital. Here, the level of cultural exposure is the independent variable!
Researchers could explore how different economic statuses impact educational achievements. In such studies, economic status is the independent variable!
Sports scientists might study how varying training intensities affect athletes’ performance. In this case, the training intensity is the independent variable!
Examining the relationship between different sports equipment and player safety is crucial. Here, the type of equipment used is the independent variable!
Suppose researchers are investigating how the size of a sports team influences game strategy. The team size becomes the independent variable!
Nutritionists may explore the impact of various diets on individuals’ health. In this exploration, the type of diet followed is the independent variable!
Investigating how different caloric intakes influence weight change is essential. In such a study, the caloric intake is the independent variable!
Researchers could examine how consuming a variety of foods affects nutrient absorption. Here, the variety of foods consumed is the independent variable!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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!
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.
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!
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!
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!
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!
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!
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!
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)
You’re cooking pasta for dinner and want to find out how the cooking time affects its texture. What is the independent variable?
You decide to try different exercise routines each week to see which one makes you feel the most energetic. What is the independent variable?
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?
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?
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.
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COMMENTS
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Variables are a vital part of research. Discover what a dependent variable is and how researchers use it in studies in this guide.
Learn about the most popular types of variables in research, including dependent, independent and control variables - as well as mediating, moderating and co...
A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function ), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...
Compare the independent variable and dependent variable in research. See other types of variables in research, including confounding and extraneous...
Reviewing independent and dependent variable examples can be the key to grasping what makes these concepts different. Explore these simple explanations here.
Recognizing dependent variables is crucial in understanding the variable relationship within research. A dependent variable is influenced by changes in one or more independent variables. For instance, if you're studying how study hours affect exam scores, the exam score acts as the dependent variable. The more hours studied, the higher the ...
Identifying Dependent and Indepent Variables Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research.
Continuing with the previous example, exam performance becomes the dependent variable, as it depends on the amount of study time. Understanding these variable types is crucial for conducting experiments and interpreting data effectively, providing clarity in research objectives and methodologies.
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.
Example. Paper title. Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize major words of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms.