2. variables
3. variables
4. variables
5. variables
6. variables
7. variables
8. variables
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 ...
Very informative, concise and helpful. Thank you
Helping information.Thanks
practical and well-demonstrated
Very helpful and insightful
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
Home » Independent Variable – Definition, Types and Examples
Table of Contents
Definition:
Independent variable is a variable that is manipulated or changed by the researcher to observe its effect on the dependent variable. It is also known as the predictor variable or explanatory variable
The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome. The relationship between the independent variable and the dependent variable is often analyzed using statistical methods to determine the strength and direction of the relationship.
Types of Independent Variables are as follows:
These variables are categorical or nominal in nature and represent a group or category. Examples of categorical independent variables include gender, ethnicity, marital status, and educational level.
These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure.
These variables are discrete in nature and can only take on specific values. Examples of discrete independent variables include the number of siblings, the number of children in a family, and the number of pets owned.
These variables are dichotomous or binary in nature, meaning they can take on only two values. Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker.
These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus.
Following analysis methods that can be used to examine the relationship between an independent variable and a dependent variable:
This method is used to determine the strength and direction of the relationship between two continuous variables. Correlation coefficients such as Pearson’s r or Spearman’s rho are used to quantify the strength and direction of the relationship.
This method is used to compare the means of two or more groups for a continuous dependent variable. ANOVA can be used to test the effect of a categorical independent variable on a continuous dependent variable.
This method is used to examine the relationship between a dependent variable and one or more independent variables. Linear regression is a common type of regression analysis that can be used to predict the value of the dependent variable based on the value of one or more independent variables.
This method is used to test the association between two categorical variables. It can be used to examine the relationship between a categorical independent variable and a categorical dependent variable.
This method is used to compare the means of two groups for a continuous dependent variable. It can be used to test the effect of a binary independent variable on a continuous dependent variable.
There are four commonly used Measuring Scales of Independent Variables:
Here are some examples of independent variables:
Independent Variable | ||
---|---|---|
The variable that is changed or manipulated in an experiment. | The variable that is measured or observed and is affected by the independent variable. | |
The independent variable is the cause and influences the dependent variable. | The dependent variable is the effect and is influenced by the independent variable. | |
Typically plotted on the x-axis of a graph. | Typically plotted on the y-axis of a graph. | |
Age, gender, treatment type, temperature, time. | Blood pressure, heart rate, test scores, reaction time, weight. | |
The researcher can control the independent variable to observe its effects on the dependent variable. | The researcher cannot control the dependent variable but can measure and observe its changes in response to the independent variable. | |
To determine the effect of the independent variable on the dependent variable. | To observe changes in the dependent variable and understand how it is affected by the independent variable. |
Applications of Independent Variable in different fields are as follows:
The purpose of an independent variable is to manipulate or control it in order to observe its effect on the dependent variable. In other words, the independent variable is the variable that is being tested or studied to see if it has an effect on the dependent variable.
The independent variable is often manipulated by the researcher in order to create different experimental conditions. By varying the independent variable, the researcher can observe how the dependent variable changes in response. For example, in a study of the effects of caffeine on memory, the independent variable would be the amount of caffeine consumed, while the dependent variable would be memory performance.
The main purpose of the independent variable is to determine causality. By manipulating the independent variable and observing its effect on the dependent variable, researchers can determine whether there is a causal relationship between the two variables. This is important for understanding how different variables affect each other and for making predictions about how changes in one variable will affect other variables.
Here are some situations when an independent variable may be used:
Here are some of the characteristics of independent variables:
Independent variables have several advantages, including:
Independent variables also have several disadvantages, including:
Researcher, Academic Writer, Web developer
General Education
Independent and dependent variables are important for both math and science. If you don't understand what these two variables are and how they differ, you'll struggle to analyze an experiment or plot equations. Fortunately, we make learning these concepts easy!
In this guide, we break down what independent and dependent variables are , give examples of the variables in actual experiments, explain how to properly graph them, provide a quiz to test your skills, and discuss the one other important variable you need to know.
A variable is something you're trying to measure. It can be practically anything, such as objects, amounts of time, feelings, events, or ideas. If you're studying how people feel about different television shows, the variables in that experiment are television shows and feelings. If you're studying how different types of fertilizer affect how tall plants grow, the variables are type of fertilizer and plant height.
There are two key variables in every experiment: the independent variable and the dependent variable.
Independent variable: What the scientist changes or what changes on its own.
Dependent variable: What is being studied/measured.
The independent variable (sometimes known as the manipulated variable) is the variable whose change isn't affected by any other variable in the experiment. Either the scientist has to change the independent variable herself or it changes on its own; nothing else in the experiment affects or changes it. Two examples of common independent variables are age and time. There's nothing you or anything else can do to speed up or slow down time or increase or decrease age. They're independent of everything else.
The dependent variable (sometimes known as the responding variable) is what is being studied and measured in the experiment. It's what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).
An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.
It can be a lot easier to understand the differences between these two variables with examples, so let's look at some sample experiments below.
Below are overviews of three experiments, each with their independent and dependent variables identified.
Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels so you can get the most value for your money. You test different brands of popcorn to see which bag pops the most popcorn kernels.
Experiment 2 : You want to see which type of fertilizer helps plants grow fastest, so you add a different brand of fertilizer to each plant and see how tall they grow.
Experiment 3: You're interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures.
For each of the independent variables above, it's clear that they can't be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.
Independent and dependent variables always go on the same places in a graph. This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis.
Here's an example:
As you can see, this is a graph showing how the number of hours a student studies affects the score she got on an exam. From the graph, it looks like studying up to six hours helped her raise her score, but as she studied more than that her score dropped slightly.
The amount of time studied is the independent variable, because it's what she changed, so it's on the x-axis. The score she got on the exam is the dependent variable, because it's what changed as a result of the independent variable, and it's on the y-axis. It's common to put the units in parentheses next to the axis titles, which this graph does.
There are different ways to title a graph, but a common way is "[Independent Variable] vs. [Dependent Variable]" like this graph. Using a standard title like that also makes it easy for others to see what your independent and dependent variables are.
Independent and dependent variables are the two most important variables to know and understand when conducting or studying an experiment, but there is one other type of variable that you should be aware of: constant variables.
Constant variables (also known as "constants") are simple to understand: they're what stay the same during the experiment. Most experiments usually only have one independent variable and one dependent variable, but they will all have multiple constant variables.
For example, in Experiment 2 above, some of the constant variables would be the type of plant being grown, the amount of fertilizer each plant is given, the amount of water each plant is given, when each plant is given fertilizer and water, the amount of sunlight the plants receive, the size of the container each plant is grown in, and more. The scientist is changing the type of fertilizer each plant gets which in turn changes how much each plant grows, but every other part of the experiment stays the same.
In experiments, you have to test one independent variable at a time in order to accurately understand how it impacts the dependent variable. Constant variables are important because they ensure that the dependent variable is changing because, and only because, of the independent variable so you can accurately measure the relationship between the dependent and independent variables.
If you didn't have any constant variables, you wouldn't be able to tell if the independent variable was what was really affecting the dependent variable. For example, in the example above, if there were no constants and you used different amounts of water, different types of plants, different amounts of fertilizer and put the plants in windows that got different amounts of sun, you wouldn't be able to say how fertilizer type affected plant growth because there would be so many other factors potentially affecting how the plants grew.
If you're still having a hard time understanding the relationship between independent and dependent variable, it might help to see them in action. Here are three experiments you can try at home.
One simple way to explore independent and dependent variables is to construct a biology experiment with seeds. Try growing some sunflowers and see how different factors affect their growth. For example, say you have ten sunflower seedlings, and you decide to give each a different amount of water each day to see if that affects their growth. The independent variable here would be the amount of water you give the plants, and the dependent variable is how tall the sunflowers grow.
Explore a wide range of chemical reactions with this chemistry kit . It includes 100+ ideas for experiments—pick one that interests you and analyze what the different variables are in the experiment!
Build and test a range of simple and complex machines with this K'nex kit . How does increasing a vehicle's mass affect its velocity? Can you lift more with a fixed or movable pulley? Remember, the independent variable is what you control/change, and the dependent variable is what changes because of that.
Can you identify the independent and dependent variables for each of the four scenarios below? The answers are at the bottom of the guide for you to check your work.
Scenario 1: You buy your dog multiple brands of food to see which one is her favorite.
Scenario 2: Your friends invite you to a party, and you decide to attend, but you're worried that staying out too long will affect how well you do on your geometry test tomorrow morning.
Scenario 3: Your dentist appointment will take 30 minutes from start to finish, but that doesn't include waiting in the lounge before you're called in. The total amount of time you spend in the dentist's office is the amount of time you wait before your appointment, plus the 30 minutes of the actual appointment
Scenario 4: You regularly babysit your little cousin who always throws a tantrum when he's asked to eat his vegetables. Over the course of the week, you ask him to eat vegetables four times.
Knowing the independent variable definition and dependent variable definition is key to understanding how experiments work. The independent variable is what you change, and the dependent variable is what changes as a result of that. You can also think of the independent variable as the cause and the dependent variable as the effect.
When graphing these variables, the independent variable should go on the x-axis (the horizontal axis), and the dependent variable goes on the y-axis (vertical axis).
Constant variables are also important to understand. They are what stay the same throughout the experiment so you can accurately measure the impact of the independent variable on the dependent variable.
Independent and dependent variables are commonly taught in high school science classes. Read our guide to learn which science classes high school students should be taking.
Scoring well on standardized tests is an important part of having a strong college application. Check out our guides on the best study tips for the SAT and ACT.
Interested in science? Science Olympiad is a great extracurricular to include on your college applications, and it can help you win big scholarships. Check out our complete guide to winning Science Olympiad competitions.
Quiz Answers
1: Independent: dog food brands; Dependent: how much you dog eats
2: Independent: how long you spend at the party; Dependent: your exam score
3: Independent: Amount of time you spend waiting; Dependent: Total time you're at the dentist (the 30 minutes of appointment time is the constant)
4: Independent: Number of times your cousin is asked to eat vegetables; Dependent: number of tantrums
These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.
How to Get Into Harvard and the Ivy League
How to Get a Perfect 4.0 GPA
How to Write an Amazing College Essay
What Exactly Are Colleges Looking For?
ACT vs. SAT: Which Test Should You Take?
When should you take the SAT or ACT?
Get Your Free
Find Your Target SAT Score
Free Complete Official SAT Practice Tests
Score 800 on SAT Math
Score 800 on SAT Reading and Writing
Score 600 on SAT Math
Score 600 on SAT Reading and Writing
Find Your Target ACT Score
Complete Official Free ACT Practice Tests
Get a 36 on ACT English
Get a 36 on ACT Math
Get a 36 on ACT Reading
Get a 36 on ACT Science
Get a 24 on ACT English
Get a 24 on ACT Math
Get a 24 on ACT Reading
Get a 24 on ACT Science
Stay Informed
Get the latest articles and test prep tips!
Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.
Have any questions about this article or other topics? Ask below and we'll reply!
The history of variables, independent variables, a final word.
An independent variable is one of the two types of variables used in a scientific experiment. The independent variable is the variable that can be controlled and changed; the dependent variable is directly affected by the change in the independent variable.
If you think back to the last science class you took, you probably remember a lot of discussion surrounding variables. In fact, this concept is widespread and applied to many different areas of life, but it has the same fundamental meaning. The weather can be “variable”, meaning that it changes quite often, and the same can be said of personalities and moods. By introducing a new “variable” into a situation, such as inviting your new in-laws over for Christmas, you are expecting the outcome to be different than if they were not in attendance.
Although you might not think of these small, daily occurrences as “experiments”, every decision in life can be compared to a scientific study! However, what you may not remember from your science class is the difference between certain variable types. This article will dive into these specifics a bit deeper, particularly in terms of independent variables .
Recommended Video for you:
In the human history of logic and reasoning, there have been many critical turning points, but one of the most fundamental concepts—the variable—has its origins in 7th century India, specifically with a mathematician named Brahmagupta. Not only was he the first mathematician to outline rules for the use of “zero”, but also developed the first rudimentary system to analyze unknowns. When designing and expressing algebraic equations, he used different colored patches to label different known and unknown quantities.
Nearly 1,000 years later, in the west, a similar concept of labeling unknown and known quantities with letters was introduced. In his equations, he utilized consonants for known quantities, and vowels for unknown quantities. Less than a century later, Rene Descartes instead chose to use a, b and c for known quantities, and x, y and z for unknown quantities. To this day, this is the standard system that remains in use across most of the sciences, including mathematics.
Two hundred years later, the idea of infinitesimal calculus was developed, which led to the development of a “function”, in which an infinitesimal variation of a variable quantity causes a corresponding variation in another quantity, making the latter of a function of the former. Without going beyond the scope of this article, this deeper definition of a variable has led to incredible modern advancements in engineering, economics and mathematics, among many others.
Variables have proven to be invaluable for the calculation and theorization of complex ideas and computations across a multitude of fields. but in the realm of scientific experiments, variables take on a slightly different (and simpler) role.
Also Read: What Is Endogeneity? What Is An Exogenous Variable?
As mentioned above, independent and dependent variables are the two key components of an experiment. Quite simply, the independent variable is the state, condition or experimental element that is controlled and manipulated by the experimenter. The dependent variable is what an experimenter is attempting to test, learn about or measure, and will be “dependent” on the independent variable.
This is similar to the mathematical concept of variables, in that an independent variable is a known quantity, and a dependent variable is an unknown quantity. In most scientific experiments, there should only be a single independent variable, as you are attempting to measure the change of other variables in relation to the controlled manipulation of the independent variable. If you change two variables, for example, then it becomes difficult, if not impossible, to determine the exact cause of the variation in the dependent variable.
To make this even easier to understand, let’s take a look at an example. Imagine that you’re conducting an experiment in which you want to see what is the best watering pattern for a particular type of plant. You line up three identical styrofoam cups full of the same quantity, quality and density of soil. You then plant three seeds of the same plant variety in each cup. The first cup receives 2 ounces of water once a day, the second cup receives 2 ounces of water every other day, and the third cup receives 2 ounces of water every third day.
In this example, there is only one independent variable—the watering regularity. All of the other potential variables are kept consistent and unchanged, such as the type of plant, the quality of the soil and even the amount of water administered each day. These represent the third type of variable present in any experiment—the controlled variables. If any additional controlled variables were changing, it would be impossible to definitively determine the connection between the independent and dependent variables.
After 4-6 weeks of the experiment, one could measure the amount of growth in each newly sprouted plant; these measurements are the dependent variables, as they are dependent on the amount of water each plant receives (the independent variable).
Also Read: What Is A Controlled Experiment? Aren’t All Experiments Controlled?
This may seem like a simple concept, but it underpins all scientific inquiry, so it’s very important to understand. It is also applicable in your own life every single day. For example, if you’re a scientifically minded person and are unhappy with the direction your life is going, try to change one thing in a concentrated way (i.e., getting a new job, finding/leaving a partner, changing a daily habit etc.). This is your independent variable. After a set amount of time (days, weeks, months), take stock of what has changed since making the change. What you identify as having changed (either good or bad) is your dependent variable!
Changing everything at the exact same time, such as simultaneously leaving a job, ending a relationship and moving to a new city, will make it difficult (if not impossible) to identify which of those changes had the most notable and measurable effect. Obviously, life is unpredictable and some variables cannot be controlled, but thinking about variables and causation in your daily decisions can help you take a more logical and informed path!
John Staughton is a traveling writer, editor, publisher and photographer who earned his English and Integrative Biology degrees from the University of Illinois. He is the co-founder of a literary journal, Sheriff Nottingham, and the Content Director for Stain’d Arts, an arts nonprofit based in Denver. On a perpetual journey towards the idea of home, he uses words to educate, inspire, uplift and evolve.
Independent vs. Dependent Variables
The two main variables in a scientific experiment are the independent and dependent variables. An independent variable is changed or controlled in a scientific experiment to test the effects on another variable. This variable being tested and measured is called the dependent variable.
As its name suggests, the dependent variable is "dependent" on the independent variable. As the experimenter changes the independent variable, the effect on the dependent variable is observed and recorded.
Let's say a scientist wants to see if the brightness of light has any effect on a moth's attraction to the light. The brightness of the light is controlled by the scientist. This would be the independent variable . How the moth reacts to the different light levels (such as its distance to the light source) would be the dependent variable .
As another example, say you want to know whether eating breakfast affects student test scores. The factor under the experimenter's control is the presence or absence of breakfast, so you know it is the independent variable. The experiment measures test scores of students who ate breakfast versus those who did not. Theoretically, the test results depend on breakfast, so the test results are the dependent variable. Note that test scores are the dependent variable even if it turns out there is no relationship between scores and breakfast.
For another experiment, a scientist wants to determine whether one drug is more effective than another at controlling high blood pressure. The independent variable is the drug, while the patient's blood pressure is the dependent variable. In some ways, this experiment resembles the one with breakfast and test scores. However, when comparing two different treatments, such as drug A and drug B, it's usual to add another variable, called the control variable. The control variable , which in this case is a placebo that contains the same inactive ingredients as the drugs, makes it possible to tell whether either drug actually affects blood pressure.
The independent and dependent variables in an experiment may be viewed in terms of cause and effect. If the independent variable is changed, then an effect is seen, or measured, in the dependent variable. Remember, the values of both variables may change in an experiment and are recorded. The difference is that the value of the independent variable is controlled by the experimenter, while the value of the dependent variable only changes in response to the independent variable.
When results are plotted in graphs, the convention is to use the independent variable as the x-axis and the dependent variable as the y-axis. The DRY MIX acronym can help keep the variables straight:
D is the dependent variable R is the responding variable Y is the axis on which the dependent or responding variable is graphed (the vertical axis)
M is the manipulated variable or the one that is changed in an experiment I is the independent variable X is the axis on which the independent or manipulated variable is graphed (the horizontal axis)
The independent and dependent variables are the two main types of variables in a science experiment. A variable is anything you can observe, measure, and record. This includes measurements, colors, sounds, presence or absence of an event, etc.
The independent variable is the one factor you change to test its effects on the dependent variable . In other words, the dependent variable “depends” on the independent variable. The independent variable is sometimes called the controlled variable, while the dependent variable may be called the experimental or responding variable.
Both the independent and dependent variables may change during an experiment, but the independent variable is the one you control, while the dependent variable is one you measure in response to this change. The easiest way to tell the two variables apart is to phrase the experiment in terms of an “if-then” or “cause and effect” statement. If you change the independent variable, then you measure its effect on the dependent variable. The cause is the independent variable, while the effect is the dependent variable. If you state “time spent studying affect grades” (independent variables determines dependent variable), the statement makes sense. If your cause and effect statement is in the wrong order (grades determine time spent studying), it doesn’t make sense.
Sometimes the independent variable is easy to identify. Time and age are almost always the independent variable in an experiment. You can measure them, but you can’t control any factor to change them.
Ask yourself these questions to help tell the two variables apart:
For example, if you want to see whether changing dog food affects your pet’s weight, you can phrase the experiment as, “If I change dog food, then my dog’s weight may change.” The independent variable is the type of dog food, while the dog’s weight is the dependent variable.
In an experiment to test whether a drug is an effective pain reliever, the presence, absence, or dose of the drug is the variable you control (the independent variable), while the pain level of the patient is the dependent variable.
In an experiment to determine whether ice cube shapes determine how quickly ice cubes melt, the independent variable is the shape of the ice cube, while the time it takes to melt is the dependent variable.
If you want to see if the temperature of a classroom affects test score, the temperature is the independent variable. Test scores are the dependent variable.
By convention, the independent variable is plotted on the x-axis of a graph, while the dependent variable is plotted on the y-axis. Use the DRY MIX acronym to remember the variables:
D is the dependent variable R is the variable that responds Y is the y-axis or vertical axis
M is the manipulated or controlled variable I is the independent variable X is the x-axis or horizontal axis
To view these resources with no ads, please login or subscribe to help support our content development. school subscriptions can access more than 175 downloadable unit bundles in our store for free (a value of $1,500). district subscriptions provide huge group discounts for their schools. email for a quote: [email protected] ..
Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature). The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.
An experiment can have three kinds of variables: i ndependent, dependent, and controlled .
For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.
Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.
Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?
Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.
Please Login or Subscribe to access downloadable content.
When you research information you must cite the reference. Citing for websites is different from citing from books, magazines and periodicals. The style of citing shown here is from the MLA Style Citations (Modern Language Association).
When citing a WEBSITE the general format is as follows. Author Last Name, First Name(s). "Title: Subtitle of Part of Web Page, if appropriate." Title: Subtitle: Section of Page if appropriate. Sponsoring/Publishing Agency, If Given. Additional significant descriptive information. Date of Electronic Publication or other Date, such as Last Updated. Day Month Year of access < URL >.
Amsel, Sheri. "Experimental Design - Independent, Dependent, and Controlled Variables" Exploring Nature Educational Resource ©2005-2024. March 25, 2024 < http://www.exploringnature.org/db/view/Experimental-Design-Independent-Dependent-and-Controlled-Variables >
Sciencing_icons_biology biology, sciencing_icons_cells cells, sciencing_icons_molecular molecular, sciencing_icons_microorganisms microorganisms, sciencing_icons_genetics genetics, sciencing_icons_human body human body, sciencing_icons_ecology ecology, sciencing_icons_chemistry chemistry, sciencing_icons_atomic & molecular structure atomic & molecular structure, sciencing_icons_bonds bonds, sciencing_icons_reactions reactions, sciencing_icons_stoichiometry stoichiometry, sciencing_icons_solutions solutions, sciencing_icons_acids & bases acids & bases, sciencing_icons_thermodynamics thermodynamics, sciencing_icons_organic chemistry organic chemistry, sciencing_icons_physics physics, sciencing_icons_fundamentals-physics fundamentals, sciencing_icons_electronics electronics, sciencing_icons_waves waves, sciencing_icons_energy energy, sciencing_icons_fluid fluid, sciencing_icons_astronomy astronomy, sciencing_icons_geology geology, sciencing_icons_fundamentals-geology fundamentals, sciencing_icons_minerals & rocks minerals & rocks, sciencing_icons_earth scructure earth structure, sciencing_icons_fossils fossils, sciencing_icons_natural disasters natural disasters, sciencing_icons_nature nature, sciencing_icons_ecosystems ecosystems, sciencing_icons_environment environment, sciencing_icons_insects insects, sciencing_icons_plants & mushrooms plants & mushrooms, sciencing_icons_animals animals, sciencing_icons_math math, sciencing_icons_arithmetic arithmetic, sciencing_icons_addition & subtraction addition & subtraction, sciencing_icons_multiplication & division multiplication & division, sciencing_icons_decimals decimals, sciencing_icons_fractions fractions, sciencing_icons_conversions conversions, sciencing_icons_algebra algebra, sciencing_icons_working with units working with units, sciencing_icons_equations & expressions equations & expressions, sciencing_icons_ratios & proportions ratios & proportions, sciencing_icons_inequalities inequalities, sciencing_icons_exponents & logarithms exponents & logarithms, sciencing_icons_factorization factorization, sciencing_icons_functions functions, sciencing_icons_linear equations linear equations, sciencing_icons_graphs graphs, sciencing_icons_quadratics quadratics, sciencing_icons_polynomials polynomials, sciencing_icons_geometry geometry, sciencing_icons_fundamentals-geometry fundamentals, sciencing_icons_cartesian cartesian, sciencing_icons_circles circles, sciencing_icons_solids solids, sciencing_icons_trigonometry trigonometry, sciencing_icons_probability-statistics probability & statistics, sciencing_icons_mean-median-mode mean/median/mode, sciencing_icons_independent-dependent variables independent/dependent variables, sciencing_icons_deviation deviation, sciencing_icons_correlation correlation, sciencing_icons_sampling sampling, sciencing_icons_distributions distributions, sciencing_icons_probability probability, sciencing_icons_calculus calculus, sciencing_icons_differentiation-integration differentiation/integration, sciencing_icons_application application, sciencing_icons_projects projects, sciencing_icons_news news.
Say you're in lab, and your teacher asks you to design an experiment. The experiment must test how plants grow in response to different colored light. How would you begin? What are you changing? What are you keeping the same? What are you measuring?
These parameters of what you would change and what you would keep the same are called variables. Take a look at how all of these parameters in an experiment are defined, as independent, dependent and controlled variables.
A variable is any quantity that you are able to measure in some way. This could be temperature, height, age, etc. Basically, a variable is anything that contributes to the outcome or result of your experiment in any way.
In an experiment there are multiple kinds of variables: independent, dependent and controlled variables.
An independent variable is the variable the experimenter controls. Basically, it is the component you choose to change in an experiment. This variable is not dependent on any other variables.
For example, in the plant growth experiment, the independent variable is the light color. The light color is not affected by anything. You will choose different light colors like green, red, yellow, etc. You are not measuring the light.
A dependent variable is the measurement that changes in response to what you changed in the experiment. This variable is dependent on other variables; hence the name! For example, in the plant growth experiment, the dependent variable would be plant growth.
You could measure this by measuring how much the plant grows every two days. You could also measure it by measuring the rate of photosynthesis. Either of these measurements are dependent upon the kind of light you give the plant.
A control variable in science is any other parameter affecting your experiment that you try to keep the same across all conditions.
For example, one control variable in the plant growth experiment could be temperature. You would not want to have one plant growing in green light with a temperature of 20°C while another plant grows in red light with a temperature of 27°C.
You want to measure only the effect of light, not temperature. For this reason you would want to keep the temperature the same across all of your plants. In other words, you would want to control the temperature.
Another example is the amount of water you give the plant. If one plant receives twice the amount of water as another plant, there would be no way for you to know that the reason those plants grew the way they did is due only to the light color their received.
The observed effect could also be due in part to the amount of water they got. A control variable in science experiments is what allows you to compare other things that may be contributing to a result because you have kept other important things the same across all of your subjects.
When graphing the results of your experiment, it is important to remember which variable goes on which axis.
The independent variable is graphed on the x-axis . The dependent variable , which changes in response to the independent variable, is graphed on the y-axis . Controlled variables are usually not graphed because they should not change. They could, however, be graphed as a verification that other conditions are not changing.
For example, after graphing the growth as compared to light, you could also look at how the temperature varied across different conditions. If you notice that it did vary quite a bit, you may need to go back and look at your experimental setup: How could you improve the experiment so that all plants are exposed to as similar an environment as possible (aside from the light color)?
In order to try and remember which is the dependent variable and which is the independent variable, try putting them into a sentence which uses "causes a change in."
Here's an example. Saying, "light color causes a change in plant growth," is possible. This shows us that the independent variable affects the dependent variable. The inverse, however, is not true. "Plant growth causes a change in light color," is not possible. This way you know which is the independent variable and which is the dependent variable!
What are constants & controls of a science project..., what are independent & dependent variables in science..., difference between manipulative & responding variable, how to collect data from a science project, science fair projects on plants: do they grow faster..., how to write a testable hypothesis, how to grow a plant from a bean as a science project, what is the role of carotenoids in photosynthesis, proper way to label a graph, two week science projects, why should you only test for one variable at a time..., definitions of control, constant, independent and dependent..., what is a constant in a science fair project, science projects on which fertilizer makes a plant..., venus flytrap science projects, the effect of temperature on the rate of photosynthesis, phototropism experiments, cool science project ideas for k-4th grade, measuring wet bulb temperature.
About the Author
Riti Gupta holds a Honors Bachelors degree in Biochemistry from the University of Oregon and a PhD in biology from Johns Hopkins University. She has an interest in astrobiology and manned spaceflight. She has over 10 years of biology research experience in academia. She currently teaches classes in biochemistry, biology, biophysics, astrobiology, as well as high school AP Biology and Chemistry test prep.
The independent variable, also known as the manipulated variable, is the factor manipulated by the researcher, and it produces one or more results, known as dependent variables .
Any factor that can take on different values in an experiment is a scientific variable.
For example, in an experiment investigating the effectiveness of a new training program, the variables might be:
Depending on how the researcher operationalizes all the variables in an experiment, the above could be either dependent or independent variables.
It’s the research design that decides which variables are manipulated and which are measured as a result of that manipulation.
The independent variable is "independent" because its variation does not depend on the variation of another variable in the experiment/research project. The independent variable is controlled or changed only by the researcher. This factor is often the research question/hypothesis behind the outcome of the experiment.
In the above example, the researcher may have wanted to see if participating in the training program raised students' scores on a final test.
Can you identify the independent variable in this experiment?
What do you think is correct? The answer is at the bottom of the article.
There are often not more than one or two independent variables tested in an experiment, otherwise it is difficult to determine the influence of each upon the final results. There may be several dependent variables , because manipulating the independent variable can influence many different things.
For example, an experiment to test the effects of a certain fertilizer on plant growth could measure height, number of fruits and the average weight of the fruit produced. All of these are valid analyzable factors arising from the manipulation of one independent variable, the amount of fertilizer.
The term independent variable is often a source of confusion; many people assume that the name means that the variable is independent of any manipulation. The name arises because the variable is isolated from any other factor, allowing experimental manipulation to establish analyzable results .
A useful acronym is DRY-MIX. This helps you remember which axis to plot your data should you need to draw a graph:
Some research papers appear to give results manipulating more than one experimental variable, but this is usually a false impression.
Each manipulated variable is likely to be an experiment in itself, one area where the words 'experiment' and 'research' differ. It is simply more convenient for the researcher to bundle them into one paper, and discuss the overall results.
The researcher above might also study the effects of temperature, or the amount of water on growth, but these must be performed as discrete experiments, with only the conclusion and discussion amalgamated at the end.
Jane elliott's anti-racism experiment.
Third grade teacher Jane Elliott’s famous experiment involved dividing her class into two groups: blue-eyed and brown-eyed children. She gave the blue-eyed children extra privileges and emphasized how superior they were to the brown-eyed, who were now a “minority group.”
As a result, the brown-eyed children saw a drop in confidence, academic performance and an increase in bullying. However, when she later labelled the blue-eyed group as the inferior, these effects were reversed.
Here, the independent variable was group status, i.e. whether the children where in the privileged group or not. This had various observable effects on the children. Importantly, the eye color of the children was not the independent variable here. Eye color was an arbitrary choice made by the teacher to draw parallels to racism and prejudice.
Can you identify a possible dependent variable in this experiment?
In the Bandura Bobo Doll experiment , whether the children were exposed to an aggressive adult, or to a passive adult, was the independent variable.
This experiment is a prime example of how the concept of experimental variables can become a little complex. Bandura also studied the differences between boys and girls, with gender as an independent variable. Surely, this is breaking the rules of only having one manipulated variable!
In fact, this is a prime example of performing multiple experiments at the same time. If you study the structure of the research design , you will see that the Bobo Doll Experiment should have been called the Bobo Doll Experiments.
It was actually four experiments, each with their own hypothesis and variables, running concurrently. It would have been expensive, and possibly unethical , to test the children four times and, if the same children were used each time, their behavior may have changed with repetition.
Careful design allowed Bandura to test different hypotheses as part of the same research.
Can you identify the separate independent variables in this experiment? Pick two.
The answer is at the bottom of the article.
Option 3. Participation on the training program .
The researcher could manipulate the variable of whether students participated on the program or not, then measure the results, for example their score on a final test.
Can you identify a possible dependent variable in this experiment?
Option 4. All of the above.
The experiment measured the children's overall behavior. But this could have been broken into separate dependent variables, for example academic performance, level of bullying, or confidence levels.
Can you identify the separate independent variables in this experiment? Pick two.
Option 2 and 3. The gender of the role models and the aggressiveness of the role models.
Bandura was interested to see if a child would imitate their role model, but he also wanted to see if a child was more likely to imitate them if they were of the same gender.
Martyn Shuttleworth , Lyndsay T Wilson (Feb 24, 2008). Independent Variable. Retrieved Sep 03, 2024 from Explorable.com: https://explorable.com/independent-variable
The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0) .
This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give appropriate credit and provide a link/reference to this page.
That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).
Get all these articles in 1 guide.
Want the full version to study at home, take to school or just scribble on?
Whether you are an academic novice, or you simply want to brush up your skills, this book will take your academic writing skills to the next level.
Download electronic versions: - Epub for mobiles and tablets - For Kindle here - For iBooks here - PDF version here
Don't have time for it all now? No problem, save it as a course and come back to it later.
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.
Notifications can be managed in browser preferences.
Please refresh the page or navigate to another page on the site to be automatically logged in Please refresh your browser to be logged in
Dogs could help scientists understand more about how animals other than humans retain their memories, article bookmarked.
Find your bookmarks in your Independent Premium section, under my profile
Sign up for our free weekly voices newsletter for expert opinion and columns, sign up to our free weekly voices newsletter, thanks for signing up to the independent voices email.
Certain dogs can remember the names of their toys for at least two years, scientists have found.
Previous research has shown these rare pooches, known as gifted word learners (GWL), have a unique ability to learn the names of hundreds of different objects.
A new study, published in the journal Biology Letters, now suggests they can remember the names of some of these toys for an extended period of time.
The hope is that the talented dogs could help scientists understand more about how animals other than humans retain their memories.
Dr Claudia Fugazza, the head of the research group at Eotvos Lorand University in Hungary, said: “We know that dogs can remember events for at least 24 hours and odours for up to one year but this is the first study showing that some talented dogs can remember words for at least two years.
“The findings of our current study cannot be generalised to other dogs because we only tested GWL dogs, individuals that show a special talent for acquiring object words.”
For the study, the researchers analysed the behaviour of five border collies: Gaia, Max, Whiskey, Squall and Rico.
These GWL dogs had learnt and remembered the names of multiple toys and were tested again two years on.
The researchers said that “remarkably” four out of five dogs remembered the names of between 60-75% of the toys after two years, with Gaia performing the best.
As a group, the dogs’ performance averaged at 44% correct choices, which is significantly above chance level, the team added.
Dr Shany Dror, lead researcher on the study at Eotvos Lorand University, said: “We waited two years and then decided to test the dogs again, to see if they still remembered the toy names.
“Because such a long time has passed some of the owners lost a few of the toys.
“Thus, three dogs were tested on 12 toys, one dog on 11 and one dog on five.
“After two years, we all had a hard time remembering the names of toys.
“But not the dogs! They did not seem to struggle.”
The research is part of a project known as the Genius Dog Challenge and the scientists are urging owners who believe their dogs know multiple toy names to contact them via the project’s website.
Join thought-provoking conversations, follow other Independent readers and see their replies
Want to bookmark your favourite articles and stories to read or reference later? Start your Independent Premium subscription today.
New to The Independent?
Or if you would prefer:
Hi {{indy.fullName}}
IMAGES
VIDEO
COMMENTS
The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.
Here are several examples of independent and dependent variables in experiments: In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to know which brand of fertilizer is best for your plants.
The 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.
Examples of Independent and Dependent Variables. 1. Gatorade and Improved Athletic Performance. A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.
The independent variable is the variable that is controlled or changed in a scientific experiment to test its effect on the dependent variable. It doesn't depend on another variable and isn't changed by any factors an experimenter is trying to measure. The independent variable is denoted by the letter x in an experiment or graph.
The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment. For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to ...
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 ...
The independent variable in your experiment would be the brand of paper towels. The dependent variable would be the amount of liquid absorbed by the paper towel. In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed (the ...
Independent variables cause changes in another variable. The researchers control the values of the independent variables. They are controlled or manipulated variables. Experiments often refer to them as factors or experimental factors. In areas such as medicine, they might be risk factors. Treatment and control groups are always independent ...
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 ...
Here are some examples of an independent variable. A scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light (cause) and the moth's reaction is the dependent variable (the effect). In a study to determine the effect of temperature on plant ...
Definition: Independent variable is a variable that is manipulated or changed by the researcher to observe its effect on the dependent variable. It is also known as the predictor variable or explanatory variable. The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome.
Independent and Dependent Variables, Explained With Examples. Written by MasterClass. Last updated: Mar 21, 2022 • 4 min read. In experiments that test cause and effect, two types of variables come into play. One is an independent variable and the other is a dependent variable, and together they play an integral role in research design.
The dependent variable (sometimes known as the responding variable) is what is being studied and measured in the experiment. It's what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).
As mentioned above, independent and dependent variables are the two key components of an experiment. Quite simply, the independent variable is the state, condition or experimental element that is controlled and manipulated by the experimenter. The dependent variable is what an experimenter is attempting to test, learn about or measure, and will ...
The independent variable is the drug, while the patient's blood pressure is the dependent variable. In some ways, this experiment resembles the one with breakfast and test scores. However, when comparing two different treatments, such as drug A and drug B, it's usual to add another variable, called the control variable.
The independent variable is the one you control, while the dependent variable depends on the independent variable and is the one you measure. The independent and dependent variables are the two main types of variables in a science experiment. A variable is anything you can observe, measure, and record. This includes measurements, colors, sounds ...
The " variables " are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment. An experiment can have three kinds of variables: i ndependent, dependent, and controlled. The independent variable is one single factor that is changed by the scientist followed by ...
References. About the Author. In an experiment, there are multiple kinds of variables: independent, dependent and controlled variables. The independent variable is the one the experimenter changes. The dependent variable is what changes in response to the independent variable. Controlled variables are conditions kept the same.
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 the experiment in question.
The independent variable is "independent" because its variation does not depend on the variation of another variable in the experiment/research project. The independent variable is controlled or changed only by the researcher. This factor is often the research question/hypothesis behind the outcome of the experiment.
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. ... [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 ...
Our mission is to deliver unbiased, fact-based reporting that holds power to account and exposes the truth. Whether $5 or $50, every contribution counts. Certain dogs can remember the names of ...