what is the dependent variable in a plant experiment

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Independent and Dependent Variables Examples

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

Independent Variable

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

Dependent Variable

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

Examples of Independent and Dependent Variables

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

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

How to Tell the Independent and Dependent Variable Apart

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

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

How to Graph Independent and Dependent Variables

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

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

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Experimental Design - Independent, Dependent, and Controlled Variables

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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 .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

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.

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Methodology

• Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

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

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

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

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

Table of contents

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

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

Independent variables are also called:

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

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

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There are two main types of independent variables.

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

Experimental variables

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

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

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

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

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

• A low-dose experimental group
• A high-dose experimental group
• A placebo group (to research a possible placebo effect )

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

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

Subject variables

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

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

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

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

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

In statistics , dependent variables are also called:

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

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

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

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

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

Here are some tips for identifying each variable type.

Recognizing independent variables

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

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

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

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

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

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

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

The type of test is determined by:

• your variable types
• level of measurement
• number of independent variable levels.

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

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

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

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

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

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

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

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

In statistics, dependent variables are also called:

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

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

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

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

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

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

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

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

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Independent and Dependent Variables: Which Is Which?

General Education

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

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

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

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

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

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

Dependent variable: What is being studied/measured.

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

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

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

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

Examples of Independent and Dependent Variables in Experiments

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

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

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

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

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

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

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

For each of the independent variables above, it's clear that they can't be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.

Where Do You Put Independent and Dependent Variables on Graphs?

Independent and dependent variables always go on the same places in a graph. This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis.

Here's an example:

As you can see, this is a graph showing how the number of hours a student studies affects the score she got on an exam. From the graph, it looks like studying up to six hours helped her raise her score, but as she studied more than that her score dropped slightly.

The amount of time studied is the independent variable, because it's what she changed, so it's on the x-axis. The score she got on the exam is the dependent variable, because it's what changed as a result of the independent variable, and it's on the y-axis. It's common to put the units in parentheses next to the axis titles, which this graph does.

There are different ways to title a graph, but a common way is "[Independent Variable] vs. [Dependent Variable]" like this graph. Using a standard title like that also makes it easy for others to see what your independent and dependent variables are.

Are There Other Important Variables to Know?

Independent and dependent variables are the two most important variables to know and understand when conducting or studying an experiment, but there is one other type of variable that you should be aware of: constant variables.

Constant variables (also known as "constants") are simple to understand: they're what stay the same during the experiment. Most experiments usually only have one independent variable and one dependent variable, but they will all have multiple constant variables.

For example, in Experiment 2 above, some of the constant variables would be the type of plant being grown, the amount of fertilizer each plant is given, the amount of water each plant is given, when each plant is given fertilizer and water, the amount of sunlight the plants receive, the size of the container each plant is grown in, and more. The scientist is changing the type of fertilizer each plant gets which in turn changes how much each plant grows, but every other part of the experiment stays the same.

In experiments, you have to test one independent variable at a time in order to accurately understand how it impacts the dependent variable. Constant variables are important because they ensure that the dependent variable is changing because, and only because, of the independent variable so you can accurately measure the relationship between the dependent and independent variables.

If you didn't have any constant variables, you wouldn't be able to tell if the independent variable was what was really affecting the dependent variable. For example, in the example above, if there were no constants and you used different amounts of water, different types of plants, different amounts of fertilizer and put the plants in windows that got different amounts of sun, you wouldn't be able to say how fertilizer type affected plant growth because there would be so many other factors potentially affecting how the plants grew.

3 Experiments to Help You Understand Independent and Dependent Variables

If you're still having a hard time understanding the relationship between independent and dependent variable, it might help to see them in action. Here are three experiments you can try at home.

Experiment 1: Plant Growth Rates

One simple way to explore independent and dependent variables is to construct a biology experiment with seeds. Try growing some sunflowers and see how different factors affect their growth. For example, say you have ten sunflower seedlings, and you decide to give each a different amount of water each day to see if that affects their growth. The independent variable here would be the amount of water you give the plants, and the dependent variable is how tall the sunflowers grow.

Experiment 2: Chemical Reactions

Explore a wide range of chemical reactions with this chemistry kit . It includes 100+ ideas for experiments—pick one that interests you and analyze what the different variables are in the experiment!

Experiment 3: Simple Machines

Build and test a range of simple and complex machines with this K'nex kit . How does increasing a vehicle's mass affect its velocity? Can you lift more with a fixed or movable pulley? Remember, the independent variable is what you control/change, and the dependent variable is what changes because of that.

Quiz: Test Your Variable Knowledge

Can you identify the independent and dependent variables for each of the four scenarios below? The answers are at the bottom of the guide for you to check your work.

Scenario 1: You buy your dog multiple brands of food to see which one is her favorite.

Scenario 2: Your friends invite you to a party, and you decide to attend, but you're worried that staying out too long will affect how well you do on your geometry test tomorrow morning.

Scenario 3: Your dentist appointment will take 30 minutes from start to finish, but that doesn't include waiting in the lounge before you're called in. The total amount of time you spend in the dentist's office is the amount of time you wait before your appointment, plus the 30 minutes of the actual appointment

Scenario 4: You regularly babysit your little cousin who always throws a tantrum when he's asked to eat his vegetables. Over the course of the week, you ask him to eat vegetables four times.

Summary: Independent vs Dependent Variable

Knowing the independent variable definition and dependent variable definition is key to understanding how experiments work. The independent variable is what you change, and the dependent variable is what changes as a result of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

When graphing these variables, the independent variable should go on the x-axis (the horizontal axis), and the dependent variable goes on the y-axis (vertical axis).

Constant variables are also important to understand. They are what stay the same throughout the experiment so you can accurately measure the impact of the independent variable on the dependent variable.

What's Next?

Independent and dependent variables are commonly taught in high school science classes. Read our guide to learn which science classes high school students should be taking.

Scoring well on standardized tests is an important part of having a strong college application. Check out our guides on the best study tips for the SAT and ACT.

Interested in science? Science Olympiad is a great extracurricular to include on your college applications, and it can help you win big scholarships. Check out our complete guide to winning Science Olympiad competitions.

Quiz Answers

1: Independent: dog food brands; Dependent: how much you dog eats

2: Independent: how long you spend at the party; Dependent: your exam score

3: Independent: Amount of time you spend waiting; Dependent: Total time you're at the dentist (the 30 minutes of appointment time is the constant)

4: Independent: Number of times your cousin is asked to eat vegetables; Dependent: number of tantrums

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

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Biology archive

Course: biology archive   >   unit 1.

• The scientific method

Controlled experiments

• The scientific method and experimental design

Introduction

How are hypotheses tested.

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍   and coral bleaching.

• What your control and experimental groups would be
• What your independent and dependent variables would be
• What results you would predict in each group

Experimental setup

• Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍   around 8.2 ‍   ). The corals in these tanks served as the control group .
• Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍   . One set of tanks was medium-acidity ( pH ‍   about 7.9 ‍   ), while another set was high-acidity ( pH ‍   about 7.65 ‍   ). Both the medium-acidity and high-acidity groups were experimental groups .
• In this experiment, the independent variable was the acidity ( pH ‍   ) of the seawater. The dependent variable was the degree of bleaching of the corals.
• The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍   fragments of coral, and there were 5 ‍   identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍   values were all above the neutral pH ‍   of 7.0 ‍   . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

• Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
• Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
• University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
• Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

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Statistics By Jim

Making statistics intuitive

Independent and Dependent Variables: Differences & Examples

By Jim Frost 15 Comments

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

What is an Independent Variable?

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

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

In machine learning, independent variables are known as features.

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

Statistical models will estimate effect sizes for the independent variables.

Relate post : Effect Sizes in Statistics

Including independent variables in studies

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

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

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

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

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

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

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

What is a Dependent Variable?

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

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

How to Identify Independent and Dependent Variables

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

Identifying IVs

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

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

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

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

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

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

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

Identifying DVs

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

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

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

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

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

How Analyses Use IVs and DVs

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

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

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

Related post : When to Use Regression Analysis

Graphing Independent and Dependent Variables

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

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

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

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

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

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Reader Interactions

April 2, 2024 at 2:05 am

Hi again Jim

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

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

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

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

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

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

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

Regards Kathy Spencer

April 1, 2024 at 4:08 pm

Hi Jim, Great website, thank you.

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

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

Any comment?

Many thanks Kathy Spencer

April 1, 2024 at 9:20 pm

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

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

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

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

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

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

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

February 6, 2024 at 6:57 pm

Excellent explanation, thank you.

February 5, 2024 at 5:04 pm

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

February 5, 2024 at 11:11 pm

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

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

February 6, 2024 at 6:32 am

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

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

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

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

February 6, 2024 at 2:17 pm

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

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

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

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

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

I hope that helps!

September 6, 2022 at 7:43 pm

Thank you for sharing.

March 3, 2022 at 1:59 am

Excellent explanation.

February 13, 2022 at 12:31 pm

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

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

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

October 28, 2021 at 12:55 pm

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

October 29, 2021 at 11:22 pm

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

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

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

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

September 6, 2021 at 8:35 am

nice explanation

August 25, 2021 at 3:09 am

it is excellent explanation

Comments and Questions Cancel reply

Independent vs. Dependent Variables: What’s the Difference?

In an experiment, there are two main variables:

The independent variable:  the variable that an experimenter changes or controls so that they can observe the effects on the dependent variable.

The dependent variable:  the variable being measured in an experiment that is “dependent” on the independent variable.

In an experiment, an experimenter is interested in seeing how the dependent variable changes as a result of the independent being changed or manipulated in some way.

Example of an Independent and Dependent Variable

For example, a researcher might change the amount of water they provide to a certain plant to observe how it affects the growth rate of the plant.

In this example, the amount of water given to the plant is controlled by the researcher and, thus, is the independent variable . The growth rate is the dependent variable because it is directly dependent on the amount of water that the plant receives and it’s the variable we’re interested in measuring.

How to Remember the Difference Between Independent and Dependent Variables

An easy way to remember the difference between independent and dependent variables is to insert the two variables into the following sentence in such a way that it makes sense:

Changing (independent variable) affects the value of (dependent variable) .

For example, it would make sense to say:

Changing the amount of water  affects the value of  the plant growth rate .

This is how we know that amount of water is the independent variable and plant growth rate is the dependent variable.

If we tried reversing the positions of these two variables, the sentence wouldn’t make sense:

Changing the plant growth rate  affects the value of  the amount of water .

Thus, we know that we must have the independent and dependent variables switched around.

More Examples 

Here are a few more examples of independent and dependent variables.

A marketer changes the amount of money they spend on advertisements to see how it affects total sales.

Independent variable:  amount spent on advertisements

Dependent variable:  total sales

A doctor changes the dose of a particular medicine to see how it affects the blood pressure of a patient.

Independent variable:  dosage level of medicine

Dependent variable:  blood pressure

A researcher changes the version of a study guide given to students to see how it affects exam scores.

Independent variable:  the version of the study guide

Dependent variable:  exam scores

Independent vs. Dependent Variables on a Graph

When we create a graph, the independent variable will go on the x-axis and the dependent variable will go on the y-axis.

For example, suppose a researcher provides different amounts of water for 20 different plants and measures the growth rate of each plant. The following scatterplot shows the amount of water and the growth rate for each plant:

The independent variable (amount of water) is shown on the x-axis while the dependent variable (growth rate) is shown on the y-axis:

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

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

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What Is a Dependent Variable?

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A dependent variable is the variable that is tested and measured in a scientific experiment. It is sometimes called the responding variable.

The dependent variable gets its name from the fact that it depends on the independent variable. As the experimenter manipulates the independent variable, a change in the dependent variable is observed and recorded.

Dependent Variable Examples

Imagine that a scientist is testing the effect of light and dark on the behavior of moths by switching a light on and off. The independent variable is the amount of light, and the dependent variable is the moths' reaction. A change in the independent variable (amount of light) directly causes a change in the dependent variable (moth behavior).

Another example of a dependent variable is a test score. How well you perform on a test depends on other variables, such as how much you studied, the amount of sleep you had the night before, whether you had breakfast that morning, and so on. The manipulation of these independent variables has an effect on the dependent variable (the test score).

In general, if you are studying the effect of a certain factor or the outcome of an experiment, the effect or outcome is the dependent variable. If you measure the effect of temperature on flower color, temperature is the independent variable—the one you manipulate—while the color of the flower is the dependent variable.

Graphing a Dependent Variable

When independent and dependent variables are plotted on a graph, the independent variable is usually measured along the x-axis and the dependent variable along the y-axis. For example, if you were examining the effect of sleep on test scores, the number of hours participants slept would be plotted along the x-axis, while the test scores they earned would be plotted along the y-axis.

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Dependent & Independent Variables in Science Experiments

by Chloe Campbell Leave a Comment

Understanding how variables in science experiments work is an important skill for our students to understand. We do a lot of science experiments in my classroom, and knowing how different factors can change the outcome of a scientific experiment is always something I want them to be able to discover and explain. It’s also great practice for the scientific method. Here are some of the best ways to teach dependent and independent variables in your science classroom.

​VARIABLES IN SCIENCE EXPERIMENTS: WHAT ARE THEY?

Here are definitions you can use with your students, using a plant growth experiment as a base:

• Example: If you are testing how different amounts of water affect plant growth, the amount of water is the independent variable because it’s what you change in your experiment.
• Example: In the plant experiment, the growth of the plant is the dependent variable because it’s what you measure to see how much the plant has grown based on the different amounts of water.

My  Independent and Dependent Variables Resource has a foldable, interactive vocabulary activity that helps students understand the concept of variables.  In the resource, students also define what control variables are.

​It’s important for our students to know the variable that we are changing and the variables that occur because of that one change. It’s also  so  important to make sure the kids understand how important changing only one thing is. We need to know what caused the outcome of the experiment, and that’s difficult if we change different variables.

DESIGNING EXPERIMENTS

Once students understand what variables are, we need to help them put this new vocabulary into action. That’s where experiments come in! I like to start with a premade experiment that guide students through how variables work in a real-world context. An easy experiment that I like to use with my students is  W hat Will Make Ice Melt the Fastest? . Students work with three different materials that we have on hand in class, and they predict which substance will make ice melt the fastest. I like to use sand, water, salt, sugar, or anything similar. I also make sure students know we need a control group to see what happens when no substance is applied to the ice.

FOCUS ON THE VARIABLES

Students can sometimes get lost in the steps of an experiment and forget what brought the results about. For this reason, I make sure that my students can communicate to each other what the variables were and, more importantly,  why  each variable exists. For example, in the plant growth experiment, the goal is for my students to be able to explain that:

• the independent variable is the amount of water we’re using, because we are changing the amount on purpose;
• the dependent variable is the plant’s growth, because that will change based on the water we give it;
• the controlled variables are anything we don’t intend to change, which in this case could be the type of soil used, the type of plant used, the amount of light each plant gets, the type of liquid (we always use the same tap water), and so on.

To keep the focus even stronger, the students know that their exit ticket for the class will be for them to explain what an independent, dependent, and controlled variable is. You can have students define in it general, or you can have them provide examples based on the results of the experiment.

ANALYZE THE DATA

Once my students have correctly identified the different types of variables in an experiment, we analyze the data we collected. I want them to understand, and then be able to explain to someone else, how the independent variable affects the dependent variable. For example, in my  What Will Make Ice Melt the Fastest?   lab, students conclude that the salt melted the ice fastest. The constant variables were anything we didn’t change, such as how long we timed them melting and the temperature of the room. The final outcome of an experiment is important, and knowing the why behind the outcome is important too.

Understanding these variables helps students design good experiments and understand the results better when they go off and create their own scientific investigations. When our students know what we are changing (independent variable) and what we are measuring (dependent variable), they can make better observations and conclusions. Being able to analyze the results of an experiment is a great critical thinking developer, and students pick up scientific inquiry skills they can use throughout the year.

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Dependent Variables (Definition + 30 Examples)

Welcome to a journey through the essential world of dependent variables! Whether you’re an avid learner, a seasoned researcher, or simply curious, unraveling the mysteries of dependent variables is crucial for making sense of scientific discoveries and everyday wonders.

A dependent variable is what we observe and measure in an experiment. It's called "dependent" because it changes based on the alterations we make to another variable, known as the independent variable. Think of it as a series of revealing clues, shedding light on the story of how one thing can affect another.

Embark with us on an enlightening adventure, as we delve into the significance of dependent variables, explore their relationship with independent variables, and uncover how they help us interpret and shape the world around us.

History of Dependent Variables

The concept of dependent variables finds its roots in the early foundations of scientific thought.

The ancient Greeks, notably Aristotle , laid down the groundwork for systematic observation and the study of cause and effect. Aristotle's ideas on causality, although different from today’s understanding, were pivotal in shaping the way we approach scientific inquiry.

Emergence of Experimental Science

The Renaissance period marked a significant shift in scientific thinking. Pioneers like Galileo Galilei and Sir Francis Bacon advocated for empirical observation and experimentation.

This period saw the emergence of experimental science, where the relationships between different variables, including dependent and independent ones, were systematically studied.

Development of Statistical Methods

The 18th and 19th centuries witnessed the development of statistical methods , which played a crucial role in understanding dependent variables.

Sir Francis Galton, a cousin of Charles Darwin, made significant contributions to the field of statistics and introduced the concept of regression, a foundational element in studying dependent variables.

Modern Day Applications

Today, the concept of dependent variables is integral to research across diverse fields, from biology and physics to psychology and economics. The evolution of research methodologies and statistical tools has allowed scientists and researchers to study dependent variables with increased precision and insight.

Conclusion on Origins

Understanding the origin of dependent variables offers a fascinating glimpse into the evolution of scientific thought and the relentless human pursuit of knowledge.

From the musings of ancient philosophers to the sophisticated research of today, dependent variables have journeyed through time, contributing to the rich tapestry of scientific discovery and progress.

What are Dependent Variables?

Understanding dependent variables is like piecing together a puzzle – it’s essential for seeing the whole picture! Dependent variables are at the core of scientific experiments, acting as the outcomes we observe and measure.

They respond to the changes we make in the independent variables , helping us unravel the connections and relationships between different elements in an experiment .

Dependent Variables in Scientific Experiments

In the realm of scientific experiments, dependent variables play the starring role of the outcome. When scientists alter something, the dependent variable is what reacts to this change.

For instance, if a botanist is examining how different amounts of sunlight (the independent variable) affect plant growth, the growth of the plant is the dependent variable.

Relationship with Independent Variables

Dependent variables and independent variables share a unique dance in the world of science. The independent variable leads, changing and altering, while the dependent variable follows, reacting and showing the effects of these changes.

It’s this intricate relationship that allows scientists and researchers to draw conclusions and make discoveries.

Making Observations and Drawing Conclusions

Observing dependent variables is like watching a story unfold. By carefully measuring and recording how they respond to changes, scientists can draw meaningful conclusions and answer pressing questions.

Whether it’s understanding how temperature affects sea levels or how diet influences health, dependent variables are the narrators of these scientific stories.

But remember, experimenters make errors, and sometimes those errors are based on their biases, or what they want to find or believe they will find, so keeping the variables in check is one way to avoid experimenter bias .

Real-World Applications

The insights gained from studying dependent variables don’t just stay in the lab – they ripple out into the real world!

From developing new medicines to improving educational techniques, understanding dependent variables is pivotal. They help us make informed decisions, solve problems, and enhance the quality of life for people around the globe.

Everyday Examples

In our everyday lives, we encounter countless instances of dependent variables.

When you adjust the brightness of your room to see how well you can read a book, the readability is your dependent variable.

Or, when a chef experiments with ingredients to observe the flavor of a dish, the taste is the dependent variable.

The Impact on Knowledge

Dependent variables are the building blocks of knowledge. They help us test hypotheses, validate theories, and expand our understanding of the universe.

Every observation, every measurement, brings us one step closer to unraveling the mysteries of the world and advancing human knowledge.

By grasping the role of dependent variables, we open doors to a myriad of possibilities, uncovering the secrets of the natural world and contributing to the rich tapestry of scientific discovery.

Dependent Variables in Research

Diving deeper into the realm of dependent variables, we uncover why they hold such an important role in the tapestry of scientific discovery and everyday life.

These variables are the storytellers, the revealers of effects, and the markers of change, helping us navigate the sea of knowledge and make waves of progress.

Scientific Discovery and Innovation

In the laboratory of discovery, dependent variables are the guiding stars. They help scientists and researchers observe the effects of changes, leading to breakthroughs and innovations.

Whether it’s finding a cure for a disease, inventing a new technology, or understanding the mysteries of the universe, dependent variables are at the heart of the eureka moments that shape our world.

Real-World Problem Solving

Outside the lab, the insights gained from dependent variables illuminate the path to solving real-world problems.

They play a crucial role in improving healthcare, education, environmental conservation, and numerous other fields, enabling us to develop solutions that enhance well-being and sustainability.

By understanding how dependent variables react, we can tailor strategies to address challenges and create a positive impact.

Informing Decision-Making

Every day, we make countless decisions, big and small. Dependent variables are like compasses, guiding our choices and actions.

Whether deciding on the best method to grow a garden, choosing a fitness routine, or selecting the right ingredients for a recipe, recognizing the dependent variables helps us make informed and effective decisions to achieve our goals.

Enhancing Understanding and Knowledge

The study of dependent variables enriches our comprehension of the world around us. They provide insights into cause and effect, helping us understand how different elements interact and influence each other.

This deepened understanding broadens our knowledge, fuels our curiosity, and inspires further exploration and learning.

Fostering Curiosity and Exploration

Peeling back the layers of dependent variables uncovers a world of wonder and curiosity. They invite us to ask questions, seek answers, and explore the intricate web of relationships in the natural and social world.

This sense of wonder and exploration drives scientific inquiry and fosters a lifelong love of learning and discovery.

Conclusion on Importance

The importance of dependent variables cannot be overstated. They are the keys that unlock the doors of understanding, the catalysts for innovation and progress, and the guides on our journey through the ever-evolving landscape of knowledge.

As we continue to explore and learn, the role of dependent variables remains central to our quest for understanding and discovery.

Challenges with Dependent Variables

While dependent variables illuminate the path of discovery, working with them can sometimes feel like navigating a labyrinth.

It’s essential to recognize the challenges and considerations that come with the territory, ensuring accurate, reliable, and meaningful outcomes in our pursuit of knowledge.

Measurement Accuracy

In the world of dependent variables, accuracy is king. Measuring outcomes precisely is crucial to avoid distorting the picture. Imagine trying to solve a puzzle with misshaped pieces – it wouldn’t fit together right! Ensuring accurate measurement means the story told by the dependent variable is true to reality.

External Influences

Sometimes, unseen forces can influence our dependent variables. These are called confounding variables , and they can sneak in and alter the outcomes, like a gust of wind turning the pages of a book.

Being aware of and controlling these external influences is essential to maintain the integrity of our observations and conclusions.

Consistency and Reliability

Consistency is the heartbeat of reliable results. When working with dependent variables, it’s vital to maintain consistent methods of measurement and observation. This consistency ensures that the story revealed is trustworthy and that the insights gained can be the foundation for further discovery and understanding.

Ethical Considerations

Exploring dependent variables also brings us face to face with ethical considerations . Whether it’s respecting privacy, ensuring safety, or acknowledging rights, it’s paramount to navigate the journey with integrity and responsibility. Ethical practices build trust and uphold the values that guide the pursuit of knowledge.

Varied Contexts and Applications

Dependent variables are versatile storytellers, revealing different tales in varied contexts and applications. Recognizing the diversity in application and interpretation is like tuning into different genres of stories – each holds unique insights and contributes to the richness of our understanding.

Reflection on Challenges and Considerations

Understanding and addressing the challenges and considerations in working with dependent variables is like sharpening the tools in our scientific toolbox. It strengthens the foundation of our exploration, ensuring that the journey is fruitful, the discoveries are genuine, and the stories told are authentic.

Famous Studies Involving Dependent Variables

The stage of scientific discovery has been graced by numerous studies and experiments where dependent variables played a starring role. These studies have shaped our understanding, answered profound questions, and paved the way for further exploration and innovation.

Ivan Pavlov’s Classical Conditioning

In the early 20th century, Ivan Pavlov ’s experiments with dogs shone a spotlight on dependent variables. He observed how dogs (the dependent variable) salivated in response to the sound of a bell (the independent variable), leading to groundbreaking insights into classical conditioning and learning.

Sir Isaac Newton’s Laws of Motion

Delving back into the 17th century, Sir Isaac Newton ’s exploration of the laws of motion involved observing how objects (the dependent variables) moved and interacted in response to forces (the independent variables). His work laid the foundations of classical mechanics and continues to influence science today .

Gregor Mendel’s Pea Plant Experiments

In the 19th century, Gregor Mendel ’s work with pea plants opened the doors to the world of genetics. By observing the traits of pea plants (the dependent variables) in response to different genetic crosses (the independent variables), Mendel unveiled the principles of heredity .

The Stanford Prison Experiment

In 1971, the Stanford Prison Experiment , led by Philip Zimbardo , explored the effects of perceived power and authority. The behavior of participants (the dependent variable) was observed in response to assigned roles as guards or prisoners (the independent variable), revealing insights into human behavior and ethics.

The Hawthorne Effect

In the 1920s and 1930s, studies at the Western Electric Hawthorne Works in Chicago observed worker productivity (the dependent variable) in response to changes in working conditions (the independent variables). This led to the discovery of the Hawthorne Effect , highlighting the influence of observation on human behavior.

Reflection on Famous Studies

These famous studies and experiments spotlight the pivotal role of dependent variables in scientific discovery. They illustrate how observing and measuring dependent variables have expanded our knowledge, led to breakthroughs, and addressed fundamental questions about the natural and social world.

Examples of Dependent Variables

1) test scores.

In an educational setting, student test scores often serve as a dependent variable to measure academic achievement.

2) Heart Rate

In health and exercise science, heart rate can be a dependent variable indicating cardiovascular response to activity.

3) Plant Growth

In botany, the growth of plants can be observed as a dependent variable when studying the effects of different environmental conditions.

4) Sales Revenue

In business, sales revenue may be a dependent variable analyzed in relation to advertising strategies.

5) Blood Pressure

In medicine, blood pressure levels can be a dependent variable to study the effects of medication or diet.

6) Job Satisfaction

In organizational psychology, job satisfaction levels of employees may be the dependent variable.

7) Ice Melt Rate

In climate studies, the rate at which ice melts can be a dependent variable in relation to temperature changes.

8) Customer Satisfaction

In service industries, customer satisfaction levels are often the dependent variable.

9) Reaction Time

In psychology, an individual's reaction time can be measured as a dependent variable in cognitive studies.

10) Fuel Efficiency

In automotive studies, the fuel efficiency of a vehicle may be the dependent variable.

11) Population Size

In ecology, the size of animal or plant populations can be a dependent variable.

12) Productivity Levels

In the workplace, employee productivity can be observed as a dependent variable.

13) Immune Response

In immunology, the body’s immune response can be the dependent variable when studying vaccines or infections.

14) Enzyme Activity

In biochemistry, the activity levels of enzymes can be measured as a dependent variable.

15) Market Share

In business, a company’s market share can be the dependent variable in relation to competition strategies.

16) Voter Turnout

In political science, voter turnout can be a dependent variable studied in relation to campaign efforts.

17) Concentration Levels

In cognitive studies, individual concentration levels can be measured as a dependent variable.

18) Pollution Levels

In environmental science, levels of pollution can be a dependent variable in relation to industrial activity.

19) Reading Comprehension

In education, students’ reading comprehension can be the dependent variable.

20) Muscle Strength

In kinesiology, an individual’s muscle strength can be measured as a dependent variable.

21) Website Traffic

In digital marketing, the traffic a website receives can be the dependent variable.

22) Patient Recovery Time

In healthcare, the recovery time of patients can be observed as a dependent variable.

23) Student Attendance

In education, student attendance rates can be a dependent variable.

24) Rainfall Amounts

In meteorology, the amount of rainfall can be a dependent variable.

25) Consumer Spending

In economics, consumer spending levels can be observed as a dependent variable.

26) Energy Consumption

In energy studies, the amount of energy consumed can be a dependent variable.

27) Body Mass Index (BMI)

In health studies, an individual’s BMI can be measured as a dependent variable.

28) Employee Retention

In human resources, employee retention rates can be the dependent variable.

29) Water Quality

In environmental studies, the quality of water can be observed as a dependent variable.

30) Customer Loyalty

In business, customer loyalty can be a dependent variable in relation to brand reputation and service quality.

These examples illustrate the diverse nature of dependent variables and how they are used to measure outcomes across a multitude of disciplines and scenarios.

Real-World Examples of Dependent Variables

Dependent variables are not just confined to textbooks; they dance through our daily lives, telling tales of change and effect. Let’s take a closer look at some real-life scenarios where dependent variables play a key role in telling the story of cause and effect.

In healthcare, dependent variables help doctors and researchers understand the effects of treatments and interventions.

For example, a patient’s blood sugar level is a dependent variable when studying the effectiveness of diabetes medication. Monitoring this variable helps healthcare professionals tailor treatments and manage health conditions effectively.

In the realm of education, dependent variables like test scores and attendance rates help educators gauge the effectiveness of teaching methods and interventions.

By observing these variables, teachers can adapt their strategies to enhance student learning and well-being.

Environmental Conservation

In the world of environmental conservation, dependent variables such as animal population sizes and pollution levels provide insights into the impact of conservation efforts.

These observations guide strategies to protect ecosystems and biodiversity, ensuring a harmonious balance between humans and nature.

Technology and Innovation

In the field of technology and innovation, dependent variables like user engagement and product performance are crucial in developing and refining groundbreaking technologies.

Observing these variables enables innovators to optimize designs, improve user experiences, and drive progress in the digital age.

Fitness and Well-being

In the pursuit of fitness and well-being, dependent variables such as muscle strength and heart rate are observed to measure the effects of different exercise routines and dietary choices.

These observations guide individuals in achieving their health and fitness goals, fostering a sense of well-being and vitality.

Social Sciences

In social sciences, dependent variables like voter turnout and job satisfaction offer insights into human behavior and societal dynamics. Studying these variables helps researchers and policymakers understand societal trends, human motivations, and the intricate tapestry of social interactions.

Business and Economics

In the business and economic landscape, dependent variables such as sales revenue and consumer spending reveal the effectiveness of marketing strategies and economic policies.

Analyzing these variables helps businesses and governments make informed decisions, fueling economic growth and prosperity.

Culinary Arts

In culinary arts, dependent variables like taste and texture are observed to perfect recipes and culinary creations. Chefs experiment with ingredients and cooking techniques, using the feedback from these variables to craft delightful culinary experiences.

Arts and Entertainment

In arts and entertainment, audience reception and ticket sales are dependent variables that offer insights into the appeal of creative works. Artists and creators use this feedback to hone their craft, create meaningful connections with the audience, and contribute to the rich tapestry of culture and creativity.

Conclusion on Real-Life Applications

Exploring the real-life scenarios and applications of dependent variables brings to light the omnipresence and significance of these variables in shaping our world.

From healthcare to the arts, understanding and observing dependent variables enable us to learn, adapt, and thrive in a constantly evolving environment.

Identifying Dependent Variables

Spotting a dependent variable might seem like looking for a needle in a haystack, but with the right tools and know-how, it becomes a fascinating treasure hunt!

Knowing how to identify dependent variables is essential whether you’re conducting an experiment, analyzing data, or just curious about the relationships between different factors.

To be a true dependent variable detective, let’s revisit its definition: a dependent variable is what we measure in an experiment and what changes in response to the independent variable. It’s like the echo to a shout, the reaction to an action.

Relationship with Changes

In the dance of variables, the dependent variable is the one that responds. When something is tweaked, adjusted, or altered (that’s the independent variable), the dependent variable is what shows the effect of those changes. It’s the piece of the puzzle that helps us see the bigger picture.

Tips and Tricks for Identification

Identifying dependent variables can be a breeze with a few handy tips!

First, ask yourself, “What am I measuring or observing?” This is usually your dependent variable.

Next, look for the effect or change that is happening as a result of manipulating something else.

If you’re still unsure, try to phrase your observation as “If we change X, then Y will respond.” Y is typically the dependent variable.

Practice Makes Perfect: Scenarios

Let’s put our knowledge to the test! Can you spot the dependent variables in these scenarios?

• Cooking Time: You’re experimenting with cooking times to see how soft the cookies become.
• Exercise Routine: Trying out different types of exercise routines to see which one increases your stamina the most.
• Plant Fertilizer: Applying different types of fertilizers to your tomato plants to observe which one produces the juiciest tomatoes.
• Study Environment: Testing various study environments to identify which one improves your focus and learning.
• Sleep Duration: Adjusting the number of hours you sleep to observe its impact on your energy level the next day.

Answers and Explanation

Got your answers ready? Let’s see how you did!

• Cooking Time: The softness of the cookies is the dependent variable.
• Exercise Routine: The increase in stamina is what you are measuring, making it the dependent variable.
• Plant Fertilizer: The juiciness of the tomatoes is the dependent variable here.
• Study Environment: Your focus and learning are the dependent variables in this scenario.
• Sleep Duration: The energy level the next day is your dependent variable.

Identifying dependent variables is a skill that sharpens with practice, helping us unravel the wonders of cause and effect in the world around us.

Final Thoughts on Identification

Mastering the art of identifying dependent variables is like gaining a superpower. It allows us to see the world through a lens of relationships and effects, deepening our understanding of how changes in one element can impact another.

In the intricate dance of cause and effect, dependent variables tell tales of outcomes, changes, and responses. From the realm of science to the canvas of art, they shape our understanding of the world and drive progress in countless fields.

The challenges faced in measuring these variables only add layers to their complexity, but the pursuit of knowledge and the joy of discovery make every step of the journey worthwhile.

As we conclude our exploration of dependent variables, we leave with a sense of wonder and curiosity, equipped with the knowledge to observe, question, and explore the world around us.

The stories of dependent variables continue to unfold, and the adventure of learning and discovery is boundless.

Thank you for joining us on this enlightening journey through the world of dependent variables. Keep exploring, stay curious, and continue to marvel at the wonders of the world we live in!

Related posts:

• Independent Variables (Definition + 43 Examples)
• Confounding Variable in Psychology (Examples + Definition)
• Positive Correlation (Meaning + 39 Examples + Quiz)
• 19+ Experimental Design Examples (Methods + Types)
• 45+ Negative Correlation Examples (Definition + Use-cases)

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. A dependent variable is something that changes BECAUSE of the independent variable. In other words, The dependent variable is usually what scientists measure in an experiment. For example, let’s say you wanted to do an experiment to see what things make plants grow taller. You predict that the amount of water a plant receives will affect its height. So you design an experiment where you plant two seeds: one seed will be watered every single day, and the other seed will be watered only once a week. You measure the height of the plant every week for a few months, and at the end of the experiment you will see which plant is taller. In this case, because that is what you're changing in the experiment: one seed gets a lot of water and the other seed only gets a little water. , because you predict that the height is going to change DEPENDING on how much water the plant receives.

An can mean a lot of different things. In math, it usually refers to The word "independent" generally always means just that - think about the July 4th holiday here in America - Its called "Independence Day", where we decided we did not want to depend on Great Britain for help anymore. So lets say you have the equation in math: y = x + 2 in this case, for its value, and depending on what x is, will always be 2 more than that. So in this case, , because it doesn't depend on y to decide what it is. Hope that helps!

Thanks for the great question. One way that scientists learn things about the world is by doing experiments. Scientists like experiments because it allows them to carefully control and measure important things they want to study. In an experiment, The Typically, the scientist wants the independent variable to change the dependent variable. Let’s think of an example. Imagine you have made a new drug that you think will make people’s memory better. To show that the drug works, you have to run an experiment. , as this is under your control. If those who took the drug remember things better than those who did not, the drug works! Thanks again for your question!

When conducting an experiment or study, we are usually interested in the relationships among these variables. These relationships determine if a variable is . An . On the other hand, a . For example, we are interested in how your height changes with time. In this study, there are 2 variables, height and time. You get taller as time passes. However, time does not pass because you get taller. Time will pass even when you stop growing.

When you make a If one axis is time, it's always the X-axis, the independent variable. For example, you might measure how tall your plant is every day or every week. I've attached a - on the X-axis it has how many hours students studied for an exam. On the Y-axis, it has their score on the exam. I don't think the graph shows real data - I think it's just an example of independent and dependent variables. But your question is a hard question, and there are many ways to answer it. Wikipedia has a description of dependent and independent variables:

For example, suppose you want to measure the effect of the temperature of a star on the temperature of its planets. The star will have the temperature that it does because of the star's mass and stage of it's life - it has nothing to do with the planets that orbit the star, if there even are any. The planets will be much hotter if they orbit a hotter star, though, because hotter stars put out more light.

For instance, variables you see every day may include the amount of water you drink, or the number of hours you spend reading books. In an experiment, independent variables are the quantities you change in order to change other quantities. For instance, if you were doing an experiment in which you want to find out how the number of hours of sunlight exposure affects plant growth, you might have two variables: number of hours of sunlight given to the plant per day, and the height of the plant. In this case, the number of hours of sunlight is the and the height of the plant is the , because the change in the height depends on the hours of sunlight, not the other way around.

At the heart of a scientific experiment, you are testing how something responds to some form of your input. When running an experiment, you repeat it many times, changing one part every time and observing what happens. (it's called a vary-able because you can vary it!). , because these depend on the in(in meaning not)dependent variable. This is vague so let's consider an example: Imagine an experiment looking at flower growth. Plant a bunch of identical seeds in separate pots and let them grow next to each other with only one difference among them. This difference could be how much you water them, then There can then be many dependent variables for an independent variable, but examples here could be how fast or tall the flower grows or what colors its petals are.

When working with equations it is really important to understand the , while This may sound a little confusing, so let’s look at an example. Let’s say that we were measuring . In this scenario, because it does not depend on anything else for it to occur, it just happens on its own. This is different from . The plant’s height changes as time goes on. . The dependent variable is always controlled by the independent variable. I hope this answers your question! to return to the search form. YEAR 10 Science Investigation Task - Progya Priya > > The Independent  and Dependent Variables Under what light source/environmental conditions (time of day) the transpiration was measured The rate of water which transpired during the given time and time of day The E xperimental C ontrols/ C ontrolled V ariables The type of plants tested The soil which both plants are growing in The area where the plants are placed The amount of time the plants were given to transpire Part of Physics Working scientifically A variable is a factor that can be changed in an experiment. Identifying control variables, independent and dependent variables is important in making experiments fair. Knowing about variables can help you make scientific predictions and test them. True or false? When changing the height from which a ball is dropped to see how high it bounces, the height from which it is dropped can be a variable. Show answer Hide answer Video - a series of experiments. Watch this video of a number of experiments being carried out. This video can not be played To play this video you need to enable JavaScript in your browser. After watching the video, make a note of what is being changed (the independent variable), measured (the dependent variable) and kept the same (the control variables) Video Transcript Video Transcript Presenter 2: We want to understand some of the different types of variables. To do this, we are going to run through an experiment. Presenter 1: For example, what will happen if I put a beaker on top of a candle? Presenter 2: To investigate this, you need to think about the variables involved to obtain valid results. Presenter 1: Variables are things that you can change or measure in an experiment. Presenter 2: The independent variable is the one you change. The dependent variable is what you measure afterwards. Presenter 1: In this experiment, we need to measure the time it takes to candle to go out. That is our dependent variable. Students: Are we ready? And…out. OK, 11… Presenter 2: So, let's change the volume of the beaker and that is our independent variable. Student 2: Ready to start the time? And… now. The bigger the beaker, the more oxygen can fit in which means it'll last longer,which is proven here. Presenter 1: To make this a valid experiment, we need to make sure we only change one variable. Other variables are kept the same or controlled. The control variable is the candle, so we have to use identical candles. Presenter 2: That means a brand new candle each time. We also need to seal the gap between the beaker and the mat to stop air escaping or entering. This helps us ensure only one variable is changing in this experiment. Presenter 1: And this means our experiment is valid. Presenter 2: So now, not only can we see the difference between independent and dependent variables, but we can also consider the control variable to ensure a valid experiment. Planning an experiment Scientists ask questions to find out more about the world, like ‘how can we get more energy from the sun?’ and ‘how can we cure diseases?’. To answer these questions scientists do experiments. During experiments, factors that can change are called variables . A variable is anything that can change and be measured. Two important types of variables are: Independent variables – the variable that is being changed during the experiment Dependent variables – the variable being tested or measured during the experiment In an experiment, the effect of changing just one variable on another is tested - testing how the independent variable affects the dependent variable. For this reason, other variables must be controlled so that they don't affect the independent variable. These variables are control variables close control variable A variable which must be kept the same so that the result of the experiment is not affected. . Making predictions More on Working scientifically Find out more by working through a topic Writing a hypothesis and prediction count 3 of 11 count 4 of 11 Maths skills for science count 5 of 11 Drawing scientific apparatus count 6 of 11 VanCleave's Science Fun Your Guide to Science Projects, Fun Experiments, and Science Research Variables: Independent, Dependent, Controlled By Janice VanCleave Types of Variables in A Science Fair Project Independent variables Dependent variables Controlled variables Example: Investigate Electromagnetics ———————————————————– Example: Investigate Plant Growth List what you know about plant growth.   Following are more examples of variables. A variable is part of an experiment that can change, such as the amount of light, temperature, humidity, or plant growth as well as the direction of plant growth. In an experiment, an independent variable is a variable that changes either on its own, or you purposely change it. For example, you can control the amount of light, environmental temperature,  and humidity in which a plant is grown. The goal of an experimental investigation is to determine how changes in an independent variable affects another variable, which is called the dependent variable.  Remember: When designing an Experimental Investigation  it is important that you only have one independent variable and one dependent variable. All other variables must be controlled, meaning they must stay the same.    FYI: Controlled variables are not the same as a Control for your Investigation. The following will explain this. Start Experiment Design: I want you to understand some of the thought processes for designing an experiment that meets Science Fair Project requirements. 1. You need to have a purpose for your investigation. At this point you may not know exactly what you want to do, but  let’s assume you want to work with plants. I’d search the web and find different project ideas about plants. Let’s assume you decide to investigate how light affects plant growth. At this point you need to write a basic purpose. I point out that this might only be a   BASIC Purpose , meaning you may need to improve it. Basic Purpose of Investigation:  How light affects plant growth. I’ll come back to the purpose, but next you need to write your science fair question. 2. The question for an experimental investigation must identify both the independent variable and the dependent variable. Your basic purpose identifies light as the independent variable and plant growth as the dependent variable. Don’t Forget:  The independent variable and the dependent variable for an experimental investigation must be measurable. Measuring the Independent Variable How does one measure light? Light is very general. You need to be more specific about the independent variable. You will get the point after reading the following questions. Do you want to measure how different types of light sources, such as sunlight, artificial light, or colored light affects plant growth?   This is a good idea for a science project. Do you want to measure how changes in the amount of one type of light effects plants growth? This is another good idea for a science project. You may have other ideas about the light you want to use, but for this article, I will give examples for measuring the amount of one type of light. The Independent Variable is the amount of light the plant received. Ideas for how to change the amount of light. 1. Use sunlight, which changes during the day as well as from one day to the next. You can also use direct and indirect sunlight. 2. If you use artificial light you can decide when and for how long the plant will receive this light. You also have the option to control the wattage of light used–its color–type (fluorescent, UV, incandescent). Just use the same type of light for each test plant. Dependent Variable The purpose of  changing an independent variable is to decide how the changes affect something else, which is called the dependent variable. In other words, changes in the independent variable may cause the dependent variable to change. In an experiment, a dependent variable may change due to the changes made in the independent variable. For the earlier plant experiment, “How Plants Grow In Response to Light,”  the growth of the plant is the dependent variable being observed  The plant growth is in response to changes in the amount of light the plant receives, is the independent variable. Controlled Variables Controlled variables sounds like an oxymoron. Just remember that variables are things that CAN change. This means that they can be controlled and prevented from changed. It is important that when you experiment that you have only two variables that change: The independent variable that you want to change and can measure how it changes. The dependent variable that you are measuring to see how much it changes in response to the independent variable. Changes in any other variable could affect your results.  So, you must try to control any other variable,  meaning you want to delete them if possible, or at least make an effort to reduce their influence if possible. For example, in the previous experiment, “How Plants Grow In Response to Light,” the  variables that must be controlled include; the type of plant tested, type of soil, temperature, amount of water, type of light These variables need to be the same for every plant tested. Some variable are difficult to control, but you should try to make every effort to keep them the same during the testing. Use the Search on this website to find more examples of variables, one that parallels the information on this page, but provides another example, is The Variables of Testing Onions In a Hay Filled Bathtub. Share this: What Is The Independent Variable In A Plant Growth Experiment  In the realm of scientific research, understanding the variables at play is crucial to unraveling the intricacies of experiments. One fundamental and influential variable within plant growth experiments is the independent variable. Defined as the condition or factor manipulated by the researchers, the independent variable holds the power to shape the course of the experiment and ultimately affect the growth of plants. It serves as a gateway to unlocking valuable insights into the intricate world of botanical development. To comprehend the role and significance of the independent variable in plant growth experiments, it is essential to delve into its characteristics and examine its implications within this scientific domain. The independent variable, often referred to as the manipulated variable, is the key element carefully selected and adjusted by researchers to explore its potential impact on plant growth. It is important to grasp that the independent variable is intentionally altered to assess its effects on the dependent variable, which represents the outcome being measured or observed. By modifying the independent variable, researchers have the opportunity to study the cause-and-effect relationship it has with plant growth, ultimately providing a deeper understanding of these organisms’ intricate mechanisms. The selection and refinement of the independent variable demand careful consideration. Its choice should be driven by a clear and focused research question or hypothesis, allowing researchers to investigate specific factors that may influence plant growth. For instance, an independent variable in a plant growth experiment could be varying levels of sunlight exposure, different concentrations of nutrients in the soil, or contrasting irrigation techniques. By manipulating these variables, scientists gain valuable insights into the correlation between their alterations and subsequent effects on plant growth. Moreover, it is important to note that a single experiment can incorporate multiple independent variables, though each one should be analyzed and treated separately to accurately assess their individual influence on plant growth. For instance, by simultaneously altering water levels and temperature, researchers can assess the distinct impacts of both variables, contributing to a more comprehensive understanding of the factors driving plant development. However, it is essential to ensure that the experimental design allows for isolation of the effects of each independent variable, enabling accurate interpretations of the results. By manipulating the independent variable within plant growth experiments, scientists unlock the potential to explore and unravel the complex relationship between various factors and the growth of plants. Through a careful selection, adjustment, and isolation of the independent variable, researchers can provide valuable insights into botanical development. Consequently, understanding the role and significance of the independent variable serves as an essential foundation for not only comprehending plant growth but also contributing to future advancements in agriculture, horticulture, and ecological studies. key Takeaways The independent variable in a plant growth experiment is the variable that is manipulated or changed by the researcher. It is the factor that the researcher believes will have an effect on the dependent variable, which is the outcome or result of the experiment. In a plant growth experiment, examples of independent variables could include the amount of water given to the plants, the type of fertilizer used, or the amount of light exposure. The independent variable is typically presented in different levels or conditions, allowing the researcher to compare the effects of each level on the dependent variable. Controlling all other variables except the independent variable is crucial in order to accurately determine its impact on plant growth. Randomization is important when assigning plants to different levels of the independent variable to minimize bias and ensure the validity of the experiment. The independent variable should be clearly defined and accurately measured to ensure consistency and reproducibility of the experiment. The results of a plant growth experiment can provide valuable insights into optimal conditions for plant growth and inform agricultural practices. Defining the Independent Variable in a Plant Growth Experiment In order to accurately conduct and analyze any scientific experiment, it is crucial to understand the concept of the independent variable. When it comes to a plant growth experiment, the independent variable plays a key role in determining the outcome and understanding the factors that influence plant growth. Understanding the Independent Variable The independent variable refers to the factor or condition that is intentionally manipulated or changed by the researcher in an experiment. It is the variable that the researcher believes will have an effect on the dependent variable, which is the variable being measured or observed in response to the changes made to the independent variable. In the context of a plant growth experiment, the independent variable refers to the factor that is being altered to examine its impact on the growth of the plants. This variable can be any aspect that the researcher wants to investigate, such as the type of fertilizer used, the amount of water provided, the intensity and duration of light exposure, or the presence of certain chemicals. Significance of the Independent Variable The independent variable is crucial for establishing cause-and-effect relationships in scientific experiments. By manipulating and controlling the independent variable, researchers can determine whether any observed changes in the dependent variable are a direct result of the manipulated factor, or if they are influenced by other variables. In a plant growth experiment, identifying and effectively manipulating the independent variable allows researchers to understand how specific conditions or factors affect the growth and development of plants. By varying one factor at a time while keeping all other variables constant, researchers can pinpoint the impact of each independent variable on the plants, leading to valuable insights and conclusions. Examples of Independent Variables in a Plant Growth Experiment There are numerous independent variables that researchers can explore in plant growth experiments. Some common examples include: Type of Fertilizer: Examining how different types of fertilizers, such as organic or synthetic ones, affect plant growth. Amount of Water: Investigating the impact of varying watering schedules or quantities of water on plant growth. Light Exposure: Studying how different light intensities, durations, or wavelengths affect the growth and development of plants. Presence of Chemicals: Analyzing the effects of specific chemicals, such as pesticides or growth hormones, on plant growth. By selecting and manipulating one independent variable at a time, researchers can uncover the relationship between that variable and the growth of plants, contributing to a better understanding of plant biology and facilitating advancements in agriculture and horticulture. Frequently Asked Questions What is the independent variable in a plant growth experiment. The independent variable in a plant growth experiment is the variable that is manipulated or changed by the researcher. It is the factor that is believed to have an impact on the growth of the plants being studied. It is typically represented on the x-axis of a graph. Why is the independent variable important in a plant growth experiment? The independent variable is important in a plant growth experiment because it allows researchers to test the effects of different factors on the growth of plants. By manipulating the independent variable, researchers can determine if there is a causal relationship between the variable and the plant’s growth. This information is essential for understanding how certain factors affect plant growth and can inform agricultural practices and plant breeding techniques. What are some examples of independent variables in plant growth experiments? Some examples of independent variables in plant growth experiments may include the amount of water given to the plants, the type of fertilizer used, the intensity of light exposure, or the temperature in which the plants are grown. These variables can be systematically manipulated to see how they affect the growth of the plants being studied. How is the independent variable determined in a plant growth experiment? The determination of the independent variable in a plant growth experiment depends on the research question or hypothesis being investigated. The researcher needs to identify the factor that they believe may have an impact on plant growth and then set up experimental conditions to test the effects of varying levels or conditions of that factor. Can there be more than one independent variable in a plant growth experiment? Yes, there can be more than one independent variable in a plant growth experiment. In some studies, researchers may be interested in examining the effects of multiple factors on plant growth simultaneously. In these cases, multiple independent variables would be manipulated and studied to understand their individual and combined effects. The Different Types and Options for Plant Growth Experiments Types of plant growth experiments. There are several types of plant growth experiments that researchers may choose to conduct, depending on their specific research question or objective. These types include comparative experiments, control experiments, factorial experiments, and field experiments. Comparative experiments In a comparative experiment, researchers compare the growth of plants under different conditions or treatments. This allows them to determine the effects of specific variables on the growth of the plants. Control experiments A control experiment involves setting up a control group that is kept under standard or normal conditions, while other groups are subjected to different treatments. The control group serves as a baseline for comparison and allows researchers to isolate the effects of the independent variables being tested. Factorial experiments Factorial experiments involve manipulating more than one independent variable simultaneously. This allows researchers to analyze the effects of each variable individually, as well as their interactions. Field experiments Field experiments are conducted in natural environments, such as farms or gardens. These experiments provide insights into how plants grow and respond to various factors in real-world conditions. Final Thoughts In conclusion, the independent variable in a plant growth experiment is the variable that is manipulated or changed by the researcher to test its impact on plant growth. It is essential in determining the causal relationship between different factors and plant growth, which can inform agricultural practices and plant breeding techniques. Some examples of independent variables include the amount of water, the type of fertilizer, light intensity, and temperature. Researchers can manipulate and control these variables to understand their effects on plant growth. When conducting plant growth experiments, researchers have various types and options available to them. Comparative experiments, control experiments, factorial experiments, and field experiments are common approaches that allow researchers to investigate different aspects of plant growth. These types of experiments enable researchers to compare different conditions, establish baseline comparisons, analyze the effects of multiple variables, and observe plant growth in real-world environments. By utilizing these different approaches, researchers can gain a comprehensive understanding of plant growth and the factors that influence it. 301 IMAGES What is an dependent variable in science? Experiment Design Science images Variables: Independent, Dependent, Controlled Science infographic [Solved] Could you please help me identify the 3 types of variables for the... COMMENTS Independent and Dependent Variables Examples 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. Experimental Design 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 ... Independent vs. Dependent Variables The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores. Independent and Dependent Variables: Which Is Which? Independent Variable: Type of fertilizer given to the plant; Dependent Variable: Plant height . Experiment 3: You're interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures. Controlled experiments (article) In contrast, the dependent variable in an experiment is the response that's measured to see if the treatment had an effect. In this case, the fraction of bean seeds that sprouted is the dependent variable. The dependent variable (fraction of seeds sprouting) depends on the independent variable (the amount of water), and not vice versa. Independent and Dependent Variables: Differences & Examples 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. ... Some independent variables are the experiment's focus, while others help keep the experimental results valid. Here's how to recognize independent variables ... Independent vs. Dependent Variables: What's the Difference? by Zach Bobbitt February 5, 2020. In an experiment, there are two main variables: The independent variable: the variable that an experimenter changes or controls so that they can observe the effects on the dependent variable. The dependent variable: the variable being measured in an experiment that is "dependent" on the independent variable. What Are Dependent, Independent & Controlled Variables? 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. Definitions of Control, Constant, Independent and Dependent Variables Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of ... Experiment This would be known as a control experiment . The plants that do not receive the fertilizer are the control group. An experiment must have a control group. In the control group, nothing is changed. It is not subjected to the independent variable. In any experiment, other factors that might affect the dependent variable must be controlled. Dependent Variable Definition and Examples 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. What Is a Dependent Variable in Science? A dependent variable is the variable that is tested and measured in a scientific experiment. It is sometimes called the responding variable. The dependent variable gets its name from the fact that it depends on the independent variable. As the experimenter manipulates the independent variable, a change in the dependent variable is observed and ... Dependent & Independent Variables in Science Experiments Dependent: A dependent variable is what we measure in an experiment. It's the result that happens because of the change we made. Example: In the plant experiment, the growth of the plant is the dependent variable because it's what you measure to see how much the plant has grown based on the different amounts of water. Dependent Variables (Definition + 30 Examples) A dependent variable is what we observe and measure in an experiment. It's called "dependent" because it changes based on the alterations we make to another variable, known as the independent variable. ... Exercise Routine: The increase in stamina is what you are measuring, making it the dependent variable. Plant Fertilizer: The juiciness of ... UCSB Science Line In an experiment, the independent variable is what the scientist has control over, it is the thing that the scientist gets to decide. The dependent variable is different, it is what the scientist looks at or measures, but does not get to decide. Typically, the scientist wants the independent variable to change the dependent variable. PDF Science SkillS Identify and dependent variable depends on the independent variable. The amount that the plants grow depends on the amount of sun they get. The independent variable is what you change, and the dependent variable is what happens because of the change. The only way to get reliable results from this experiment is to make sure that all the other variables stay ... PDF PLANT-GROWTH EXPERIMENT The plant-growth experiment is an example of a factorial experiment. A factorial experiment consists of several factors (seed, water) which are set at different levels, and a response variable (plant height). The purpose of the experiment is to assess the impact of different combinations of the levels of seed and water on plant height. Analysis of The Variables The dependent variables are: The rate of water which transpired during the given time and time of day; The time of day is an independent variable because this will affect the dependent variable: the rate of transpiration. This is because the different times of day have different light and environmental conditions which will either slow or speed ... Variables The independent variable close independent variable The variable that is changed during an experiment. is the volume of water given to each plant. The dependent variable close dependent variable ... Variables: Independent, Dependent, Controlled Following are more examples of variables. A variable is part of an experiment that can change, such as the amount of light, temperature, humidity, or plant growth as well as the direction of plant growth.. In an experiment, an independent variable is a variable that changes either on its own, or you purposely change it. For example, you can control the amount of light, environmental ... What Is The Independent Variable In A Plant Growth Experiment The independent variable in a plant growth experiment is the variable that is manipulated or changed by the researcher. It is the factor that the researcher believes will have an effect on the dependent variable, which is the outcome or result of the experiment. What is the Dependent, Independent, and Controlled Variable of a plant It is the variable that is believed to have an effect on the dependent variable. In a plant growth experiment, the independent variable could be the amount of water, sunlight, or fertilizer provided to the plants. The controlled variables are the factors that are kept constant throughout the experiment. essay homework paper phd project resume review speech thesis