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Control Variables: Definition, Uses & Examples

By Jim Frost 4 Comments

What is a Control Variable?

Control variables, also known as controlled variables, are properties that researchers hold constant for all observations in an experiment. While these variables are not the primary focus of the research, keeping their values consistent helps the study establish the true relationships between the independent and dependent variables. The capacity to control variables directly is highest in experiments that researchers conduct under lab conditions. In observational studies, researchers can’t directly control the variables. Instead, they record the values of control variables and then use statistical procedures to account for them.

In science, researchers assess the effects that the independent variables have on the dependent variable. However, other variables can also affect the outcome. If the scientists do not control these other variables, they can distort the primary results of interest. In other words, left uncontrolled, those other factors become confounders that can bias the findings. The uncontrolled variables may be responsible for the changes in the outcomes rather than your treatment or experimental variables. Consequently, researchers control the values of these other variables.

Suppose you are performing an experiment involving different types of fertilizers and plant growth. Those are your primary variables of interest. However, you also know that soil moisture, sunlight, and temperature affect plant growth. If you don’t hold these variables constant for all observations, they might explain the plant growth differences you observe. Consequently, moisture, sunlight, and temperature are essential control variables for your study.

If you perform the study in a controlled lab setting, you can control these environmental conditions and keep their values constant for all observations in your experiment. That way, if you do see plant growth differences, you can be more confident that the fertilizers caused them.

When researchers use control variables, they should identify them, record their values, and include the details in their write-up. This process helps other researchers understand and replicate the results.

Related posts : Independent and Dependent Variables and Confounding Variables

Control Variables and Internal Validity

By controlling variables, you increase the internal validity of your research. Internal validity is the degree of confidence that a causal relationship exists between the treatment and the difference in outcomes. In other words, how likely is it that your treatment caused the differences you observe? Are the researcher’s conclusions correct? Or, can changes in the outcome be attributed to other causes?

If the relevant variables are not controlled, you might need to attribute the changes to confounders rather than the treatment. Control variables reduce the impact of confounding variables.

Controlled Variable Examples

How to Control Variables in Science

Scientists can control variables using several methods. In some cases, variables can be controlled directly. For example, researchers can control the growing conditions for the fertilizer experiment. Or use standardized procedures and processes for all subjects to reduce other sources of variation. These efforts attempt to eliminate all differences between the treatment and control groups other than the treatments themselves.

However, sometimes that’s not possible. Fortunately, there are other approaches.

Random assignment

In some experiments, there can be too many variables to control. Additionally, the researchers might not even know all the potential confounding variables. In these cases, they can randomly assign subjects to the experimental groups. This process controls variables by averaging out all traits across the experimental groups, making them roughly equivalent when the experiment begins. The randomness helps prevent any systematic differences between the experimental groups. Learn more in my post about Random Assignment in Experiments .

Statistical control

Directly controlled variables and random assignment are methods that equalize the experimental groups. However, they aren’t always feasible. In some cases, there are too many variables to control. In other situations, random assignment might not be possible. Try randomly assigning people to smoking and non-smoking groups!

Fortunately, statistical techniques, such as multiple regression analysis , don’t balance the groups but instead use a model that statistically controls the variables. The model accounts for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while the treatment variable fluctuates. This process allows you to isolate the role of the treatment while accounting for confounders. You can also use ANOVA and ANCOVA.

For more information, read my posts, When to Use Regression and ANOVA Overview .

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Reader Interactions

July 13, 2024 at 2:19 am

Sir you are doing a good job. much appreciated. Could you please tell us how to read the values of control variables like ranges and what do they mean. For instance how to read this (F=1.83; p= 0.07). Thank YOU

February 28, 2024 at 2:09 pm

In your explanation of control variables you use the example of a research study of plant fertilizers and their growth, wanting to control for moisture, sunshine and temperature. You state “Consequently, moisture, sunlight, and temperature are essential control variables for your study. These variables can be controlled by keeping their values constant for all observations in your experiment. You do not go further as to how you control for these values, particularly when such variables are continually changing. Al Wassler

February 28, 2024 at 2:13 pm

Presumably, this experiment would occur in a lab setting where you can control these variables. Plants would be raised with the same humidity, soil moisture, and light conditions.

I’ll add some text to the article to clarify that. Thanks!

January 26, 2023 at 7:00 pm

I have a question please about when a control variable is also itself part of the dependent variable. I see this referred to in the medical research literature as ‘mathematical coupling’, where – for example – the beats per minute (BPM) is the dependent variable and researchers want to put minutes also as a control variable. This seems to create a problem because ‘minutes’ appears on both sides of the equation, and the medical literature talks about spurious correlation, and the model needing to be redesigned. But do you have a simple text or reference – ideally just plain statistics/OLS rather than linked to medical research – where this could be explained in theory terms ? What goes wrong in the regression when a variable is both a control variable and part of the dependent variable (perhaps as part of a ratio or measurement of change)? I just haven’t found a textbook reference that says definitively ‘you can’t have the same variable in both sides of the regression simultaneously’, so I’m not sure whether this violates OLS and so is something to avoid entirely (with a new model design or different research question) or to live with.

Any help would be great, thank you for your work,

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Home » Control Variable – Definition, Types and Examples

Control Variable – Definition, Types and Examples

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Control Variable

Definition :

Control variable, also known as a “constant variable,” is a variable that is held constant or fixed during an experiment or study to prevent it from affecting the outcome. In other words, a control variable is a variable that is kept the same or held constant to isolate the effects of the independent variable on the dependent variable.

For example, if you were conducting an experiment to test how temperature affects plant growth, you might want to control variables such as the amount of water, the amount of sunlight, and the type of soil to ensure that these variables do not interfere with the results. By controlling these variables, you can isolate the effect of temperature on plant growth and draw more accurate conclusions from the experiment.

Types of Control Variables

Types of Control Variables are as follows:

Environmental Control Variables

These are variables related to the physical environment in which the experiment is conducted, such as temperature, humidity, light, and sound.

Participant Control Variables

These are variables related to the participants in the experiment, such as age, gender, prior knowledge, or experience.

Experimental Control Variables

These are variables that the researcher manipulates or controls to ensure that they do not affect the outcome of the experiment. For example, in a study on the effects of a new medication, the researcher might control the dosage, frequency, or duration of the treatment.

Procedural Control Variables

These are variables related to the procedures or methods used in the experiment, such as the order in which tasks are completed, the timing of measurements, or the instructions given to participants.

Equipment Control Variables

These are variables related to the equipment or instruments used in the experiment, such as calibration, maintenance, or proper functioning.

How to Control a Variable

To control a variable in a scientific experiment, you need to ensure that it is kept constant or unchanged throughout the experiment. Here are some steps to help you control a variable:

Identify the Variable

Start by identifying the variable that you want to control. This can be an environmental, subject, procedural, or instrumentation variable.

Determine the Level of Control Needed

Depending on the variable, you may need to exert varying levels of control. For example, environmental variables may require you to control the temperature, humidity, and lighting in your experiment, while subject variables may require you to select a specific group of participants that meet certain criteria.

Establish a Standard Level

Determine the standard level or value of the variable that you want to control. For example, if you are controlling the temperature, you may set the temperature to a specific degree and ensure that it is maintained at that level throughout the experiment.

Monitor the Variable

Throughout the experiment, monitor the variable to ensure that it remains constant. Use appropriate equipment or instruments to measure the variable and make adjustments as necessary to maintain the desired level.

Document the Process

Document the process of controlling the variable to ensure that the experiment is replicable. This includes documenting the standard level, monitoring procedures, and any adjustments made during the experiment.

Examples of Control Variables

Here are some examples of control variables in Scientific Experiments and Research:

• Environmental Control Variables Example: Suppose you are conducting an experiment to study the effect of light on plant growth. You would want to control environmental factors such as temperature, humidity, and soil nutrients. In this case, you might keep the temperature and humidity constant and use the same type and amount of soil for all the plants.
• Subject Control Variables Example : If you are conducting an experiment to study the effect of a new medication on blood pressure, you would want to control subject variables such as age, gender, and health status. In this case, you might select a group of participants with similar ages, genders, and health conditions to ensure that these variables do not affect the results.
• Procedural Control Variables Example : Suppose you are conducting an experiment to study the effect of distraction on reaction time. You would want to control procedural variables such as the time of day, the order of the tasks, and the instructions given to the participants. In this case, you might ensure that all participants perform the tasks in the same order, at the same time of day, and receive the same instructions.
• Instrumentation Control Variables Example : If you are conducting an experiment to study the effect of a new measurement device on the accuracy of readings, you would want to control instrumentation variables such as the type and calibration of the device. In this case, you might use the same type and model of the device and ensure that it is calibrated before each use.

Applications of Control Variable

Control variables are widely used in scientific research across various fields, including physics, biology, psychology, and engineering. Here are some applications of control variables:

• In medical research , control variables are used to ensure that any observed effects of a new treatment or medication are due to the treatment and not some other variable. By controlling subject variables such as age, gender, and health status, researchers can isolate the effects of the treatment and determine its effectiveness.
• In environmental research , control variables are used to study the effects of changes in the environment on various species or ecosystems. By controlling environmental variables such as temperature, humidity, and lighting, researchers can determine how different species adapt to changes in the environment.
• In psychology research, control variables are used to study the effects of different interventions or therapies on cognitive or behavioral outcomes. By controlling procedural variables such as the order of tasks, the length of time allotted for each task, and the instructions given to participants, researchers can isolate the effects of the intervention and determine its effectiveness.
• In engineering research, control variables are used to study the effects of different design parameters on the performance of a system or device. By controlling instrumentation variables such as the type of measurement device used and the calibration of instruments, researchers can ensure that the measurements are accurate and reliable.

Purpose of Control Variable

The purpose of a control variable in an experiment is to ensure that any observed changes or effects are a result of the manipulation of the independent variable and not some other variable. By keeping certain variables constant, researchers can isolate the effects of the independent variable and determine whether it has a significant effect on the dependent variable.

Control variables are important because they help to increase the reliability and validity of the experiment. Reliability refers to the consistency and reproducibility of the results, while validity refers to the accuracy and truthfulness of the results. By controlling variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the likelihood that the results accurately reflect the effect of the independent variable on the dependent variable.

Characteristics of Control Variable

Control variables have the following characteristics:

• Constant : Control variables are kept constant or unchanged throughout the experiment. This means that their values do not vary or change during the experiment. Keeping control variables constant helps to ensure that any observed effects or changes are due to the manipulation of the independent variable and not some other variable.
• Independent : Control variables are independent of the independent variable being studied. This means that they do not affect the relationship between the independent and dependent variables. By controlling for independent variables, researchers can isolate the effect of the independent variable and determine its impact on the dependent variable.
• Documented: Control variables are documented in the experiment. This means that their values and methods of control are recorded and reported in the results section of the research paper. By documenting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Relevant: Control variables are relevant to the research question. This means that they are chosen based on their potential to affect the outcome of the experiment. By selecting relevant control variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the reliability and validity of the results.
• Varied : Control variables can be varied across different conditions or groups. This means that different levels of control may be needed depending on the research question or hypothesis being tested. By varying control variables, researchers can test different hypotheses and determine the factors that affect the outcome of the experiment.

Advantages of Control Variable

The advantages of using control variables in an experiment are:

• Increased accuracy : Control variables help to increase the accuracy of the results by reducing the potential for extraneous or confounding variables that can affect the outcome of the experiment. By controlling for these variables, researchers can isolate the effect of the independent variable on the dependent variable and determine whether it has a significant impact.
• Increased reliability : Control variables help to increase the reliability of the results by reducing the variability in the experiment. By keeping certain variables constant, researchers can ensure that any observed changes or effects are due to the manipulation of the independent variable and not some other variable.
• Reproducibility: Control variables help to increase the reproducibility of the results by ensuring that the same results can be obtained when the experiment is repeated. By documenting and reporting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Generalizability : Control variables help to increase the generalizability of the results by reducing the potential for bias and increasing the external validity of the experiment. By controlling for relevant variables, researchers can ensure that their findings are applicable to a broader population or context.
• Causality : Control variables help to establish causality by ensuring that any observed changes or effects are due to the manipulation of the independent variable and not some other variable. By controlling for confounding variables, researchers can increase the internal validity of the experiment and establish a cause-and-effect relationship between the independent and dependent variables.

Disadvantages of Control Variable

There are some potential disadvantages or limitations of using control variables in an experiment:

• Complexity : Controlling for multiple variables can make an experiment more complex and time-consuming. This can increase the likelihood of errors and reduce the feasibility of the experiment, especially if the control variables require a lot of resources or are difficult to measure.
• Artificiality : Controlling for variables can make the experimental conditions artificial and not reflective of real-world situations. This can reduce the external validity of the experiment and limit the generalizability of the findings to real-world settings.
• Limited scope : Controlling for specific variables can limit the scope of the experiment and make it difficult to generalize the results to other situations or populations. This can reduce the external validity of the experiment and limit its practical applications.
• Assumptions: Controlling for variables requires making assumptions about which variables are relevant and how they should be controlled. These assumptions may not be valid or accurate, and the results of the experiment may be affected by uncontrolled variables that were not considered.
• Cost : Controlling for variables can be costly, especially if the control variables require additional resources or equipment. This can limit the feasibility of the experiment, especially for researchers with limited funding or resources.

Limitations of Control Variable

There are several limitations of using control variables in an experiment, including:

• Not all variables can be controlled : There may be some variables that cannot be controlled or manipulated in an experiment. For example, some variables may be too difficult or expensive to measure or control, or they may be affected by factors outside of the researcher’s control.
• Interaction effects : Control variables can interact with each other, which can lead to unexpected results. For example, controlling for one variable may have a different effect when another variable is also controlled, or when the two variables interact with each other. These interaction effects can be difficult to predict or control for.
• Over-reliance on statistical significance: Controlling for variables can increase the statistical significance of the results, but this may not always translate to practical significance or real-world significance. Researchers should interpret the results of an experiment in light of the practical significance, not just the statistical significance.
• Limited generalizability : Controlling for variables can limit the generalizability of the results to other populations or situations. If the control variables are not representative of other populations or situations, the results of the experiment may not be applicable to those contexts.
• May mask important effects : Controlling for variables can mask important effects that are related to the independent variable. By controlling for certain variables, researchers may miss important interactions between the independent variable and the controlled variable, which can limit the understanding of the causal relationship between the two.

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Controlled Experiment

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This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) – is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables that are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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• What Are Control Variables | Definition & Examples

What Are Control Variables? | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 16 June 2023.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomisation or statistical control (e.g., to account for participant characteristics like age in statistical tests).

Table of contents

Why do control variables matter, how do you control a variable, control variable vs control group, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest.

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

• Marital status

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There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook have more screen time during the day, and this might influence how alert they are in your study.

Standardised procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables, you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

• To control for diet, fresh and frozen meals are delivered to participants three times a day.
• To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
• To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other variables.

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

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

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

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

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

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

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

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Controlling Variables

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

This material is excerpted from a teaching module on the Visionlearning website, to view this material in context, please visit Research Methods: Experimentation.

Controlling variables is an important part of experimental design. Controlled variables refer to variables or contributing factors that are fixed or eliminated in order to clearly identify the relationship between an independent variable and a dependent variable . For example, in an experiment designed to quantify the effect of vitamin A dose on the metabolism of beta-carotene in humans, Shawna Lemke and colleagues had to precisely control the diet of their human volunteers (Lemke, Dueker et al. 2003). They asked their participants to limit their intake of foods rich in vitamin A and further asked that they maintain a precise log of all foods eaten for 1 week prior to their study. At the time of their study, they controlled their participants’ diet by feeding them all the same meals, described in the methods section of their research article in this way, “Meals were controlled for time and content on the dose administration day. Lunch was served at 5.5 h postdosing and consisted of a frozen dinner (Enchiladas, Amy's Kitchen, Petaluma, CA), a blueberry bagel with jelly, 1 apple and 1 banana, and a large chocolate chunk cookie (Pepperidge Farm). Dinner was served 10.5 h post dose and consisted of a frozen dinner (Chinese Stir Fry, Amy's Kitchen) plus the bagel and fruit taken for lunch.”

Controlling variables is important because slight variations in the experimental set-up could strongly affect the outcome being measured. For example, during the 1950s, a number of experiments were conducted to evaluate the toxicity in mammals of the metal molybdenum, using rats as experimental subjects . Unexpectedly, these experiments seemed to indicate that the type of cage the rats were housed in affected the toxicity of molybdenum. In response, G. Brinkman and Russell Miller set up an experiment to investigate this observation (Brinkman & Miller, 1961). Brinkman and Miller fed two groups of rats a normal diet that was supplemented with 200 parts per million (ppm) of molybdenum. One group of rats was housed in galvanized steel (steel coated with zinc to reduce corrosion) cages and the second group was housed in stainless steel cages. Rats housed in the galvanized steel cages suffered more from molybdenum toxicity than the other group: they had higher concentrations of molybdenum in their livers and lower blood hemoglobin levels. It was then shown that when the rats chewed on their cages, those housed in the galvanized metal cages absorbed zinc plated onto the metal bars and zinc is now known to affect the toxicity of molybdenum. In order to control for zinc exposure, then, stainless steel cages needed to be used for all rats.

While controlling variables is an important aspect of making an experiment manageable and informative, it is often not representative of the real world, in which many variables may change at once, including the foods you eat. Still, experimental research is an excellent way of determining relationships between variables that can be later validated in real world settings through descriptive or comparative studies.

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Microbe Notes

Controlled Experiments: Definition, Steps, Results, Uses

Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively.

Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.

Real-world applications demonstrate the practical impact of controlled experiments, guiding informed decision-making in diverse domains.

Controlled experiments are the systematic research method where variables are intentionally manipulated and controlled to observe the effects of a particular phenomenon. It aims to isolate and measure the impact of specific variables, ensuring a more accurate causality assessment.

Table of Contents

Interesting Science Videos

Importance of controlled experiments in various fields

Controlled experiments are significant across diverse fields, including science, psychology, economics, healthcare, and technology.

They provide a systematic approach to test hypotheses, establish cause-and-effect relationships, and validate the effectiveness of interventions or solutions.

Why Controlled Experiments Matter? 

Validity and reliability of results.

Controlled experiments uphold the gold standard for scientific validity and reliability. By meticulously controlling variables and conditions, researchers can attribute observed outcomes accurately to the independent variable being tested. This precision ensures that the findings can be replicated and are trustworthy.

Minimizing Biases and Confounding Variables

One of the core benefits of controlled experiments lies in their ability to minimize biases and confounding variables. Extraneous factors that could distort results are mitigated through careful control and randomization. This enables researchers to isolate the effects of the independent variable, leading to a more accurate understanding of causality.

Achieving Causal Inference

Controlled experiments provide a strong foundation for establishing causal relationships between variables. Researchers can confidently infer causation by manipulating specific variables and observing resulting changes. The capability informs decision-making, policy formulation, and advancements across various fields.

Planning a Controlled Experiment

Formulating research questions and hypotheses.

Formulating clear research questions and hypotheses is paramount at the outset of a controlled experiment. These inquiries guide the direction of the study, defining the variables of interest and setting the stage for structured experimentation.

Well-defined questions and hypotheses contribute to focused research and facilitate meaningful data collection.

Identifying Variables and Control Groups

Identifying and defining independent, dependent, and control variables is fundamental to experimental planning. 

Precise identification ensures that the experiment is designed to isolate the effect of the independent variable while controlling for other influential factors. Establishing control groups allows for meaningful comparisons and robust analysis of the experimental outcomes.

Designing Experimental Procedures and Protocols

Careful design of experimental procedures and protocols is essential for a successful controlled experiment. The step involves outlining the methodology, data collection techniques, and the sequence of activities in the experiment. 

A well-designed experiment is structured to maintain consistency, control, and accuracy throughout the study, thereby enhancing the validity and credibility of the results.

Conducting a Controlled Experiment

Randomization and participant selection.

Randomization is a critical step in ensuring the fairness and validity of a controlled experiment. It involves assigning participants to different experimental conditions in a random and unbiased manner. 

The selection of participants should accurately represent the target population, enhancing the results’ generalizability.

Data Collection Methods and Instruments

Selecting appropriate data collection methods and instruments is pivotal in gathering accurate and relevant data. Researchers often employ surveys, observations, interviews, or specialized tools to record and measure the variables of interest. 

The chosen methods should align with the experiment’s objectives and provide reliable data for analysis.

Monitoring and Maintaining Experimental Conditions

Maintaining consistent and controlled experimental conditions throughout the study is essential. Regular monitoring helps ensure that variables remain constant and uncontaminated, reducing the risk of confounding factors. 

Rigorous monitoring protocols and timely adjustments are crucial for the accuracy and reliability of the experiment.

Analysing Results and Drawing Conclusions

Data analysis techniques.

Data analysis involves employing appropriate statistical and analytical techniques to process the collected data. This step helps derive meaningful insights, identify patterns, and draw valid conclusions. 

Common techniques include regression analysis, t-tests , ANOVA , and more, tailored to the research design and data type .

Interpretation of Results

Interpreting the results entails understanding the statistical outcomes and their implications for the research objectives. 

Researchers analyze patterns, trends, and relationships revealed by the data analysis to infer the experiment’s impact on the variables under study. Clear and accurate interpretation is crucial for deriving actionable insights.

Implications and Potential Applications

Identifying the broader implications and potential applications of the experiment’s results is fundamental. Researchers consider how the findings can inform decision-making, policy development, or further research. 

Understanding the practical implications helps bridge the gap between theoretical insights and real-world application.

Common Challenges and Solutions

Addressing ethical considerations.

Ethical challenges in controlled experiments include ensuring informed consent, protecting participants’ privacy, and minimizing harm. 

Solutions involve thorough ethics reviews, transparent communication with participants, and implementing safeguards to uphold ethical standards throughout the experiment.

Dealing with Sample Size and Statistical Power

The sample size is crucial for achieving statistically significant results. Adequate sample sizes enhance the experiment’s power to detect meaningful effects accurately. 

Statistical power analysis guides researchers in determining the optimal sample size for the experiment, minimizing the risk of type I and II errors .

Mitigating Unforeseen Variables

Unforeseen variables can introduce bias and affect the experiment’s validity. Researchers employ meticulous planning and robust control measures to minimize the impact of unforeseen variables. 

Pre-testing and pilot studies help identify potential confounders, allowing researchers to adapt the experiment accordingly.

A controlled experiment involves meticulous planning, precise execution, and insightful analysis. Adhering to ethical standards, optimizing sample size, and adapting to unforeseen variables are key challenges that require thoughtful solutions. 

Real-world applications showcase the transformative potential of controlled experiments across varied domains, emphasizing their indispensable role in evidence-based decision-making and progress.

• https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
• https://www.scribbr.com/methodology/controlled-experiment/
• https://link.springer.com/10.1007/978-1-4899-7687-1_891
• http://ai.stanford.edu/~ronnyk/GuideControlledExperiments.pdf
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776925/
• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
• https://www.merriam-webster.com/dictionary/controlled%20experiment

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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.

Related Posts

Understanding Control Variables In Experiments

Make sure to consider how the control variable can help your experiments remain unaffected by external influences.

A researcher’s life is full of challenges. As soon as you enter the research program or join any institution the goal of the researcher is to look for similarities, differences, trends and find statistical significance in the experimental data obtained. Every day a researcher tries to solve a mysterious universe in his/her own way. You could be finding answers to immunological questions related to COVID or you could be questioning the fundamental theory of physics. Irrespective of the field of research a researcher lives a life of question > experiment > analyze > repeat ! 

Imagine a scenario: one fine morning, it is your day of observing results after 15 days of incubation of your microbiological experiment and you are certain that this would be it! This is the solution to the world food crisis! You enter your lab with extreme enthusiasm and after fine coffee in the morning, you walk towards your incubator. With a record book in your hand and a camera strap lying around your neck, you approach the incubator, and you are shocked!! The incubator temperature was changed from your required one to 45 degrees Celsius! Your heart is pounding and you don’t know what to do with those dried plates in your hand! 

Calm down, this was just an imagination, but if you focus on the story you will know what you cared about the most was the temperature of the incubator. That variable that you kept constant to all the plates while you changed some nutrients in all the media composition. Yes! That is the variable that we are going to talk about today, The control variable. To define a control variable is the parameter that is kept constant for the experiment. There can be more than one control variable in one experiment. Let us explore the characteristics of the control variable and how to get most of it under any experimental setup. 

Definition And Purpose

A control variable, also known as a controlled variable, is an element that is not changed throughout an experiment. Its purpose is to ensure that the effect of the independent variable on the dependent variable can be accurately measured without interference from other variables. Control variables are kept constant to prevent them from influencing the outcome, thus allowing a clear assessment of the relationship between the independent and dependent variables.

Consider a simple experiment to determine how the amount of sunlight affects plant growth. Here, the independent variable is the amount of sunlight, and the dependent variable is the growth of the plants. Possible control variables might include:

• Type of plant: Ensure all plants used are of the same species.
• Amount of water: Water each plant with the same amount of water.
• Soil type: Use the same type of soil for all plants.
• Pot size: Ensure all plants are in pots of the same size.
• Temperature: Keep the plants in the same temperature conditions.

By controlling these variables, you can more confidently attribute differences in plant growth to the amount of sunlight they received, rather than to differences in water, soil, or other factors. Learn about various variable types here.

Identifying Control Variables

Identification of control variables is the first step in the experimental design. The control variables are technically indirect indicators of what will be our experimental variables. All the variables for which we want to check the impact would vary in different ranges during the experiment whereas the control variable will stay constant. 

Define The Purpose Of The Experiment

The first step in determining what the control variables will be is to clearly define the purpose of the experiment. One expounds on the purpose of the experiment by making a statement or a research question about the experiment he wants to conduct. A research question is an interrogative statement on what you will be investigating and why it is of interest/importance. For instance, if you are testing the effect of some factor on some outcome, you have to state what that factor is that you are controlling or manipulating and just what it is you measure to ascertain the outcome. The determination of these two things is the foundation of constructing the total experimental design. It helps in structuring the experiment in such a manner that results can be truly attributed to the variables being tested and not other factors.

List Potential Influencing Factors

Having defined the purpose of the experiment, identifying all the potentially relevant factors that could influence the dependent variable that isn’t the independent variable. These are variables that might act on the output and therefore make the results biased or with high fluctuation. This identification of influential factors mandates a clear knowledge of the domain of exploration and the setting of the experiment. For instance, potential factors of influence could be environmental conditions, material properties, participant characteristics, and procedural details. Listing all these parameters makes it very easy to determine which needs to be addressed to ensure that the experimental results formulated are considered valid and acceptable.

Select Variables To Control

After identifying the potential parameters that will serve as the factors being controlled and their potential to act as factors in the process, the other step is to select the variables to be controlled. It is vitally observed that under this step the variables to be controlled need to be termed based on the level of impact each will have on the dependent variable and the level of ease or cost that will be experienced in trying to control such parameters. The reason is that only the independent variable should cause an effect on the dependent variable. This serves a function of balancing the compromised need to control so many variables in the experiment to be sure of some stated relationship between the independent and dependent variables versus a simple desire not to make the experiment too complicated.

Rationalize The Need For Control

The last step is to achieve the rationalization of why the identified variables must be controlled. It involves an explanation of how the changes in the variables thus identified might entail some changes in the dependent variable and thus explain several perceptions that end up as errors. Justification of why each control variable is included will allow you to make sure that your design is good, thus attributing the findings to the manipulation of independent variables and not extraneous variables. This will justify a resilient experiment in which the influence of extraneous variables is reduced, so results will be more precise and reliable. It also helps to describe the experimental design to others and ensures transparency and reproducibility in research.

Examples Of Control Variables

The Role Of Control Variables In An Experiment

The role of control variables can be understood more deeply through their contributions to validity and their function in eliminating alternative explanations.

Ensuring Validity

Validity refers to the extent to which the results of an experiment accurately reflect the phenomenon being studied. There are several types of validity, including internal validity (the degree to which the experiment accurately demonstrates a causal relationship) and external validity (the extent to which the results can be generalized to other settings). 

Eliminating Alternative Explanations

One of the primary purposes of control variables is to eliminate alternative explanations for the observed outcomes. In any experiment, multiple factors can influence the dependent variable. Without controlling for these factors, it would be difficult to ascertain whether the observed effects are due to the independent variable or some other variable.

Control variables help reduce various types of bias that could otherwise skew the results. For example, selection bias can be minimized by controlling demographic variables such as age and gender. Similarly, procedural biases can be reduced by standardizing the procedures across different experimental groups. By controlling these variables, researchers can minimize the influence of biases that might otherwise provide alternative explanations for the results.

Distinguishing Control Variables From Other Variables

Independent and dependent variables.

As defined in the Indeed career guide an independent variable is, “A variable that stands alone and isn’t changed by other variables or factors that are measured” whereas the dependent variable is, “A variable that relies on and can be changed by other factors that are measured” read more here.

Monitoring And Adjusting Control Variables

Regular monitoring.

• Continuously monitor control variables using appropriate tools and methods. For instance, use diet logs, exercise trackers, and periodic health check-ups to ensure participants adhere to the study protocol.

Implement Randomization

Randomly assign subjects or samples to experimental and control groups to minimize selection bias and ensure that control variables are evenly distributed.

If possible, implement single or double-blind procedures where participants and/or researchers do not know the group assignments. This helps reduce bias in handling and measurement.

Challenges In Managing Control Variables

Identifying hidden variables.

Imagine a study aiming to evaluate the effectiveness of a new teaching method on student performance in mathematics. The researchers compared two groups of students: one taught using the traditional method (control group) and the other taught using the new method (experimental group). The primary measure of success is the students’ scores on a standardized math test.

Hidden variable: Students’ socioeconomic status is a hidden variable that could significantly influence their academic performance. SES can affect access to resources such as private tutoring, books, parental support, and a conducive learning environment at home.

Strategies For Ensuring Consistency

• Standard Operating Procedures (SOPs): Implementing detailed SOPs for every aspect of the experiment can help ensure that control variables are managed consistently. SOPs should cover everything from how samples are collected and processed to how measurements are taken and recorded.
• Calibration and Maintenance of Equipment: Regular calibration and maintenance of equipment are essential to ensure that measurements remain consistent over time. Using the same equipment for all experimental groups can help minimize variability due to instrumentation.
• Training and Monitoring: Ensuring that all personnel involved in the experiment are thoroughly trained can help maintain consistency. Regular monitoring and periodic retraining can ensure that procedures are followed correctly throughout the duration of the study.
• Environmental Controls: For experiments sensitive to environmental conditions, using controlled environments like climate-controlled rooms or growth chambers can help maintain consistent conditions. Monitoring environmental variables continuously can help identify and correct any deviations promptly.
• Blinding: Implementing blinding techniques, where the person conducting the experiment does not know which group is the control and which is the experimental group, can help reduce bias and ensure that control variables are applied uniformly.

Despite these strategies, maintaining consistency in control variables can be labor-intensive and requires meticulous attention to detail. Variations, even if minor, can have significant impacts on the results, especially in experiments where the dependent variable is highly sensitive to changes in the control variables.

Mind the Graph: Our Platform Will Be Your Control Variable

The effective communication of results follows after your experimentation is concluded and, respectively, your hypothesis is tested. Communicate your results in an easy, clear, engaging, and visual way to ensure that your research gets across and resonates with people. Being your trustful partner, we can help you in this important part of your research.

Whether you are preparing a poster for an academic conference, writing a research paper, or creating a graphical abstract to summarize your study, we offer tools and services that support your illustrations. At Mind the Graph, we have a great tool for you to reach these purposes. We have a large free illustration gallery on our platform with themes targeting presentations of scientific studies, allowing you to find just the right graphics to illustrate your findings. With these illustrations, one can develop pleasant graphics with the objective of contextualizing relevant points of this research.

The designers are also there to provide personalized support so that your visuals are not just accurate from a scientific point of view but also professionally polished. Engaging with our team means you can get a greater visual impact with your research, enabling your audience to easily understand the complexities and value of the work.

We encourage researchers to explore the resources that Mind the Graph offers for free and the availability of expert design; we can help any researcher, whether a beginner or more experienced, to better communicate research outputs.

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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?

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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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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What Are Dependent, Independent & Controlled Variables?

What Is a Responding Variable in Science Projects?

Say you're in lab, and your teacher asks you to design an experiment. The experiment must test how plants grow in response to different colored light. How would you begin? What are you changing? What are you keeping the same? What are you measuring?

These parameters of what you would change and what you would keep the same are called variables. Take a look at how all of these parameters in an experiment are defined, as independent, dependent and controlled variables.

What Is a Variable?

A variable is any quantity that you are able to measure in some way. This could be temperature, height, age, etc. Basically, a variable is anything that contributes to the outcome or result of your experiment in any way.

In an experiment there are multiple kinds of variables: independent, dependent and controlled variables.

What Is an Independent Variable?

An independent variable is the variable the experimenter controls. Basically, it is the component you choose to change in an experiment. This variable is not dependent on any other variables.

For example, in the plant growth experiment, the independent variable is the light color. The light color is not affected by anything. You will choose different light colors like green, red, yellow, etc. You are not measuring the light.

What Is a Dependent Variable?

A dependent variable is the measurement that changes in response to what you changed in the experiment. This variable is dependent on other variables; hence the name! For example, in the plant growth experiment, the dependent variable would be plant growth.

You could measure this by measuring how much the plant grows every two days. You could also measure it by measuring the rate of photosynthesis. Either of these measurements are dependent upon the kind of light you give the plant.

What Are Controlled Variables?

A control variable in science is any other parameter affecting your experiment that you try to keep the same across all conditions.

For example, one control variable in the plant growth experiment could be temperature. You would not want to have one plant growing in green light with a temperature of 20°C while another plant grows in red light with a temperature of 27°C.

You want to measure only the effect of light, not temperature. For this reason you would want to keep the temperature the same across all of your plants. In other words, you would want to control the temperature.

Another example is the amount of water you give the plant. If one plant receives twice the amount of water as another plant, there would be no way for you to know that the reason those plants grew the way they did is due only to the light color their received.

The observed effect could also be due in part to the amount of water they got. A control variable in science experiments is what allows you to compare other things that may be contributing to a result because you have kept other important things the same across all of your subjects.

Graphing Your Experiment

When graphing the results of your experiment, it is important to remember which variable goes on which axis.

The independent variable is graphed on the x-axis . The dependent variable , which changes in response to the independent variable, is graphed on the y-axis . Controlled variables are usually not graphed because they should not change. They could, however, be graphed as a verification that other conditions are not changing.

For example, after graphing the growth as compared to light, you could also look at how the temperature varied across different conditions. If you notice that it did vary quite a bit, you may need to go back and look at your experimental setup: How could you improve the experiment so that all plants are exposed to as similar an environment as possible (aside from the light color)?

How to Remember Which is Which

In order to try and remember which is the dependent variable and which is the independent variable, try putting them into a sentence which uses "causes a change in."

Here's an example. Saying, "light color causes a change in plant growth," is possible. This shows us that the independent variable affects the dependent variable. The inverse, however, is not true. "Plant growth causes a change in light color," is not possible. This way you know which is the independent variable and which is the dependent variable!

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• NCES Kids: What are Independent and Dependent Variables?
• Khan Academy: Dependent and independent variables review (article)

About the Author

Riti Gupta holds a Honors Bachelors degree in Biochemistry from the University of Oregon and a PhD in biology from Johns Hopkins University. She has an interest in astrobiology and manned spaceflight. She has over 10 years of biology research experience in academia. She currently teaches classes in biochemistry, biology, biophysics, astrobiology, as well as high school AP Biology and Chemistry test prep.

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Control Variables in Statistics

Control Variable is a type of variable used to verify the accuracy of any experiment, as the control variable is an essential part of experimental design. Control Variables are used extensively in the field of research where experiments are conducted to compare the new approach to the standard baseline.

In this article, we will discuss the concept of the Control Variable in sufficient detail including its definition, and examples as well as its differences with dependent and independent variables.

Table of Content

What Is a Control Variable in Science?

Examples of control variables, independent, dependent, and control variables, importance of control variables.

A control variable, also known as a constant variable, is a variable that does not change during the investigation in scientific experiments. Its function is to serve as a reliable benchmark, assisting researchers in separating the impacts of the independent variable and guaranteeing that changes in the dependent variable that are noticed are due to deliberate manipulation and not extraneous influences.

Definition of Control Variable

A control variable in an experiment is a variable that is kept constant so as not to affect the result.

To confirm that any observed effects are most likely the result of the manipulated variables rather than outside influences, it is used to isolate and evaluate the impact of the independent variable(s) on the dependent variable.

Imagine conducting an experiment to determine how different musical genres affect focus. As control variables, the music’s volume, the lighting in the space, and the temperature of the room would enable researchers to concentrate only on how different types of music affect concentration without being distracted by other variables.

Example in Chemistry Experiment

• The concentration of a reactant is an independent variable.
• Reaction rate is a dependent variable.
• Pressure and temperature are control variables.

Example in Medical Experiment

• The new drug’s dosage is an independent variable.
• Patient recuperation time is a dependent variable.
• Exercise and diet are control variables.

Example in Physics Experiment

• Angle of inclination is an independent variable.
• A ball’s travel distance is a dependent variable.
• The variables under control are surface type and starting velocity.

Let us dissect these variables’ functions to better understand the differences between them:

• Independent Variable: The variable that the researcher modifies or manipulates is known as an independent variable.
• Dependent Variable: The variable being measured or watched for changes is known as the dependent variable.
• Control Variable: A variable that is maintained at a constant value to minimize any potential impact on the experiment.

Differences between Independent, Dependent, and Control Variables

The key difference between Independent, Dependent, and Control Variables are listed in the following table:

Control Variable are important because it:

• Controls for factors influencing dependent variable.
• Isolates manipulated independent variable’s impact.
• Eliminates alternative explanations for outcomes.
• Strengthens reproducibility of experiments.
• Accounts for variations across contexts.
• Considers factors affecting generalizability.
• Identifies factors impacting success/failure.
• Allows accurate group or condition comparisons.
• Ensures responsible and ethical research practices.
• Random Variables
• Discrete Random Variables
• Is a Variable Considered a Term?

Control Variable: FAQs

1. what is the meaning of control variable.

A factor intentionally kept constant in an experiment to isolate the effect of the independent variable.

2. What is an Example of Control Variable?

In a plant growth experiment, if researchers are testing the effect of different fertilizers on plant height, the amount of sunlight, water, and temperature should be kept constant (controlled) to make them control variables.

3. What is a Control Variable in an Experiment?

A control variable in an experiment is a factor that is intentionally kept constant and unchanged throughout the study.

4. How Control Variable is Used in Experiments?

In experiments, control variables are kept constant to isolate the effect of the independent variable on the dependent variable.

5. Why Control Variable is Used in Research Experiments?

Control variables are used in research experiments to eliminate or minimize the impact of extraneous factors that could affect the dependent variable.

6. What is the Other Name for Control Variable?

Control Variable is also knonw as Extraneous variable.

7. What is the key Difference between Control Variable and Independent Variable?

The key difference is their role in an experiment. The independent variable is manipulated to observe its effect, while the control variable is kept constant to eliminate potential confounding factors.

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What Is a Controlled Experiment?

Definition and Example

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A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

• A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
• A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
• The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

• Box, George E. P., et al.  Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005. 
• Creswell, John W.  Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
• Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
• Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
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More From Forbes

Think you’re in control the surprising ways illusions of control fool us.

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We all want to bend the world to our will, but more often than not, it is the other way around – a fact that we often fail to recognize. This tendency to exaggerate our ability to influence events is known as the illusion of control , a cognitive bias that causes us to overestimate our ability to control the world around us, leading to overconfidence in our ability to influence the outcomes of our decisions.

That can really cost you.

A 2003 study of traders working at four City of London investment banks found those “with a high propensity to illusion of control exhibit a lower profit performance and earn less than those with low illusion of control.” The same study also found a strong correlation “between illusion of control and poor risk management and analysis.”

We all want to bend the world to our will, but more often than not, it is the other way around – a ... [+] fact that we often fail to recognize. This tendency to exaggerate our ability to influence events is known as the illusion of control.

The illusion of control can lead executives to exaggerate their ability to find and recruit top talent, design winning products, or secure needed financing. It can also lead managers to spend too little time questioning their plans before executing them or doing the heavy lifting necessary to guarantee their success.

The reverse can be true as well.

Ironically, researchers also have found that, in situations in which people do have a great deal of control, they tend to underestimate the amount of control they actually exert. This underestimation can lead to poor decision making, for instance, by obscuring our awareness of how we contribute to a competitor’s success or by causing us to miss out on key opportunities.

Understanding the Illusion of Control

The illusion of control was first identified by psychologist Ellen Langer in the 1970s. She first observed this cognitive bias in situations involving chance, such as gambling or stock market investments, where outcomes are largely unpredictable. Langer’s experiments demonstrated that when individuals were given choices or allowed to practice, even in scenarios governed by pure chance, they exhibited greater confidence in their influence over the results.

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The roots of this bias can be traced to a fundamental human need for control, which is linked to survival instincts. Feeling in control reduces stress and anxiety, making us feel safer and more secure. Therefore, our brains often create an illusion of control to maintain a sense of order and predictability in an inherently chaotic world.

The illusion of control can significantly impact decision making in various domains, leading to overly optimistic assessments of risk and ability.

For example, in financial markets, traders might believe that their expertise or past success grants them control over market movements, prompting them to take unwarranted risks.

Similarly, in personal health, individuals might underestimate the role of chance in medical outcomes, leading to poor compliance with treatment protocols or preventative measures.

In organizational settings, leaders might fall prey to this bias by overestimating their ability to steer complex projects or influence organizational culture changes, ignoring the myriad external factors that play critical roles. This can result in inadequate planning, failure to anticipate challenges, and misallocation of resources.

Five Ways to Dispel Illusions of Control

Mitigating the effects of the illusion of control requires awareness, education, and specific cognitive strategies. Here are several approaches to counter this bias:

• Awareness and Education. Recognizing the existence of the illusion of control is the first step toward mitigation. Educational programs and workshops that focus on cognitive biases can equip individuals and organizations with the knowledge to identify and understand this phenomenon.
• Fostering a Realistic Assessment of Control. Individuals should be encouraged to critically assess which aspects of a situation they can influence and which are beyond their control. Techniques such as the locus of control assessment can help individuals distinguish between internal and external controls realistically.
• Encouraging Reflective Thinking. Reflective thinking involves questioning one's assumptions and beliefs about control. Practices such as keeping a decision journal where you record your predictions and the actual outcomes can help illustrate the role of chance and reduce bias over time.
• Promoting Group Decision-Making. Collective decision-making can help counter individual biases, including illusions of control. By incorporating diverse perspectives and challenging each other’s assumptions, groups can arrive at more balanced and realistic decisions.
• Red Teaming Important Decisions. Using decision support red teaming tools and techniques can help organizations better understand the myriad factors that can contribute to the success or failure of a plan or strategy, allowing decision makers to think more three-dimensionally before acting.

By understanding this bias and implementing strategies to counter it, individuals and organizations can enhance their decision-making processes, align expectations with reality, and ultimately achieve better outcomes. As we navigate a world teeming with uncertainty, fostering a realistic sense of control not only sharpens our decision making but also prepares us for the inevitable surprises life throws our way.

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