controlled variable in experiment example

25 Control Variables Examples

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Control variables, sometimes called “controlled” variables or “constant” variables, are elements within a study that researchers deliberately keep constant.

In a research study, it is often required to determine the possible impact of one or more independent variables on a dependent variable. To maintain the validity of the results, scientists keep certain variables in check, known as the control variables, ensuring they do not influence the study outcome.

Through careful control of these variables, scientists can prevent confounding effects, allowing for the clear understanding of the relationship between the independent and dependent variables (Scharrer & Ramasubramanian 2021; Knapp 2017).

Control Variables Examples

Here are some concrete examples to better understand the role of control variables:

1. Participant Age When studying the effect of a new teaching method on students’ mathematical abilities, the age of the participants (all students studied are in the 8th grade) remains a control variable.

2. Participant Gender In investigating the impact of a physical fitness program on participants’ cardiovascular health, researchers control for participants’ gender (only female participants are included).

3. Socioeconomic Status (SES) While examining the effect of job training programs on employment rates, scientists control the socioeconomic status of participants (all participants fall under the same socioeconomic category).

4. Educational Level In a research study examining the impact of management styles on worker productivity, educational level (all workers involved hold a Bachelor’s degree in their corresponding fields) is considered a control variable.

5. Cultural Background In studying the influence of music therapy on stress reduction, researchers maintain cultural background constant (only participants from a specific cultural group are included).

6. Time of Day If a researcher is testing the effect of caffeine on alertness, the time of day (all tests are conducted in the morning) is controlled to ensure that circadian rhythms do not confound results.

7. Previous Experience In evaluating the effectiveness of a new software tutorial, previous experience with the software (all participants are novice users) is hold constant to avoid confounding effects.

8. Medication Usage When researching the correlation between a balanced diet and blood pressure, medication usage (none of the participants are on any medication) is a control variable.

9. Sleep Quality In correlating cognitive performance and sleep patterns, sleep quality (all participants are healthy sleepers, as assessed by a sleep quality questionnaire) is maintained constant.

10. Hunger/Fullness While exploring the link between taste perception and caloric intake, researchers control for hunger/fullness (all tests are conducted two hours after a standardized meal) to eliminate any potential confounding effects.

11. Caffeine Intake When evaluating the impact of a mindfulness exercise on attention spans, caffeine intake (participants are required to abstain from caffeine on the day of the testing) is controlled.

12. Mental Health Status During a research study exploring the effects of exercise on sleep quality, the mental health status of participants (all participants do not have any known mental health issues as per a screening survey) is kept constant.

13. Motivation Level In research on the effectiveness of a language learning app, the motivation level (participants are all deemed to have a high level of motivation as assessed by a standardized motivational questionnaire) is a control variable.

14. Instructions Given When scientists are studying the effect of a new fitness routine on muscle strength, the instructions given (all participants receive the same detailed instructions about the exercises) remain consistent.

15. Testing Environment In studying the impact of ambient noise on focus and concentration, the testing environment (all testing is conducted in a silent room) is controlled for.

16. Researcher Presence While experimenting to assess the influence of color on memory recall, researcher presence (all testing happens without the presence of the researcher to avoid pressure or distraction) is kept constant.

17. Mode of Data Collection When comparing coping styles and resilience, mode of data collection (all data is collected through online self-report surveys) is controlled.

18. Order of Questionnaires or Tasks During a study to understand the relation between personality traits and career choices, the order of questionnaires or tasks (participants are all subjected to the tasks and questionnaires in the exact same order) is maintained same.

19. Familiarity with Technology In researching the benefits of virtual reality in improving social skills, the familiarity with technology (all participants have basic computer skills) is considered constant.

20. Expectations/Briefing In a study of the correlation between study habits and academic performance, expectations/briefing about the study (all participants receive the same briefing regarding what the study entails) is controlled to maintain uniformity.

21. Physical Activity Level In a study analyzing the correlation between diet and energy levels, the physical activity level of participants (all participants engage in a moderate level of daily physical activity) is controlled.

22. Stress Levels When researching the impact of sleep duration on cognitive functions , the stress level of participants (all participants have reported average stress levels on a standard stress scale) is kept constant.

23. Relationship Status In researching the influence of relationships on happiness levels, the relationship status of participants (all participants are single at the time of the study) is kept constant.

24. Number of Hours Worked Recently While examining the effect of work-life balance on the job satisfaction of employees, the number of hours worked recently (all employees have worked standard 40 hour weeks) is considered a control variable.

25. Current Emotional State In a study evaluating the impact of a relaxation technique on anxiety levels, the current emotional state of the participants (all participants have to record a neutral emotional state at the time of testing) is maintained constant.

Related: Quantitative Reasoning Examples

How to Control a Variable

Controlling a variable in a research study involves ensuring that it is kept constant or unchanged throughout the entire experiment.

This technique allows the researchers to focus on the potential relationship between the remaining variables, the independent variable(s) and the dependent variable (Sproull, 2002).

Here’s an outline of the process:

• Identify Potential Control Variables Before beginning the experiment, identify all the variables that might potentially affect the outcome of your research. This process can be informed by a literature review on similar studies, brainstorming sessions, or consultations with other professionals in the field.
• Define the Conditions of Control Set specific conditions for each control variable. For example, if you’re studying the effects of a new teaching method on student learning outcomes, the students’ grade level might be a control variable. You would then decide to limit your study to only 8th-grade students.
• Maintain Consistent Environment Ensure that the environment or conditions in which your research is carried out stay constant. Changes in external variables might indirectly alter your control variables.
• Monitor Regularly Record data related to your control variables regularly. If there are changes, they will need to be corrected or accounted for in your final analysis.
• Analyze the Confounding Effect Once your experiment is completed, you should perform a statistical analysis to ensure that your controlled variables did not influence the outcome.

By regularly monitoring and adjusting these variables, you can limit their influence on your study, increasing the odds that any observed effects are due to the independent variable(s).

It’s important to note that it’s not always possible to control every variable in a study and that’s okay. In such cases, it is important that the researchers are aware of these uncontrollable variables and can discuss their potential impact when interpreting the results.

Types of Control Variables: Positive and Negative

Positive and negative controls are two types of control groups in experimental research. They act as a benchmark and provide context for interpreting the results of the experiment.

• Positive control refers to a test where the outcome is already known from the onset. It is implemented to ensure that an experimental procedure is working as intended. It is crucial for validating the test results and serves as a benchmark for comparison. These controls are used across various disciplines, from biology to engineering, cultivates consistency, reliability, and accuracy in experimental work.
• Negative control is a test that anticipates a negative result. It is carried out to ensure that no change occurs when no experimental variable is introduced. The key purpose of such controls is to rule out other factors that might lead to a change in the outcome. Overall, negative controls add credence to the experimental process, helping to confirm that observed changes in the positive control or experimental test result from the factor being tested.

Both positive and negative controls contribute to experimental reliability and validity. They allow scientists to have confidence in their results by reducing the likelihood of experimental error. They also facilitate a better understanding of the experimental processes and outcomes, which is key in research and experimentation.

These controls are, in essence, safeguards against inaccurate or skewed results, ensuring that the conclusions drawn are as accurate as possible, thus avoiding misleading deductions.

Go Deeper: Positive Control vs Negative Control

Control vs Confounding Variables

Control Variables and Confounding Variables each have substantial importance in research studies, and need to be accounted for. Both types of variables can influence results, but they serve different roles in the research process.

• Control Variables: Control variables are the variables that researchers control throughout a study, usually by ensuring they remain consistent and unchanged throughout the study (Lock et al., 2020; Parker & Berman, 2016). By controlling these variables, researchers can reduce the number of extraneous factors that could interfere with the results, thereby minimizing potential error, ensuring the integrity of the experiment, and reducing the risk of false outcomes.
• Confounding Variables : Confounding variables may pose a risk to the validity of a study’s results (Nestor & Schutt, 2018). These are variables that researchers didn’t account for, and they may influence both the independent and dependent variables, making it hard to determine if the effects were caused by the independent variable or the confounder.

The primary difference between control and confounding variables is how they’re managed in a study. Control variables are identified and kept constant by the researcher to isolate the relationship between the independent and dependent variables (Boniface, 2019; Lock et al., 2020).

On the other hand, confounding variables are extraneous factors that can influence the study results and have not been controlled (Riegelman, 2020). While researchers aim to identify possible confounding variables before a study to control or account for them, they often become clear during or after the experiment, introducing uncertainty about causation between dependent and independent variables.

Control variables are critical to maintaining the integrity and validity of research studies. By carefully selecting and managing these variables, researchers can limit confounding influences, allowing them to focus on the relationship between the independent and dependent variables. Understanding control variables assists researchers in developing robust study designs and reliable findings.

Boniface, D. R. (2019). Experiment Design and Statistical Methods For Behavioural and Social Research . CRC Press. ISBN: 9781351449298.

Knapp, H. (2017). Intermediate Statistics Using SPSS. SAGE Publications.

Lock, R. H., Lock, P. F., Morgan, K. L., Lock, E. F., & Lock, D. F. (2020). Statistics: Unlocking the Power of Data (3rd ed.). Wiley.

Nestor, P. G., & Schutt, R. K. (2018). Research Methods in Psychology: Investigating Human Behavior . SAGE Publications.

Parker, R. A., & Berman, N. G. (2016). Planning Clinical Research . Cambridge University Press.

Riegelman, R. K. (2020). Studying a Study and Testing a Test (7th ed.). Wolters Kluwer Health.

Scharrer, E., & Ramasubramanian, S. (2021). Quantitative Research Methods in Communication: The Power of Numbers for Social Justice . Taylor & Francis.

Sproull, N. L. (2002). Handbook of Research Methods: A Guide for Practitioners and Students in the Social Sciences . Scarecrow Press.

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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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Methodology

• Control Variables | What Are They & Why Do They Matter?

Control Variables | What Are They & Why Do They Matter?

Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 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 objectives , 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 randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests). Control variables can help prevent research biases like omitted variable bias from affecting your results.

Table of contents

Why do control variables matter, how do you control a variable, control variable vs. control group, other interesting articles, 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 and helps avoid research bias .

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 and affect the reliability of your arguments.

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 studies 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 use more screen time during the day, and this might influence how alert they are in your study.

Standardized 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 types of 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.

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

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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 .

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.

“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.

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Home » Control Variable – Definition, Types and Examples

Control Variable – Definition, Types and Examples

Table of Contents

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 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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• Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

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You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

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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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• Controlled Experiments: Methods, Examples & Limitations

What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the dependent variables. 

Therefore, when the experiment is controlled, you can expect that the researcher will control all other variables except for the independent variables . This is done so that the other variables do not have an influence on the dependent variables. 

In this article, we are going to consider controlled experiment, how important it is in a study, and how it can be designed. But before we dig deep, let us look at the definition of a controlled experiment.

What is a Controlled Experiment?

In a scientific experiment, a controlled experiment is a test that is directly altered by the researcher so that only one variable is studied at a time. The single variable being studied will then be the independent variable.

This independent variable is manipulated by the researcher so that its effect on the hypothesis or data being studied is known. While the researcher studies the single independent variable, the controlled variables are made constant to reduce or balance out their impact on the research.

To achieve a controlled experiment, the research population is mostly distributed into two groups. Then the treatment is administered to one of the two groups, while the other group gets the control conditions. This other group is referred to as the control group.

The control group gets the standard conditions and is placed in the standard environment and it also allows for comparison with the other group, which is referred to as the experimental group or the treatment group. Obtaining the difference between these two groups’ behavior is important because in any scientific experiment, being able to show the statistical significance of the results is the only criterion for the results to be accepted.  

So to determine whether the experiment supports the hypothesis, or if the data is a result of chance, the researcher will check for the difference between the control group and experimental group. Then the results from the differences will be compared with the expected difference.

For example, a researcher may want to answer this question, do dogs also have a music taste? In case you’re wondering too, yes, there are existing studies by researchers on how dogs react to different music genres. 

Back to the example, the researcher may develop a controlled experiment with high consideration on the variables that affect each dog. Some of these variables that may have effects on the dog are; the dog’s environment when listening to music, the temperature of the environment, the music volume, and human presence. 

The independent variable to focus on in this research is the genre of the music. To determine if there is an effect on the dog while listening to different kinds of music, the dog’s environment must be controlled. A controlled experiment would limit interaction between the dog and other variables. 

In this experiment, the researcher can also divide the dogs into two groups, one group will perform the music test while the other, the control group will be used as the baseline or standard behavior. The control group behavior can be observed along with the treatment group and the differences in the two group’s behavior can be analyzed. 

What is an Experimental Control?

Experimental control is the technique used by the researcher in scientific research to minimize the effects of extraneous variables. Experimental control also strengthens the ability of the independent variable to change the dependent variable.

For example, the cause and effect possibilities will be examined in a well-designed and properly controlled experiment if the independent variable (Treatment Y) causes a behavioral change in the dependent variable (Subject X).

In another example, a researcher feeds 20 lab rats with an artificial sweetener and from the researcher’s observation, six of the rats died of dehydration. Now, the actual cause of death may be artificial sweeteners or an unrelated factor. Such as the water supplied to the rats being contaminated or the rats could not drink enough, or suffering a disease. 

Read: Nominal, Ordinal, Interval & Ratio Variable + [Examples]

For a researcher, eliminating these potential causes one after the other will consume time, and be tedious. Hence, the researcher can make use of experimental control. This method will allow the researcher to divide the rats into two groups: one group will receive the artificial sweetener while the other one doesn’t. The two groups will be placed in similar conditions and observed in similar ways. The differences that now occur in morbidity between the two groups can be traced to the sweetener with certainty.

From the example above, the experimental control is administered as a form of a control group. The data from the control group is then said to be the standard against which every other experimental outcome is measured.

Purpose & Importance of Control in Experimentation

1. One significant purpose of experimental controls is that it allows researchers to eliminate various confounding variables or uncertainty in their research. A researcher will need to use an experimental control to ensure that only the variables that are intended to change, are changed in research.  

2. Controlled experiments also allow researchers to control the specific variables they think might have an effect on the outcomes of the study. The researcher will use a control group if he/she believes some extra variables can form an effect on the results of the study. This is to ensure that the extra variable is held constant and possible influences are measured.  

3. Controlled experiments establish a standard that the outcome of a study should be compared to, and allow researchers to correct for potential errors. 

Read more: What are Cross-Sectional Studies: Examples, Definition, Types

Methods of Experimental Control

Here are some methods used to achieve control in experimental research

• Use of Control Groups

Control groups are required for controlled experiments. Control groups will allow the researcher to run a test on fake treatment, and comparable treatment. It will also compare the result of the comparison with the researcher’s experimental treatment. The results will allow the researcher to understand if the treatment administered caused the outcome or if other factors such as time, or others are involved and whether they would have yielded the same effects.  

For an example of a control group experiment, a researcher conducting an experiment on the effects of colors in advertising, asked all the participants to come individually to a lab. In this lab,  environmental conditions are kept the same all through the research.

For the researcher to determine the effect of colors in advertising, each of the participants is placed in either of the two groups: the control group or the experimental group.

In the control group, the advertisement color is yellow to represent the clothing industry while blue is given as the advertisement color to the experimental group to represent the clothing industry also. The only difference in these two groups will be the color of the advertisement, other variables will be similar.

• Use of Masking (blinding)

Masking occurs in an experiment when the researcher hides condition assignments from the participants.  If it’s double-blind research, both the researcher and the participants will be in the dark. Masking or blinding is mostly used in clinical studies to test new treatments.

Masking as a control measure takes place because sometimes, researchers may unintentionally influence the participants to act in ways that support their hypotheses. In another scenario, the goal of the study might be revealed to the participants through the study environment and this may influence their responses.

Masking, however, blinds the participants from having a deeper knowledge of the research whether they’re in the control group or the experimental group. This helps to control and reduce biases from either the researcher or the participants that could influence the results of the study.

• Use of Random Assignment

Random assignment or distribution is used to avoid systematic differences between participants in the experimental group and the control group. This helps to evenly distribute extraneous participant variables, thereby making the comparison between groups valid. Another usefulness of random assignment is that it shows the difference between true experiments from quasi-experiments.

Learn About: Double-Blind Studies in Research: Types, Pros & Cons

How to Design a Controlled Experiment

For a researcher to design a controlled experiment, the researcher will need:

• A hypothesis that can be tested.
• One or more independent variables can be changed or manipulated precisely.
• One or more dependent variables can be accurately measured.

Then, when the researcher is designing the experiment, he or she must decide on:

• How will the variables be manipulated?
• How will control be set up in case of any potential confounding variables?
• How large will the samples or participants included in the study be?
• How will the participants be distributed into treatment levels?

How you design your experimental control is highly significant to your experiment’s external and internal validity.

Controlled Experiment Examples

1. A good example of a controlled group would be an experiment to test the effects of a drug. The sample population would be divided into two, the group receiving the drug would be the experimental group while the group receiving the placebo would be the control group (Note that all the variables such as age, and sex, will be the same).

The only significant difference between the two groups will be the taking of medication. You can determine if the drug is effective or not if the control group and experimental group show similar results. 

2. Let’s take a look at this example too. If a researcher wants to determine the impact of different soil types on the germination period of seeds, the researcher can proceed to set up four different pots. Each of the pots would be filled with a different type of soil and then seeds can be planted on the soil. After which each soil pot will be watered and exposed to sunlight.

The researcher will start to measure how long it took for the seeds to sprout in each of the different soil types. Control measures for this experiment might be to place some seeds in a pot without filling the pot with soil. The reason behind this control measure is to determine that no other factor is responsible for germination except the soil.

Here, the researcher can also control the amount of sun the seeds are exposed to, or how much water they are given. The aim is to eliminate all other variables that can affect how quickly the seeds sprouted. 

Experimental controls are important, but it is also important to note that not all experiments should be controlled and It is still possible to get useful data from experiments that are not controlled.

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Problems with Controlled Experiments

It is true that the best way to test for cause and effect relationships is by conducting controlled experiments. However, controlled experiments also have some challenges. Some of which are:

• Difficulties in controlling all the variables especially when the participants in your research are human participants. It can be impossible to hold all the extra variables constant because all individuals have different experiences that may influence their behaviors.
• Controlled experiments are at risk of low external validity because there’s a limit to how the results from the research can be extrapolated to a very large population .
• Your research may lack relatability to real world experience if they are too controlled and that will make it hard for you to apply your outcomes outside a controlled setting.

Control Group vs an Experimental Group

There is a thin line between the control group and the experimental group. That line is the treatment condition. As we have earlier established, the experimental group is the one that gets the treatment while the control group is the placebo group.

All controlled experiments require control groups because control groups will allow you to compare treatments, and to test if there is no treatment while you compare the result with your experimental treatment.

Therefore, both the experimental group and the control group are required to conduct a controlled experiment

FAQs about Controlled Experiments

• Is the control condition the same as the control group?

The control group is different from the control condition. However, the control condition is administered to the control group. 

• What are positive and negative control in an experiment?

The negative control is the group where no change or response is expected while the positive control is the group that receives the treatment with a certainty of a positive result.

While the controlled experiment is beneficial to eliminate extraneous variables in research and focus on the independent variable only to cause an effect on the dependent variable.

Researchers should be careful so they don’t lose real-life relatability to too controlled experiments and also, not all experiments should be controlled.

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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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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Controlled Variable

Reviewed by: BD Editors

A controlled variable is a commonly used term in the field of scientific research, where finding evidence to support a theory is rarely straightforward. In the case of the natural sciences, some research features are constant, but the majority of these have inconsistencies. These inconsistencies are known as variables.

For an experiment to give statistically useful results , every aspect of the study subject and the environment must be the same, or as similar as possible . Studies are made up of independent variables (the effects of a specific change the researcher wishes to observe), the dependent variable (the measurement of this change), and the controlled variable. A controlled variable creates a similar environment across the board, so that the change that is being studies is not influenced by multiple, uncontrolled factors.

If seedlings are being tested for their rates of growth at two different light levels, the results of the independent variable (light levels) and the dependent variable (millimeters of growth) will be much more accurate if the seedlings are exactly the same. This does not only refer to their genetic make-up (the size of the seed, the parent plants, the species), but also external variables such as temperature, moisture levels, soil mineral content, air quality, position, and many others.

By using genetically cloned seeds in a carefully prepared growth medium placed inside a closed and highly controlled environment, and by following exact schedules for sowing and measuring times – as is the case in the image above – this study may then come to the conclusion that any changes in growth are due to light levels, rather than to other changes. Controlled variables should make study subjects and their environment as similar as possible. The perfect experiment controls all variables except the dependent variable – the result.

Controlled Variable Examples

In science, and in basic and applied research, variables are innumerable . From the simplest of elements to the most complex organisms, any number of differences can change the results of a line of study. The conclusions of an experiment carried out in one facility can differ to that of another, even when the same methods are applied . Living organisms are often too complicated to be expected to react in exactly the same way, whether this refers to the research subject, or to the researcher.

Non-Living Materials

Controlled variable examples in non-living materials are easier to implement than in research on living organisms. Research that looks at the reaction of one non-living material to another has the potential to implement near-perfect experimental controls. One example of a study on non-living materials could be the testing of two different smoothing processes on four different brands of dental cement. Testing can be carried out ‘ in vitro’ , meaning outside of a living organism, and thereby removing countless potential variables.

Controlled variables of this experiment would include application method and materials, light-curing intensities on the cement, specimen storage (temperature and duration), the length of time of the polishing process, the settings of the electron microscope, and the rotation speed of the polishing device. The addition of a control group would be a subdivision of the controlled variable. A control group is a group that undergoes the same preparation and is kept in the same environment as the tested samples, but is not exposed to the independent variable . In the above example, the control group features cement left unpolished. This removes the possibility of natural processes such as oxidation or air humidity that might affect the smoothness of untreated cement being counted towards the effect of the mechanical smoothing processes.

What this experiment would find difficult to control would be how identical each cement sample would be, as manufactured samples can differ. The distribution of ingredients in manufactured compounds can not be considered to be identical unless stringent tests are carried out before research commences.

Living Organisms

Controlled variable examples for living organisms are much more complex than in the majority of research upon non-living materials. In more complex and naturally produced living organisms, variables are predominantly uncontrolled. This is the primary reason why very simple organisms like fruit flies, or very similar organisms such as genetically cloned mice and rats, are used in testing environments. Once statistically relevant results are available in non-human models, human testing is initiated on groups that are as non-diverse as possible. The graphic below shows the steps all FDA-approved medications must go through. From the pre-clinical stage to stage III of the clinical trial, the possibilities for implementing controlled variables drastically diminish.

Advertisements for research subjects often ask for people of a certain age group, gender, or body mass index. They also refer to medical, behavioral or lifestyle variables such as no cigarette or alcohol consumption, no medication use, no co-morbidities, and medium to high levels of exercise . By implementing controlled variables early on, a researcher can create a group where results are more generic .

If a potential study subject fits this initial brief they are usually invited for further analysis. In the case of a weight-loss drug, for example, this might include insulin resistance and glucose testing, endocrine function , blood count, heart and lung function tests, and medical and familial history taking. Examples of controlled variables at this phase might be candidates without any family history of diabetes, or those who pass a specific psychological test.

Initial trials are able to study the effects of treatment in a very generic and similar population. The importance of psychological variables – something less influential in animal models – is never underestimated. However, once a drug, a chemical, or a therapy must be tested on the population for which it is designed, controlled variables become more difficult to achieve. In the case of the weight-loss drug example, a morbidly obese adult with a binge-eating disorder, sedentary lifestyle, anxiety and diabetes may not respond in the same way as a slightly overweight adult who gained weight after breaking a leg and has no comorbidities. But to which variables can a difference in response be attributed?

In all types of research, limiting other factors which may change either the action of the independent variable, or the result of the dependent variable is essential to obtain the best quality data. As organisms become more complex, the ability to limit these factors progressively decreases. By implementing as many controlled variables as possible, scientific evidence becomes more accurate and is a more solid and trustworthy foundation for the next generation of researchers.

What Exactly are Variables?

In experimental processes, variables can influence the final results. Researchers must attempt to limit these variables to the specific changes they are studying. A variable represents anything that undergoes change . Variables may be temperature fluctuations, comorbidities, behaviors, environments, diet, air quality, stress levels, metabolism, or allergies. Even seasonal or global events may have an effect upon the final results.

For research purposes, variables are categorized into three groups. The first is the independent (or manipulated) variable – the change that is consciously made in order to study a particular action or reaction, or change that is independent of our control, namely time and the ageing process.

The second variable is the dependent (or responding) variable, which the researcher measures in order to come to the final result. For example, a study may look at the effect a serving of blueberries has to the results of a color-coded memory test. The independent variable is the dietary change (blueberries). The dependent variable is the memory test used to measure whether blueberries affect the memory. It is easy to envision how potential variables can limit the accuracy of the researcher’s findings. Did the subject get a good night’s sleep? Did the subject feel unwell at the time? Did the subject understand the concept of the game? Is the subject color-blind? To limit these variables, this study requires a third type of variable – the controlled variable.

1. Which of the following is a definition of a dependent variable?

2. Which of these is not a controlled variable?

3. The further along the research route a clinical drug trial, the less the controlled variables.

4. Which of the following provides the most controlled variables?

5. A control group:

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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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Controlled Variables

Controlled variables are variables that is sometimes overlooked by researchers, but it is usually far more important than the dependent or independent variables.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

A failure to isolate the controlled variables, in any experimental design , will seriously compromise the internal validity . This oversight may lead to confounding variables ruining the experiment , wasting time and resources, and damaging the researcher's reputation.

In any experimental design, a researcher will be manipulating one variable , the independent variable , and studying how that affects the dependent variables .

Most experimental designs measures only one or two variables at a time. Any other factor, which could potentially influence the results , must be correctly controlled. Its effect upon the results must be standardized, or eliminated, exerting the same influence upon the different sample groups .

For example, if you were comparing cleaning products, the brand of cleaning product would be the only independent variable measured. The level of dirt and soiling, the type of dirt or stain, the temperature of the water and the time of the cleaning cycle are just some of the variables that must be the same between experiments. Failure to standardize even one of these controlled variables could cause a confounding variable and invalidate the results.

Control Groups

In many fields of science, especially biology and behavioral sciences, it is very difficult to ensure complete control , as there is a lot of scope for small variations.

Biological processes are subject to natural fluctuations and chaotic rhythms. The key is to use established operationalization techniques, such as randomization and double blind experiments . These techniques will control and isolate these variables, as much as possible. If this proves difficult, a control group is used, which will give a baseline measurement for the unknown variables.

Sound statistical analysis will then eliminate these fluctuations from the results. Most statistical tests have a certain error margin built in, and repetition and large sample groups will eradicate the unknown variables.

There still needs to be constant monitoring and checks, but due diligence will ensure that the experiment is as accurate as is possible.

The Value of Consistency

It is important to ensure that all these possible variables are isolated, because a type III error may occur if an unknown factor influences the dependent variable . This is where the null hypothesis is correctly rejected, but for the wrong reason.

In addition, inadequate monitoring of controlled variables is one of the most common causes of researchers wrongly assuming that a correlation leads to causality .

Controlled variables are the road to failure in an experimental design , if not identified and eliminated. Designing the experiment with controls in mind is often more crucial than determining the independent variable .

Poor controls can lead to confounding variables , and will damage the internal validity of the experiment.

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Martyn Shuttleworth (Jun 2, 2008). Controlled Variables. Retrieved Aug 28, 2024 from Explorable.com: https://explorable.com/controlled-variables

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