control variable research 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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• What Are Control Variables | Definition & Examples

What Are Control Variables? | Definition & Examples

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

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

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

Table of contents

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

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

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

Control variables in experiments

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

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

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

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

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

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

• Marital status

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

Random assignment

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

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

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

Standardised procedures

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

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

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

Statistical controls

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

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

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

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

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

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

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

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

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

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

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

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

• Duration of the experiment
• Size and composition of containers
• Temperature
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 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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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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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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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable?

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

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What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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Published by Nicolas at March 17th, 2024 , Revised On March 12, 2024

A Practical Guide to Choosing the Right Control Variables for Your Research

Research is a dynamic process, where scientists strive to unravel the mysteries of the world through systematic inquiry. In this pursuit, control variables play a crucial role in shaping the reliability and validity of research findings. This blog serves as a practical guide to aid researchers in the thoughtful selection of control variables.

Table of Contents

“Control variables, often referred to as covariates, are elements in a study that are intentionally held constant or systematically manipulated to examine their impact on the relationship between independent and dependent variables. These variables act as safeguards against confounding factors, ensuring that the observed effects can be attributed more accurately to the independent variable under investigation.

Importance of Selecting the Right Control Variables

The choice of control variables is not arbitrary; it demands careful consideration and a deep understanding of the research context. The significance of selecting the right control variables cannot be overstated, as these elements serve as the bedrock for establishing the internal validity of a study.

Internal validity refers to the accuracy of causal inferences within an experiment – the extent to which changes in the dependent variable can be confidently attributed to manipulating the independent variable.

By meticulously selecting control variables, researchers can minimize the risk of alternative explanations, ensuring that observed effects are more likely to reflect true causal relationships.

How Control Variables Enhance Research Validity

Research validity is a multifaceted concept that encompasses various dimensions, including internal, external, construct, and statistical validity. Control variables primarily enhance internal validity by minimizing the influence of extraneous variables that could introduce bias or confound the results.

Researchers create a more controlled and precise experimental environment by strategically incorporating control variables. This, in turn, allows for a clearer understanding of the relationship between the independent and dependent variables, bolstering the overall validity of the research findings.

In essence, control variables act as gatekeepers, fortifying the integrity of the research process and paving the way for more robust and trustworthy scientific conclusions.

Understanding Control Variables

Control variables, also known as covariates, are integral components of experimental design and statistical analysis in research. Their primary purpose is to add precision to investigations by accounting for potential confounding factors that might otherwise distort the interpretation of results. 

For instance, imagine a study examining the impact of a new drug on patients’ recovery time after surgery. The type of anesthesia used, the patient’s age, and pre-existing health conditions are all factors that could influence the recovery time. 

By identifying and controlling for these variables, researchers can more confidently attribute any observed changes in recovery time to the specific effects of the drug being studied.

How Control Variables Differ from Independent and Dependent Variables

To grasp the role of control variables, it is essential to differentiate them from independent and dependent variables. The researcher manipulates or selects independent variables to observe their effect on the dependent variable.

On the other hand, dependent variables are the outcomes or responses measured in the experiment, dependent on the changes in the independent variable.

Control variables, however, are not the variables of primary interest. Instead, they are chosen to minimize the influence of extraneous variables that might interfere with the relationship between the independent and dependent variables. While independent and dependent variables are central to the research question, control variables act as safeguards to ensure the integrity and validity of the study.

Examples of Control Variables

Control variables are versatile and their selection depends on the specifics of each study. 

In social science research, control variables may include demographic factors like age, gender, and socioeconomic status.

In experimental studies in the physical sciences, factors such as temperature, humidity, or pressure might be controlled to isolate the effects of the manipulated variables.

Consider a psychological study exploring the impact of a new therapy on reducing anxiety levels. Control variables in this scenario could include the participants’ previous experiences with therapy, baseline anxiety levels, or even the time of day the therapy sessions are conducted.

These variables, when controlled, allow the researcher to attribute any observed changes in anxiety levels more confidently to the therapeutic intervention.

Criteria for Selecting Control Variables

The following are the criteria for selecting the right control variables.

Relevance to the Research Question

One of the foremost considerations when selecting control variables is their relevance to the research question or thesis statement . The chosen control variables should have a logical and theoretical connection to the study, aligning with the overarching objectives. 

Researchers must carefully evaluate whether the control variables are likely to influence the relationship between the independent and dependent variables. A judicious selection based on relevance ensures that the controlled factors contribute meaningfully to the study’s internal validity.

Potential Confounding Factors

Control variables act as a shield against confounding factors—variables that might distort the observed relationship between the independent and dependent variables. Identifying potential confounding factors requires an understanding of the subject and a thorough literature review. 

Researchers must anticipate variables that could muddy the waters and strategically incorporate them as control variables to isolate the effects of the independent variable accurately.

Feasibility and Practicality

While researchers aim for inclusivity in control variable selection, practical considerations cannot be ignored. Feasibility and practicality play a pivotal role in the decision-making process. 

Researchers must assess whether the chosen control variables are measurable, obtainable, and manageable within the constraints of the study. Pragmatic decisions ensure that the research remains feasible without compromising the overall quality and validity.

Balance Between Inclusivity and Specificity

Achieving a delicate balance between inclusivity and specificity is crucial in control variable selection. Including too few control variables may leave the study vulnerable to lurking confounders, while an overly exhaustive list may complicate the analysis and risk diluting the primary focus. 

Researchers must strike a balance, aiming for inclusivity without sacrificing the specificity necessary to draw meaningful and precise conclusions from the data.

Common Pitfalls in Control Variable Selection

Here are some common pitfalls in control variable selection.

Overlooking Relevant Variables

One common pitfall in control variable selection is overlooking variables that could significantly impact the study’s outcomes. Researchers may inadvertently omit relevant factors that, when unaccounted for, introduce bias or confound the results. 

Rigorous literature reviews and a comprehensive understanding of the research domain are crucial in avoiding this oversight.

Including Unnecessary Variables

Conversely, the inclusion of unnecessary variables poses another challenge. Researchers may be tempted to incorporate a multitude of control variables without clear theoretical or empirical justification. 

This not only complicates the study unnecessarily but can also lead to overfitting models, reducing the generalizability of findings. Prudent selection is key to avoiding this pitfall.

Confusing Control Variables with Mediators or Moderators

Control variables should not be confused with mediators or moderators . Mediators explain how an independent variable affects a dependent variable, while moderators influence the strength or direction of the relationship between the independent and dependent variables. 

Confusing these concepts can lead to misinterpretation of results and compromise the overall integrity of the study. Researchers must delineate between control variables, mediators, and moderators to ensure accurate analyses.

Strategies for Identifying Control Variables

You can identify control variables with the help of the following strategies.

Literature Review and Prior Research

A robust literature review is a cornerstone for identifying relevant control variables. Existing research provides valuable insights into potential factors that could confound or influence the relationships under investigation. 

By examining similar studies and drawing on the collective knowledge within the field, researchers can identify common control variables used by peers and gain a better understanding of the variables that warrant consideration in their own work.

Preliminary Data Analysis

Conducting preliminary data analysis can unearth patterns and relationships that may guide the selection of control variables. Exploratory data analysis allows researchers to identify potential confounding factors by examining correlations, patterns, and outliers.

By scrutinizing the data before formal analysis, researchers can make informed decisions about which variables to control for, refine their study design, and ensure a more robust research paper approach.

Expert Consultation and Peer Feedback

Seeking input from experts in the field and obtaining peer feedback can provide valuable perspectives on control variable selection. Collaborating with colleagues who have expertise in the subject or statistical methods can offer fresh insights and help researchers consider variables they might have overlooked. 

Peer review processes also serve as a checkpoint, allowing external experts to assess the validity and appropriateness of chosen control variables.

Documentation and Transparency

Thorough documentation of control variable choices is essential for the transparency and replicability of research. Researchers should meticulously record the rationale behind each control variable selection, detailing the theoretical or empirical basis for inclusion. 

This documentation serves as a critical reference point for both internal and external stakeholders, aiding in the understanding and evaluation of the study’s design and validity.

Case Studies

Here are some case studies to help you better understand control variables.

Examining real-world examples of well-selected control variables can provide valuable insights into effective research practices. In a study investigating the impact of a nutritional intervention on weight loss, well-chosen control variables might include participants’ baseline body mass index (BMI), exercise habits, and pre-existing medical conditions.

 These control variables help ensure that observed changes in weight can be confidently attributed to the nutritional intervention, minimizing the influence of extraneous factors.

In another example, a social science study exploring the effects of a community development program may appropriately control for demographic factors such as income, education level, and employment status. By doing so, the researchers can isolate the specific impact of the intervention on community outcomes without the interference of socioeconomic disparities.

Analysis of Studies with Inadequate Control Variable Selection

Conversely, inadequate control variable selection can compromise the validity of study findings. For instance, a study examining the effectiveness of a new teaching method in improving student performance may fall short if it fails to control for factors like students’ prior academic achievement, socio-economic background, or teacher-student ratios.

In such cases, the observed improvements in student performance may be confounded by these uncontrolled variables, making it challenging to attribute the effects solely to the teaching method.

Similarly, a health-related study investigating the impact of a wellness program may encounter issues if it neglects to control for participants’ pre-existing health conditions or lifestyle factors. Without proper controls, the study risks drawing inaccurate conclusions about the program’s effectiveness.

Lessons Learned from Real-World Examples

Analyzing case studies with both effective and inadequate control variable selection provides valuable lessons for researchers. It underscores the importance of understanding the research context and the critical role that control variables play in ensuring the internal validity of a study. 

Researchers can learn to anticipate potential confounding factors, appreciate the complexity of real-world scenarios, and recognize the significance of meticulous control variable selection in generating trustworthy research outcomes.

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Practical Tips for Implementing Control Variables

With the help of these tips, you can implement control variables. 

Tip 1: Monitoring and Adjusting Control Variables During the Research Process

The research process is dynamic, and unforeseen variables may emerge. Researchers should adopt a proactive approach to monitor and adjust control variables as necessary throughout the study. 

Regularly assessing the relevance and impact of control variables allows researchers to adapt to changing circumstances, ensuring that the study remains robust and that unexpected confounding factors are addressed promptly.

Tip 2: Using Statistical Techniques to Assess the Impact of Control Variables

Statistical techniques can aid researchers in assessing the impact of control variables on study outcomes. Regression analysis, for example, allows researchers to examine how changes in the independent variable relate to changes in the dependent variable while holding control variables constant. 

This analysis helps quantify the contribution of each variable and ensures that control variables are appropriately considered in the interpretation of results.

Tip 3: Considerations for Longitudinal or Experimental Studies

Longitudinal or experimental studies present unique challenges in control variable selection. In longitudinal studies, where data is collected over an extended period, researchers must carefully choose control variables that account for changes over time. 

In experimental studies, the manipulation of variables introduces complexities that require strategic control variable selection. Researchers should be attuned to their study design, ensuring that control variables are relevant and measurable, and effectively mitigate potential confounding factors specific to their experimental or longitudinal context.

Frequently Asked Questions

What are the examples of variable control.

Examples of variable control include maintaining consistent temperature in a scientific experiment, controlling for participants’ age and gender in social research, or standardizing testing conditions to isolate the impact of an independent variable on a dependent variable.

What are 3 controlled variables?

• Temperature: Ensuring a constant temperature in an experiment to isolate the effects of other variables.
• Time: Controlling the duration of an experiment to prevent time-related influences on the dependent variable.
• Light: Standardizing light conditions to eliminate its impact on experimental outcomes.

What are system control variables?

System control variables are parameters or factors intentionally regulated or kept constant in a system to observe the impact of independent variables. By controlling these elements, researchers can isolate and assess the effects of specific variables on the system’s behaviour or outcomes.

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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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• Research Methodology

What Are Control Variables? Definition, Uses & Examples

The knowledge of control variables is extremely important for the students and academic professionals which will give us critical insight into how it improves our research outcome. So, first, we will start with the control variables’ definitions and examples associated with them.  

A control variable is an experimental element which is constant or limited throughout the course of the research investigation. More often, the control variables may not have a direct interest in the aim and objectives of the study, but it tends to have a significant influence on the resulting outcome of the research. 

Not just the control variables definition , but we will put forth the examples of it for better clarity. 

For example, to evaluate the effect of soil quality on plant growth, the temperature, light and water are held constant during an experiment, which is referred to as the controlled variables. 

Similarly, to investigate the relationship between happiness and income, we measure the control variables of age, health and marital status. 

Let us provide some more examples for better understanding!!

In research studies and experiments, the aim is to understand the impact of an independent variable on a dependent variable. The control variables ensure to keep the experimental results are fair and unskewed devoid of any experimental manipulation. 

The above examples indicate that control variables ensure the results obtained are solely dependent on the experimental evaluation. The variables independent or dependent, are not the primary focus of any research, rather keeping their values constant throughout helps in the establishment of true correlations between the dependent and independent variables. Now don’t get confused between the control variables and control groups as they strike a stark distinction. 

In the research methodology , the use of control variables must be identified with recorded values to evaluate the results with precision. Moreover, the implication of control variables increases the internal validity of your research study which is otherwise a pretty difficult task to attain. To be specific, internal validity improves the degree of confidence in the differences you observe in the findings and attain the correct conclusions. 

Why is it important to have control variables?  

Very simple!! If researchers do not have control variables planned in the research methodology , it will become difficult to figure out or prove their exact impact on the results. It is crucial to find out whether the results of the research are an effect of the independent variable to justify experimental errors. Moreover, controlled variations are important because even the slightest variations in the research findings could have a significant influence on the results. Another major advantage of control variables points out the convenience of reproducing any research study while creating a strong relationship between the dependent and independent variables. 

 Taking over the examples set above, while we try to determine the effect of soil quality on plant growth, the independent variable refers to the soil quality whereas the dependent variable indicates the rate of plant growth. Hence, if we do not have control over the soil quality, we may end up with skewed results which may distort the actual outcome of the study.  

Approaches to control variables in Research  

You can make use of several approaches to control the variables in a research study. In some scenarios, variables can be controlled directly or by using standardized procedures which will be discussed further. 

• Some experiments can have several variables to control whereas in some cases the researchers may not be aware of all the potential variables that need to be controlled. Now, this sounds confusing, isn’t it? The random process controls of variables ensure to average of all the associated traits across the experimental groups which make them roughly equivalent while the experiment begins to start. Such random process controls prevent the occurrence of systematic differences between the multiple variable groups. 

Nevertheless, the direct approach and random control of the variables are effective in equalizing the experimental groups, however, it may not be feasible always. So we apply the statistical approaches for better clarity in the process. 

• Statistical techniques such as multiple regression do not create a balance between variable groups, rather it employs a robust model which can statistically control the variables. For example, the multiple regression analysis includes a variable within the model and holds it constant while the treatment variables keep on fluctuating. You can implement ANCOVA and ANOVA for the same process. 

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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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A controlled variable is one which the researcher holds constant (controls) during an experiment. It is also known as a constant variable or simply as a "control." The control variable is not part of an experiment itself—it is neither the independent nor dependent variable —but it is important because it can have an effect on the results. It is not the same as a control group.

Any given experiment has numerous control variables, and it's important for a scientist to try to hold all variables constant except for the independent variable. If a control variable changes during an experiment, it may invalidate the correlation between the dependent and independent variables. When possible, control variables should be identified, measured, and recorded.

Examples of Controlled Variables

Temperature is a common type of  controlled variable . If a temperature is held constant during an experiment, it is controlled.

Other examples of controlled variables could be an amount of light, using the same type of glassware, constant humidity, or duration of an experiment.

Importance of Controlled Variables

Although control variables may not be measured (though they are often recorded), they can have a significant effect on the outcome of an experiment. Lack of awareness of control variables can lead to faulty results or what are called "confounding variables." Additionally, noting control variables makes it easier to reproduce an experiment and establish the relationship between the independent and dependent variables .

For example, say you are trying to determine whether a particular fertilizer has an effect on plant growth. The independent variable is the presence or absence of the fertilizer, while the dependent variable is the height of the plant or rate of growth. If you don't control the amount of light (e.g., you perform part of the experiment in the summer and part during the winter), you may skew your results.

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Chapter 3 -- Introducing a Control Variable (Multivariate Analysis)

Human behavior is usually too complicated to be studied with only two variables. Often we will want to consider sets of three or more variables (called multivariate analysis ). We will want to consider three or more variables when we have discovered a relationship between two variables and want to find out 1) if this relationship might be due to some other factor, 2) how or why these variables are related, or 3) if the relationship is the same for different types of individuals. In each situation, we identify a third variable that we want to consider. This is called the control or the test variable . (Although it is possible to use several control variables simultaneously, we will limit ourselves to one control variable at a time.) To introduce a third variable, we identify the control variable and separate the cases in our sample by the categories of the control variable. For example, if the control variable is age divided into these two categories--younger and older, we would separate the cases into two groups. One group would consist of individuals who are younger and the other group would be those who are older. We would then obtain the crosstabulation of the independent and dependent variables for each of these age groups. Since there are two categories in this control variable, we obtain two partial tables , each containing part of the original sample. (If there were three categories in our control variable, for example, young, middle aged, and old, we would have three partial tables.) The process of using a control variable in the analysis is called elaboration and was developed at Columbia University by Paul Lazarsfeld and his associates. There are several different types of outcomes to the elaboration process. We will discuss each briefly. Table 2.3 showed that females were more likely than males to say they were willing to vote for a woman. Let's introduce a control variable and see what happens. In this example we are going to use age as the control variable. Table 3.1 is the three-variable table with voting preference as the dependent variable, sex as the independent variable, and age as the control variable. When we look at the older respondents (the left-hand partial table), we discover that this partial table is very similar to the original two-variable table (Table 2.3). The same is true for the younger respondents (the right-hand partial table). Each partial table is very similar to the original two-variable table. This is often referred to as replication because the partial tables repeat the original two-variable table (see Babbie 1997: 393-396). It is not necessary that they be identical; just that each partial table be basically the same as the original two-variable table. Our conclusion is that age is not affecting the relationship between sex and voting preference. In other words, the difference between males and females in voting preference is not due to age. Table 3.1 -- Voting Preference by Sex Controlling for Age   Older Younger   Male  %  Female  %  Total  %  Male  %  Female  %  Total  %  Voting Preference             Willing to Vote for a Woman 43.8  56.1  49.0  44.2  55.8  52.9  Not Willing to Vote for a Woman 56.2  43.9  51.0  55.8  44.2    100.0  100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  Since this is a hypothetical example, imagine a different outcome. Suppose we introduce age as a control variable and instead of getting Table 2.1, we get Table 3.2. How do these two tables differ? In Table 3.2, the percentage difference between males and females has disappeared in both of the partial tables. This is called explanation because the control variable, age, has explained away the original relationship between sex and voting preference. (We often say that the relationship between the two variables is spurious , not genuine.) When age is held constant, the difference between males and females disappears. The difference in the relationship does not have to disappear entirely, only be reduced substantially in each of the partial tables. This can only occur when there is a relationship between the control variable (age) and each of the other two variables (sex and voting preference). Next, we are interested in how or why the two variables are related. Suppose females are more likely than males to vote for a woman and that this difference cannot be explained away by age or by any other variable we have considered. We need to think about why there might be such a difference in the preferences of males and females. Perhaps females are more often liberal Table 3.2 -- Voting Preference by Sex Controlling for Age   Older Younger   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 32.9  33.9  33.3  65.8  66.9  66.7  Not Willing to Vote for a Woman 67.1  66.1  66.7  34.2  33.1  33.3    100.0 100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  than males, and liberals are more likely to say they would vote for a woman. So we introduce liberalism/conservatism as a control variable in our analysis. If females are more likely to support a woman because they are more liberal, then the difference between the preferences of men and women should disappear or be substantially reduced when liberalism/conservatism is held constant. This process is called interpretation because we are interpreting how one variable is related to another variable. Table 3.3 shows what we would expect to find if females supported the woman because they were more liberal. Notice that in both partial tables, the differences in the percentages between men and women has disappeared. (It is not necessary that it disappears entirely, but only that it is substantially reduced in each of the partial tables.) Table 3.3 -- Voting Preference by Sex Controlling for Liberalism/Conservatism   Older Younger   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 32.9  33.9  33.3  65.8  66.9  66.7  Not Willing to Vote for a Woman 67.1  66.1  66.7  34.2  33.1  33.3    100.0  100.0  100.0  100.0  100.0  100.0    (240)  (180)  (420)  (120)  (360)  (480)  Finally, let's focus on the third of the situations outlined at the beginning of this section--whether the relationship is the same for different types of individuals. Perhaps the relationship between sex and voter preference varies with other characteristics of the individuals. Maybe among whites, females are more likely to prefer women candidates than the males are, but among blacks, there is little difference between males and females in terms of voter preference. This is the outcome shown in Table 3.4. This process is called specification because it specifies the conditions under which the relationship between sex and voter preference varies. In the earlier section on bivariate analysis, we discussed the use of chi square. Remember that chi square is a test of independence used to determine if there is a relationship between two variables. Chi square is used in multivariate analysis the same way it is in bivariate analysis. There will be a separate value of chi square for each partial table in the multivariate analysis. You should keep a number of warnings in mind. Chi square assumes that the expected frequencies for each cell are five or larger. As long as 80% of these expected frequencies are five or larger and no single expected frequency is very small, we don't have to worry. However, the expected frequencies often drop below five when the number of cases in a column or row gets too small. If this should occur, you will have to either recode (i.e., combine columns or rows) or eliminate a column or row from the table. Table 3.4 -- Voting Preference by Sex Controlling for Race   White African American   Male %  Female %  Total %  Male %  Female %  Total %  Voting Preference             Willing to Vote for a Woman 42.9  56.5  51.2  50.0  50.0  50.0  Not Willing to vote for a Woman 57.1  43.5  48.8  50.0  50.0  50.0    100.00  100.00  100.00  100.00  100.00  100.00    (310)  (490)  (800)  (50)  (50) (100)  Another point to keep in mind is that chi square is affected by the number of cases in the table. With a lot of cases it is easy to reject the null hypothesis of no relationship. With a few cases, it can be quite hard to reject the null hypothesis. Also, consider the percentages within the table. Look for patterns. Do not rely on any single piece of information. Look at the whole picture. We have concentrated on crosstabulation and chi square. There are other types of statistical analysis such as regression and log-linear analysis. When you have mastered these techniques, look at some other types of analysis. REFERENCES AND SUGGESTED READING Methods of Social Research Riley, Matilda White. 1963. Sociological Research I: A Case Approach . New York: Harcourt, Brace and World. Survey Research and Sampling Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA: Wadsworth. Babbie, Earl R. 1997. The Practice of Social Research (8 th Ed.). Belmont, CA: Wadsworth.   Statistical Analysis K noke, David, and George W. Bohrnstedt. 1991. Basic Social Statistics . Itesche, IL: Peacock. Riley, Matilda White. 1963. Sociological Research II Exercises and Manual . New York: Harcourt, Brace & World. Norusis, Marija J. 1997. SPSS 7.5 Guide to Data Analysis . Upper Saddle River, New Jersey: Prentice Hall. Elaboration and Causal Analysis Hirschi, Travis and Hanan C. Selvin. 1967. Delinquency Research--An Appraisal of Analytic Methods . New York: Free Press. Rosenberg, Morris. 1968. The Logic of Survey Analysis . New York: Basic Books. Data Sources The Field Institute. 1985. California Field Poll Study, July, 1985 . Machine-readable codebook. The Field Institute. 1991. California Field Poll Study, September, 1991 . Machine-readable codebook. The Field Institute. 1995. California Field Poll Study, February, 1995. Machine-readable codebook.

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Control Variables in Statistical Studies

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Control variables play a pivotal role in scientific methodologies , ensuring the integrity of research outcomes. These are specific variables that researchers keep constant throughout an experiment to minimize external influence and ensure that the results accurately reflect the relationship between independent vs. dependent variables .

Inhaltsverzeichnis

• 1 Control Variables – In a Nutshell
• 2 Definition: Control variables
• 3 The importance of control variables
• 4 How to control variables
• 5 Control variables vs. Control groups

Control Variables – In a Nutshell

• Control variables let us control an experiment’s conditions via set criteria by improving statistical repeatability, validity, and applicability.
• Control variables protect the independent and dependent variables of the study from undue bias (e.g. confounding causative variables).
• How and which variables are selected depends on the topic and mode of study.
• Control variables are expressed as simple quantities (numerical) or qualitative statements.
• Experimental variables govern ‘test lab’ conditions via stabilization.
• Non-experimental variables help eliminate bias in active (wild) observation, where complete control is impossible.

Definition: Control variables

Control variables are essential empirical tools. They help us create replicable, verifiable data (i.e. statistics) from direct experimentation, observation, and sampling by setting hard limits. They also allow us to bypass ‘false’ causatives in observational studies.

Variables may be expressed as a qualitative or quantitative statement and may be limited (i.e. within a set range) or total. Integers (i.e. whole numbers) are usual for statistical controls. A single study may use many different control variables.

The importance of control variables

Control variables are crucial, as they greatly enhance an experiment’s internal validity. When you’re assessing a study’s statistical power, internal validity plays an important role.

Why? Quality, empirical statistics must reflect ‘control’ conditions relevant to that topic (i.e. model reality) to their absolute best. Improving internal validity means a study becomes more reliable, repeatable, broadly applicable, and likely to survive intense peer review.

Using control variables in experiments

Experimental control values let us isolate the dependent and independent variables in a closed environment. They’re the ‘lab’ variety.

In research, to see if adding a different mineral to soil stimulates houseplant growth, set control variables must detail how much sunlight, water, and air each plant should receive. As these factors are recognized as growth causatives (i.e. positive and negative confounders), quantitive nominal amounts may be required.

To do so, the scientists may read past papers, search through data about chronic conditions and rainfall, and examine similar nearby plants in the wild. Afterwards, they may agree that the plants should all receive 8.0 hours of daily light and 500 ml of daily water in a closed, single-fan-circulated shed. As a result, they may reach a consensus on their chosen brand of compost, pot diameter and volume, and the species and variety of plant seed.

Due to control variables, the experiment’s active and passive constants and constraints may now be ready and the effects of the independent variable (i.e. mineral type) on the dependent variable (i.e. plant growth rate) may now be safely observable.

Using control variables in non-experimental studies

Non-experimental control variables are similar. However, they’re tailored much more towards validating observations of natural phenomena, particularly human behavior.

Non-experimental variables are helpful when potential confounding causative factors (i.e. income, age, gender) may not be removed entirely from samples for ethical, legal, or practical reasons. Instead, they monitor or neutralize data on known causatives.

Suppose a freak lightning strike incinerates the carefully planned plant lab. The paper is due for submission in two weeks. There’s no money left. How will the researchers create useable statistics now?

One generous scientist suggests they may borrow their garden and spare (uniform) compost bags as a ‘real’ test bed. However, a few non-experimental design changes are needed first so that this new, natural approach works.

It’s impossible to control the rainfall and sunlight each day outdoors. However, they may be tracked instead by converting the set of experimental variables into monitored, non-experimental categories. Statistical reasoning (i.e. mean value calculation) allows the scientists to work out expected ‘real’ growth ranges for the plants they study, limiting bias.

How to control variables

Three advanced techniques that use control variables help remove bias from sample sets – if applied correctly. Here’s how they work.

Control variable methodology – Random assignment

If you have a set prone to outliers or clusters with wildly different behavior, you may want to make sure ‘lucky’ or ‘unlucky’ samples don’t skew your findings.

Researchers may use variables to ‘scramble’ set populations containing biased sub-sets, offsetting sampling bias. Random assignment ensures that sub-samples, with a balanced demographic ratio, occur. Pure luck determines which sample points are selected.

Here’s another example.

The unfortunate scientists double-check their equipment to find they have accidentally purchased three different varieties of tomato seed packet plant – ‘Medium-Mato’, ‘Mini-Mato’, and ‘Mega-Mato’ (x 100). A conundrum!

Luckily, the scientists also find past papers indicating these three varieties map comfortably onto a standard natural distribution. The researchers quickly pour the seeds into a container and shake it thoroughly for ten minutes before selecting exactly 100 samples (c.33%). Here, randomization guards against confounding influence.

Modern statistical studies may use a digital database to calculate random samples rather than a simple glass jar. Demographic weighting (i.e. stratification) might also be applied to better model divided random populations.

Control variable methodology – Standardized procedures

Do you have a repetitive daily routine that governs your time? Experiments often do. The exact time and way something must be delivered may have surprising, unforeseen effects on a subject.

It’s therefore critical that all manual test procedures remain uniform. We may set variables as timed and listed instructions to ensure this happens. They may also exclude any eccentric behavior that might skew the tests.

Let’s look at our final practical example.

Forward-thinking researchers set an additional control rule that outside plants should be watered at 10 AM and 5 PM if it hasn’t rained for two consecutive days. They also specify that no researchers should substitute cups of tea for rain-trough water as an exclusionary control.

Control variable methodology – Statistical controls

If everything else fails? You can apply statistical control methods to limit bias via final analysis.

Sometimes, removing all traces of extraneous influence is impossible. By applying modeling, weighting, and averaging based on what’s known about the factors you’re trying to account for, a more realistic statistical picture may emerge.

Applying multiple linear regression may help. By using averaged predictors as hypotheticals to weight and limit your values, trends and correlations may be better isolated.

Control variables vs. Control groups

Control variables shouldn’t be confused with control groups ! Control groups are governed by control variables, allowing the creation of a ‘neutral’ sub-sample.

What's a control variable?

A scientific safeguard that details a factor in a study that should (ideally) be kept the same. Control variables can also set out what factors should be accounted for and excluded from causative arguments.

Why do we use control variables?

Control variables add immense statistical power and validity. They’re an easy-to-use, effective way to guard against confounding factors that might warp our understanding of a complex topic.

What can control variables do?

A good set of variables may create an unchanging, dependable ‘test chamber’. Within, researchers may modify an independent variable to see how it affects a dependent one without risk.

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Control Variables: Problematic Issues and Best Practices

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Schjoedt and Sangboon hold a positivist ideology. In this chapter they discuss an important aspect of the unit of analysis strategy in research designs: How does one account for or control factors that the researcher is aware of in the model but are beyond the focus of a within-groups or between-groups comparison? In other words, control factors are confounding, moderating, or mediating variables. The reason it is important to identify and control (or account for) these factors is so that the researcher can generalize to other populations, that is, by identifying the confounding factors that are present but are beyond the unit of analysis interest. When participants are samples for a between-group unit of analysis comparison, individual attributes in each participant often differ. Designing control variables is one approach among others to address this.

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Schjoedt, L., Sangboon, K. (2015). Control Variables: Problematic Issues and Best Practices. In: Strang, K.D. (eds) The Palgrave Handbook of Research Design in Business and Management. Palgrave Macmillan, New York. https://doi.org/10.1057/9781137484956_15

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Research and simulation analysis of fuzzy intelligent control system algorithm for a servo precision press.

1. Introduction

2. principle of servo precision press and transmission mechanism, 3. design of fuzzy pid controller for servo precision press, 3.1. determining the structure of the fuzzy controller, 3.2. fuzzification of input and output quantities, 3.3. determination of domain and related parameters, 3.4. principles of parameter adjustment and establishment of fuzzy rules, 3.5. fuzzy inference rules, 4. experimental study on the position of the slider in servo precision press, 4.1. implementation plan, 4.2. step response experiment, 4.3. sinusoidal tracking experiment, 5. servo high-precision press closed-loop control system design, 5.1. servo motor transfer function block diagram, 5.2. current loop block diagram, 5.3. speed loop block diagram, 5.4. position ring block diagram, 6. control system simulation analysis based on simulink, 6.1. dynamics simulation analysis of each stamping stage, 6.2. dynamic simulation analysis with load, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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He, Y.; Luo, X.; Wang, X. Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press. Appl. Sci. 2024 , 14 , 6592. https://doi.org/10.3390/app14156592

He Y, Luo X, Wang X. Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press. Applied Sciences . 2024; 14(15):6592. https://doi.org/10.3390/app14156592

He, Yanzhong, Xiang Luo, and Xingsong Wang. 2024. "Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press" Applied Sciences 14, no. 15: 6592. https://doi.org/10.3390/app14156592

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Research on the output power control strategy of wind turbine considering the effect of complex wind speed

This paper studies how to improve the power output and the stability of the power generation system under the influence of different wind speed. This paper proposes a power control strategy based on PID controller using the optimal tip speed ratio method to optimize the control parameters and combine the variable pitch control method to realize the effective power control of 1.5MW direct drive permanent magnet synchronous wind turbine. The simulation results prove that the controller can improve the dynamic performance of the 1.5MW direct drive permanent magnet synchronous wind turbine power control system, improve the reliability of the unit operation, especially the capture efficiency of the wind energy of the power generation system, so as to improve the power generation.

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Research: People Still Want to Work. They Just Want Control Over Their Time.

• Stephanie Tepper
• Neil Lewis, Jr.

It’s a strong predictor for how satisfied they are with their work — and their lives.

To better understand the role that control over one’s time plays in job and life satisfaction, the authors analyzed survey data from a nationally representative sample. They found: 1) People who had greater control over their time had the highest job satisfaction and overall satisfaction with their lives, 2) Those who felt a sense of time scarcity had less satisfaction with their jobs and were less satisfied with their lives, 3) The number of hours people worked was not related to how satisfied people were with their jobs, and 4) For those who had more control over their time, feeling time scarcity did not undermine their job satisfaction as much as it did for those who had less control over their time. Employers should therefore create and tailor flexible work policies to meet diverse employee needs, fostering satisfaction and retention.

Workers — particularly those considered “ knowledge workers ” who are able to do most if not all of their work with a laptop and an internet connection — have been fighting for the right to maintain control over their time for years. While working from home in 2020 and 2021, they demonstrated to their bosses that they are able to maintain, or in some cases even increase , their productivity while working flexibly. Their bosses, on the other hand, have been pulling them in the opposite direction; executives and managers have been fighting to get workers back into the offices that companies are paying a lot of money to lease. This struggle has affected workers and companies alike. Workers quit en masse during a period that became known as “ the Great Resignation ,” and employers who instituted return-to-office mandates have struggled to hire and retain top talent . Now, especially with Gen Z making up an increasing share of the working population and the conversations around hybrid work and returning to the office stagnating, demands for increased flexibility in work arrangements are still top of mind for many employees and job seekers.

• ST Stephanie Tepper is a behavioral scientist who studies behavioral and policy interventions to reduce economic inequality and promote economic opportunity. She is an Associate Fellow at the U.S. Office of Evaluation Sciences and a Postdoctoral Scholar at Jeb E. Brooks School of Public Policy at Cornell University.
• NL Dr. Neil Lewis Jr is a behavioral scientist who studies the motivational, behavioral, and equity implications of social interventions and policies. He is a Nancy and Peter Meinig Family Investigator in the Life Sciences at Cornell University and Weill Cornell Medicine, where he is also associate professor of communication, medicine, and public policy.

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• Types of Variables in Research & Statistics | Examples

Types of Variables in Research & Statistics | Examples

Published on September 19, 2022 by Rebecca Bevans . Revised on June 21, 2023.

In statistical research , a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

If you want to test whether some plant species are more salt-tolerant than others, some key variables you might measure include the amount of salt you add to the water, the species of plants being studied, and variables related to plant health like growth and wilting .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, other interesting articles, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts
• Categorical data represents groupings

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variables can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables .

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is color-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms “dependent” and “independent” don’t apply, because you are not trying to establish a cause and effect relationship ( causation ).

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e. the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variables are listed below.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

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