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.
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.
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.
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]