In scientific research, controlling variables is crucial for obtaining reliable and valid results. Control variables are factors that researchers deliberately keep constant or unchanging throughout an experiment or study to isolate the effects of the independent variable on the dependent variable. By controlling these variables, researchers can eliminate or minimize their potential influence on the outcome, allowing them to establish a cause-and-effect relationship between the variables of interest.
Examples of control variables
To better understand control variables, consider the following examples from various research contexts:
| Research Question | Control Variables |
| How does the amount of fertilizer affect plant growth? | Soil type, water amount, sunlight exposure, temperature, container size, etc. |
| What is the effect of a new teaching method on student performance? | Teacher qualifications, class size, subject matter, socioeconomic status of students, etc. |
| Does a new medication effectively lower blood pressure? | Age, gender, diet, physical activity levels, existing medical conditions, etc. |
| How does the color of a product’s packaging influence consumer purchasing decisions? | Product type, pricing, advertising, store layout, demographics of participants, etc. |
Why do control variables matter?
Control variables are essential in research for several reasons:
- Isolating the effects of the independent variable: By controlling extraneous variables, researchers can isolate the influence of the independent variable on the dependent variable, allowing them to establish a causal relationship.
- Increasing internal validity: Internal validity refers to the degree to which the observed effects can be attributed solely to the independent variable and not to other confounding factors. Controlling variables helps enhance internal validity by minimizing the potential influence of extraneous variables.
- Improving replicability: When researchers control variables consistently across studies, it becomes easier to replicate the findings, further strengthening the validity and generalizability of the results.
- Minimizing bias: Uncontrolled variables can introduce bias and systematic errors in the research, leading to inaccurate or misleading conclusions. Controlling variables helps reduce bias and ensures that the results are reliable and representative of the phenomenon under investigation.
Control variables in experiments
In experimental research, controlling variables is essential for establishing cause-and-effect relationships. Here’s an example:
Example: Experiment
You want to investigate the effect of different types of music (independent variable) on task performance (dependent variable). To control for extraneous variables, you might:
- Use participants with similar age, education level, and musical background.
- Conduct the experiment in a quiet room with consistent lighting and temperature.
- Provide standardized instructions and task materials to all participants.
- Randomize the order in which participants are exposed to different music conditions.
By controlling these variables, you can isolate the effect of the music type on task performance and minimize the influence of other factors.
Control variables in non-experimental research
Even in non-experimental research, where manipulating variables is not possible, controlling variables is crucial for drawing valid conclusions. Here’s an example:
Example: Non-experimental design
In a correlational study examining the relationship between social media use and academic performance, researchers might control for variables such as:
- Socioeconomic status
- Age and grade level
- School quality
- Extracurricular activities
- Parental involvement
By controlling for these variables, researchers can better understand the true relationship between social media use and academic performance, independent of other potential influences.
How do you control a variable?
There are several techniques researchers use to control variables. These can also be used in quasi-experimental designs or observational studies. They are:
Random assignment
Random assignment is a powerful technique used in experimental research to control for potential confounding variables. By randomly assigning participants to different treatment conditions, researchers ensure that any systematic differences between groups are due to chance, rather than pre-existing factors.
Example: Random assignment
In a study investigating the effectiveness of a new teaching method, researchers could randomly assign students to either the experimental group (using the new teaching method) or the control group (using the traditional teaching method). This random assignment helps control for factors like student ability, motivation, and prior knowledge, which could otherwise influence the results.
Standardized procedures
Standardized procedures involve implementing consistent protocols, instructions, and conditions across all participants or groups in a study. This helps control for variables that could potentially affect the outcome.
Example: Standardized procedures
In a psychological experiment studying the effects of sleep deprivation on cognitive performance, researchers might standardize the following procedures:
- Provide all participants with the same set of instructions and practice trials.
- Administer the cognitive tests in a controlled laboratory environment with consistent lighting, temperature, and noise levels.
- Ensure all participants follow the same sleep deprivation protocol and have their sleep monitored consistently.
Statistical controls
Statistical controls involve using advanced analytical techniques to account for the influence of potential confounding variables on the outcome variable. This approach is particularly useful in non-experimental research where random assignment or direct manipulation of variables is not possible.
Example: Statistical control
In a study examining the relationship between exercise and mental health, researchers might use statistical techniques like multiple regression analysis or propensity score matching to control for variables like age, gender, income, and pre-existing health conditions. These statistical controls help isolate the unique contribution of exercise to mental health outcomes.
Control variable vs. control group
- Control variables are factors that researchers keep constant or unchanging across all participants or conditions in a study to isolate the effects of the independent variable.
- Control groups are groups of participants who do not receive the experimental treatment or intervention. They serve as a baseline or comparison group to evaluate the effects of the treatment on the experimental group.
In experimental research, control groups are essential for establishing cause-and-effect relationships, while control variables help ensure that any observed differences between the experimental and control groups are due to the independent variable, rather than extraneous factors.