Control 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. Control variables are different from control groups.
What is a Control Variable?
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. Keep these variables 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 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.
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 you don’t control the relevant variables, you might need to attribute the changes to confounders rather than the treatment. Control variables reduce the impact of confounding variables.
Control Variable Examples
|Does a medicine reduce illness?||
|Are different weight loss programs effective?||
|Do kiln time and temperature affect clay pot quality?||
|Does a supplement improve memory recall?||
How to Control Variables in Science
Scientists can control variables using several methods. In some cases, they can hold them constant intentionally. For example, they 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.
Related post: Control Groups in Experiments
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 averages out all the traits of the experimental groups, making them roughly the same 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.
Controlling 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 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.