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
- Humidity
- Sample volume
- Pressure
- 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.
References
- 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