Your school science class might be accustomed to performing science experiments with only a single manipulated variable, but a gap between school science and science performed in laboratories throughout the world exists. The short answer to whether scientists can use more than one manipulated variable in their experiments is “yes.” But just as important as the answer to this question is understanding why scientists would want to include two manipulated variables.
Scientists Are Manipulative
One of the key aims of science is to make changes to things and see how those things react. When performing a science experiment, a scientist knows what she plans to manipulate, or change. This thing might be the temperature of a chemical liquid, the length of time she allows a plant to grow, or the type of drug she gives to a lab mouse. Scientists are always looking for changes that matter. When they suspect a certain change might matter, they label the change the “manipulated variable.” For example, when giving a mouse a certain drug and timing how long it takes it to complete a maze, the scientist is considering the drug her manipulated variable. The word comes from her ability to “manipulate” what drug the mouse receives. She might be selecting from a choice of two or three, which would give the manipulated variable two or three values.
The question of whether a science experiment can have two manipulated variables brings up another important question: Assuming that experiments can include two manipulated variables, why would a scientist bother to include more than one? The truth is, sometimes scientists suspect the simultaneous change of two different variables as being the real reason for a result. For example, variable 1 by itself might not have any effect on the responding variable alone. But when a scientist manipulates variable 1 and variable 2, she might see a significant change in the responding variable. Another reason to manipulate more than one variable in an experiment is if you want to control something that you think might be affecting the results. For example, if you are growing multiple plants and your manipulated variable is “amount of sunlight,” you might be surprised to see that the plants with more sunlight aren’t growing as fast as you thought. If you suspect that those plants aren’t growing fast enough because you’re giving them too little water, you might change the amount of water you give them, too. Your second manipulated variable would then be “amount of water,” and you would have four types of plants: much sunlight, much water; much sunlight, little water; little sunlight, much water; and little sunlight, little water.
Trouble around the Corner
The fact is, according to NC State University, scientists can include as many manipulated variables in their experiments as they want. The statistics behind all sciences allows for multiple manipulated variables and provides scientists many tools to evaluate the results of a study using many manipulated variables. But scientists don’t always purposely include multiple manipulated variables in their research. If they did, they would have to deal with increases in the difficulty of the experiment design in terms of price; time; number of samples, such as lab rats, needed; and complexity of the statistical tools that scientists use to evaluate results. You might have noticed school science fairs and experiments mainly using a single manipulated experiment and began to wonder whether two manipulated variables is a possibility. Well, while nothing is wrong with two manipulated variables, most teachers don’t want to handle the complexity of multiple manipulated variables. Adding more manipulated variables to a class experiment would confuse most students and sometimes the teacher himself. (But don’t mention that to your teacher.)
Rats, Rats, and More Rats: An Example
Scientists working with lab rats might suspect that lab rats with certain genes are more likely to die early but only when that group of lab rats eats a high-fat diet. So, scientists would need to check for the existence of this “cooperative change,” what scientists call an “interaction effect.” The scientists could then divide rats into two sets of two groups: One set being those with the gene and those without the gene; the other set being those who receive a high fat diet and those that don’t. Only then can scientists check whether it is the combination of a high fat diet and existence of a certain gene that leads to early death.
About the Author
Having obtained a Master of Science in psychology in East Asia, Damon Verial has been applying his knowledge to related topics since 2010. Having written professionally since 2001, he has been featured in financial publications such as SafeHaven and the McMillian Portfolio. He also runs a financial newsletter at Stock Barometer.