The independent and dependent variables are the two main types of variables in a science experiment. A variable is anything you can observe, measure, and record. This includes measurements, colors, sounds, presence or absence of an event, etc.
The independent variable is the one factor you change to test its effects on the dependent variable. In other words, the dependent variable “depends” on the independent variable. The independent variable is sometimes called the controlled variable, while the dependent variable may be called the experimental or responding variable.
- The independent variable is the one you control or manipulate. The dependent variable is the one that responds and that you measure.
- The independent variable is the cause, while the dependent variable is the effect.
- Graph the independent variable on the x-axis. Graph the dependent variable on the y-axis.
How to Tell the Independent and Dependent Variable Apart
Both the independent and dependent variables may change during an experiment, but the independent variable is the one you control, while the dependent variable is one you measure in response to this change. The easiest way to tell the two variables apart is to phrase the experiment in terms of an “if-then” or “cause and effect” statement. If you change the independent variable, then you measure its effect on the dependent variable. The cause is the independent variable, while the effect is the dependent variable. If you state “time spent studying affect grades” (independent variables determines dependent variable), the statement makes sense. If your cause and effect statement is in the wrong order (grades determine time spent studying), it doesn’t make sense.
Sometimes the independent variable is easy to identify. Time and age are almost always the independent variable in an experiment. You can measure them, but you can’t control any factor to change them.
Ask yourself these questions to help tell the two variables apart:
Independent Variable
- Can you control or manipulate this variable?
- Does this variable come first in time?
- Are you trying to tell whether this variable affects an outcome or answers a question?
Dependent Variable
- Does this variable depend on another variable in the experiment?
- Do you measure this variable after controlling another factor?
Examples of Independent and Dependent Variables
For example, if you want to see whether changing dog food affects your pet’s weight, you can phrase the experiment as, “If I change dog food, then my dog’s weight may change.” The independent variable is the type of dog food, while the dog’s weight is the dependent variable.
In an experiment to test whether a drug is an effective pain reliever, the presence, absence, or dose of the drug is the variable you control (the independent variable), while the pain level of the patient is the dependent variable.
In an experiment to determine whether ice cube shapes determine how quickly ice cubes melt, the independent variable is the shape of the ice cube, while the time it takes to melt is the dependent variable.
If you want to see if the temperature of a classroom affects test score, the temperature is the independent variable. Test scores are the dependent variable.
Graphing Independent and Dependent Variables With DRYMIX
By convention, the independent variable is plotted on the x-axis of a graph, while the dependent variable is plotted on the y-axis. Use the DRY MIX acronym to remember the variables:
D is the dependent variable
R is the variable that responds
Y is the y-axis or vertical axis
M is the manipulated or controlled variable
I is the independent variable
X is the x-axis or horizontal axis
References
- Carlson, Robert (2006). A Concrete Introduction to Real Analysis. CRC Press.
- Edwards, Joseph (1892). An Elementary Treatise on the Differential Calculus (2nd ed.). London: MacMillan and Co.
- Everitt, B. S. (2002). The Cambridge Dictionary of Statistics (2nd ed.). Cambridge UP. ISBN 0-521-81099-X.
- Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
- Quine, Willard V. (1960). “Variables Explained Away“. Proceedings of the American Philosophical Society. American Philosophical Society. 104 (3): 343–347.