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Scientific Method

Independent and Dependent Variables


In an experiment, the independent variable is the variable that is varied or manipulated by the researcher.

The dependent variable is the response that is measured.  One way to think about it is that the dependent variable depends on the change in the independent variable.  In theory, a change in the independent variable will lead to a change in the dependent variable.

Example 1:

In a study of how different doses of a drug affect the severity of symptoms, a researcher could compare the frequency and intensity of symptoms when different doses are administered.

Here the independent variable is the dose and the dependent variable is the frequency/intensity of symptoms.

Example 2:

The rudder on a boat directs the course of the boat.  By changing the position of the rudder (turning it left or right), the rudder moves a certain way in the water, and that movement changes the trajectory of the boat.  

Here the independent variable is the rudder, while the dependent variable is the trajectory of the boat.


Independent and dependent variables are often referred to in other ways.  For instance, independent variables are sometimes called experimental variables or predictor variables.  Dependent variables are sometimes called outcome variables.

One way to differentiate between whether a variable is independent or dependent is to consider when each variable occurred.  Typically, the independent variable will be the variable that happened earlier. Meaning, if I am looking at a dataset that has a variable for the year someone was born and a variable for their level of happiness in 2019, it’s a good bet that the birth year is the independent variable because it happened before the current measure of happiness in 2019 (assuming we are not surveying newborn babies). In effect, this question would be measuring whether someone’s year of birth (maybe translated as generation affiliation) relates to how happy they are in 2019.

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