In statistics, the most often used word is ‘variable’ which refers to a characteristic that contains the value, which may vary from one entity to another. It is similar to the variables used in other disciplines like science and mathematics. The two most common types of variable are the dependent variable and independent variable. A variable is said to be independent, whose change influence another variable, while if the variable is dependent, it will change in response to the change in some other variable.
The dependence of the former on the latter is being examined by the statistical models. So, here in this article, we are going to discuss some important points of difference between independent and dependent variable.
Content: Independent Variable Vs Dependent Variable
|Basis for Comparison
|Independent Variable is one whose values are deliberately changed by the researcher in order to obtain a desired outcome.
|Dependent Variable refers to a variable which changes its values in order to reciprocate change in the values of independent variable.
|What is it?
|Manipulated by the researcher.
|Measured by the researcher.
|Usually denoted by
Definition of Independent Variable
As its name suggests, an independent variable is one which remains unaffected by other variables. Alternately known as the predictor variable, explanatory variable, controlled variable. It is a variable; the researcher has control over its selection and manipulation, i.e. the levels can be changed, or it changes on its own due to circumstances. Moreover, its effect on other variables is measured and compared.
Definition of Dependent Variable
A dependent variable is a consequence of an independent variable i.e. it is variable that measures the effect of the independent variable on the test units. It is also known as the criterion or measured variable.
It is something that the experimenter observes during an experiment and is influenced by the experiment. It is expected to change in response to some other factors. The revised value of the dependent value depends on the independent variable.
Key Differences Between Independent and Dependent Variable
The significant differences between independent and dependent variable are explained in the following points:
- The variable whose values are deliberately changed by the researcher in order to obtain the desired outcome is called an independent variable. The variable, which changes its values in order to reciprocate change in the values of the independent variable is called the dependent variable.
- The values of the independent variable can be changed as per requirement, by the researcher. Conversely, the value of the independent variables is unchangeable.
- Manipulation can be done in the values of the independent variable, but the researcher observes the value of a dependent variable during an experiment.
- The independent variable is known as the experiment controller in an experiment, whereas, the dependent variable is also known as experiment measure.
- An independent variable is a presumed cause whereas the dependent variable is a measured effect.
- In simple linear regression, ‘y’ denotes dependent variable while ‘x’ denotes independent variable, which means y depends on x.
Video: Independent Vs Dependent Variable
Suppose you want to check the height of a normal at different ages. So here the dependent variable is height, while the independent variable is the age, which is going to change on its own. Hence, here the height of the boy is shown on the y-axis, whereas x-axis indicates the age.
There can be multiple dependent variables for one independent variable. In a scientific experiment, the independent variables are controlled or changed whereas the dependent variables tend to be measured and tested. An independent variable is the one that does not rely on anything else and hence can be manipulated, while the dependent shows the effect, of changes made to the independent variable.