It is crucial to understand the difference between dependent and independent variables when it comes to statistical analysis. Although both metrics are only characteristics of a particular distribution of a single variable (“Age” or “Height”), an idea of what exactly is an independent variable for a test makes sense. The independent variable is the variable whose change throughout the test should affect the dependent variable.
In other words, it is the manipulated variable by which the researcher observes the change in the dependent variable. From this point of view, the dependent variable is the variable that must be subjected to changes during testing, and these changes are the products of the influence of the dependent variable.
In this sense, it is essential to emphasize that the dependent variable must change and not be constant because, in this case, a violation of the logic of the definition is achieved. A variable is any variable that changes during testing, which means that constant values must be excluded. There are similarities and differences between independent and dependent variables in the analysis.
First, the independent variable is controllable, whereas the dependent variable is observable. Second, the value of the independent variable is uninfluenced by other variables, which means that it is a kind of isolated instrument for measuring the dependent variable. Third, the dependent variable is always a cause, whereas the independent variable cannot be a cause by definition.
However, it is essential to recognize that both the dependent and independent variables can be of any type: quantitative or categorical. In addition, these are highly contingent concepts that can be easily swapped. Thus, an independent variable can become a dependent variable, and vice versa.