Dependent and Independent Variables

Basic Concepts

Every experiment involves two types of variables; these include the dependent and independent variables.
The independent variable (IV) is the variable that you, the researcher, control and change to test its effects on the dependent variable.
The dependent variable (DV) is the outcome being measured. It is believed to depend on, or be caused by, the independent variable.
In a mathematical model, the dependent variable is denoted by ‘y’ and the independent variable is denoted by ‘x’.

The relationship between these variables is captured in the equation of the fitting line in a scatter plot.
This fitting line, often a straight line in simple cases, is given by y = mx + c, where ‘m’ is the gradient, and ‘c’ is the y-intercept.
The correlation between the independent variable and the dependent variable shows the strength and direction of the relationship between them.
The correlation coefficient (Pearson’s r) measures the linear relationship between the two variables. The closer this coefficient is to -1 or 1, the stronger the linear relationship.

Being clear about which variable is the independent variable and which is the dependent variable is essential in regression analysis.
A common error in correlation and regression analysis is swapping of the independent and dependent variables. It’s crucial to identify these correctly before performing any calculations.

The specific way in which the independent variable affects the dependent variable is measured by the coefficient of the independent variable in the regression analysis.
When calculating the correlation coefficient, the selection of dependent and independent variables can affect the results.
It’s essential to keep in mind that correlation is not causation. Although a strong correlation can be observed, it does not prove that changes in the independent variable cause changes in the dependent variable.

Dependent and independent variables play a key role when modelling real-life scenarios.
They help us to answer questions such as: “Is there a relationship?”, “How strong is that relationship?”, and “What can we predict?” Bearing in mind the dependent and independent variables can help to deduce conclusions from data analysis.