Correlation studies are used by researchers to describe and predict the direction and strength of relations among variables.


A correlational study is predictive and descriptive research that shows the relationship between variables. If someone finds a correlation between variable X and variable Y, then he/she knows that a change in variable X would make it more likely for a change in variable Y to occur.

Correlation studies often represent their data through scatter plots that represent the correlation coefficient. Сorrelation coefficient is a statistical index of the dependence between two random variables. The relationship may be expressed through a positive or negative correlation.

The first one means that an increase in the value of one variable entails an increase in the value of another variable in the similar direction. A negative correlation means that a decrease in the value of one variable causes an increase in the value of another one. When a change in the value of the first variable has no impact on the value of the second variable, it indicates the absence of correlation.

The strength of the relationship between variables indicates a degree of dependency. The correlation may range from -1 to +1, where ‘-1’ is a negative correlation, ‘0’ indicates the absence of any relationship between variables, and ‘+1’ indicates a positive correlation.

Correlation studies are used in various spheres, and their popularity is explained by the fact that correlation coefficients are relatively simple to calculate. Correlation studies are conducted to:

  1. determine whether there is a relationship between variables;
  2. predict whether a change in one parameter will entail when the value of another parameter is known;
  3. to select a set of independent features for classification.