Pearson's Correlation Statistic is used to evaluate the strength and direction of the linear relationship between two variables.
Correlation means that as one variable increases, another variable tends to either increase or decrease.
Continuing with the weight loss example in my previous tutorials, suppose that the dependent variable is weight loss but this time the independent variable is measured on a continuous measurement scale, such as the level of adherence to the diet. Imagine there was a valid and reliable instrument to quantify the level of adherence to the diet and that the adherence score was measured on a continuous measurement scale, such as a number between 0 (complete lack of adherence) to 100 (perfect adherence).
You might expect to see that better adherence to the diet plan tends to be correlated with greater weight loss and worse adherence to the diet tends to be correlated with less weight loss.
The mathematical calculations are performed by software like SPSS. The software will produce a p-value (probability value). By convention, if p is < 0.05, the sample results provide strong evidence that there is a statistically significant correlation between adherence to the diet and amount of weight lost.
If the correlation is statistically significant, the Pearson's correlation statistic will tell you if the correlation is positive (as adherence increases, weight loss increases) or negative (as adherence increases, weight loss decreases. The Pearson's correlation statistic will also tell you how strongly the two variables are correlated with each other. Values near 0 indicate a weak correlation and values near -1 or +1 indicate a stronger correlation