A correlation between variables indicates that as one variable changes in value, the other variable tends to change in a specific direction. A correlation coefficient measures both the direction and the strength of this tendency to vary together.

- A positive correlation indicates that as one variable increases the other variable tends to increase.
- A correlation near zero indicates that as one variable increases, there is no tendency in the other variable to either increase or decrease.
- A negative correlation indicates that as one variable increases the other variable tends to decrease.

The correlation coefficient can range from -1 to 1. The extreme values of -1 and 1 indicate a perfectly linear relationship where a change in one variable is accompanied by a perfectly consistent change in the other. In practice, you wonâ€™t see either type of perfect relationship.

The two most common types of correlation coefficients are Pearsonâ€™s product moment correlation and the Spearman rank-order correlation.

### Pearson product moment correlation

The Pearson correlation evaluates the linear relationship between two continuous variables. A relationship is linear when a change in one variable is associated with a proportional change in the other variable.

### Spearman rank-order correlation

Also called Spearman’s rho, the Spearman correlation evaluates the monotonic relationship between two continuous or ordinal variables. In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data.