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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. For example, you might use a Pearson correlation to evaluate whether increases in temperature at your production facility are associated with decreasing thickness of your chocolate coating.

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.

Spearman correlation is often used to evaluate relationships involving ordinal variables. For example, you might use a Spearman correlation to evaluate whether the order in which employees complete a test exercise is related to the number of months they have been employed.

It is always a good idea to examine the relationship between variables with a scatterplot. Correlation coefficients only measure linear Pearson or monotonic Spearman relationships. Other relationships are possible. However, the real value of correlation values is in quantifying less than perfect relationships. Finding that two variables are correlated often informs a regression analysis which tries to describe this type of relationship more. This graph shows a very strong relationship. The Pearson coefficient and Spearman coefficient are both approximately 0.

A comparison of the Pearson and Spearman correlation methods Learn more about Minitab. In This Topic What is correlation? Comparison of Pearson and Spearman coefficients Other nonlinear relationships. A correlation coefficient measures the extent to which two variables tend to change together. The coefficient describes both the strength and the direction of the relationship.

Minitab offers two different correlation analyses: Pearson product moment correlation The Pearson correlation evaluates the linear relationship between two continuous variables.

Spearman rank-order correlation The Spearman correlation evaluates the monotonic relationship between two continuous or ordinal variables.

This relationship forms a perfect line. When a relationship is random or non-existent, then both correlation coefficients are nearly zero. Other nonlinear relationships Pearson correlation coefficients measure only linear relationships.

Spearman correlation coefficients measure only monotonic relationships. So a meaningful relationship can exist even if the correlation coefficients are 0. Examine a scatterplot to determine the form of the relationship. Coefficient of 0 This graph shows a very strong relationship. By using this site you agree to the use of cookies for analytics and personalized content.