## Is Pearson correlation same as Spearman?

Pearson correlation: Pearson correlation evaluates the linear relationship between two continuous variables. Spearman correlation: Spearman correlation evaluates the monotonic relationship. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data.

**Should I use Pearson or Spearman?**

The difference between the Pearson correlation and the Spearman correlation is that the Pearson is most appropriate for measurements taken from an interval scale, while the Spearman is more appropriate for measurements taken from ordinal scales.

**Is Excel correl Pearson or Spearman?**

The correlation coefficients in Excel only measure linear (Pearson) or monotonic (Spearman) relationships.

### How do you interpret a Spearman correlation?

The Spearman correlation coefficient, rs, can take values from +1 to -1. A rs of +1 indicates a perfect association of ranks, a rs of zero indicates no association between ranks and a rs of -1 indicates a perfect negative association of ranks. The closer rs is to zero, the weaker the association between the ranks.

**What does Spearman correlation measure?**

Spearman’s correlation measures the strength and direction of monotonic association between two variables. Monotonicity is “less restrictive” than that of a linear relationship. For example, the middle image above shows a relationship that is monotonic, but not linear.

**Is there a relationship between the Spearman coefficient and the Pearson coefficient?**

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. This graph shows a very strong relationship. The Pearson coefficient and Spearman coefficient are both approximately 0.

#### Is it bad to use Spearman instead of Pearson?

No harm would be done by switching to Spearman even if the data turned out to be perfectly linear. But, if it’s not exactly linear and we use Pearson’s coefficient then we’ll miss out on the information that Spearman could capture.

**What is the assumption of the Spearman-Brown formula?**

The assumption of the Spearman-Brown formula is that split-halves are parallel, which means that the variances of the split-halves are equal. The systematic name proposed for the Spearman-Brown formula is split-half parallel reliability.

**When to use Spearman instead of Pearson in scatterplot?**

Now, if we feel that a scatterplot is visually indicating a “might be monotonic, might be linear” relationship, our best bet would be to apply Spearman and not Pearson. No harm would be done by switching to Spearman even if the data turned out to be perfectly linear.