Correlation Calculator

Pearson or Spearman?

Pearson’s r measures the strength of a straight-line relationship between two numeric variables. Spearman’s rho works on ranks, so it captures any consistently rising or falling relationship and is less affected by outliers. Use Spearman for ordinal data, skewed data or curved but steadily increasing patterns. For full models, see our correlation and regression analysis service.

Reading the coefficient

The coefficient ranges from −1 to +1. The sign gives the direction, and the size gives the strength. As a rough guide, values below 0.1 are negligible, 0.1 to 0.3 weak, 0.3 to 0.5 moderate, 0.5 to 0.7 strong and above 0.7 very strong. Squaring Pearson’s r gives R², the share of variation in one variable that is linearly explained by the other.

Before you trust the number

• Always look at the scatter plot. Very different patterns can give the same r.
• Check for outliers, which can create or hide a correlation.
• Correlation does not show that one variable causes the other.
• A significant p-value with a small sample, or a tiny r with a huge sample, both need careful interpretation.

Worked example

The sample data has ten pairs where Y rises steadily with X. Pearson r is 0.995 and Spearman rho is 1.0, both highly significant, and the scatter plot shows the points sitting almost exactly on the trend line.

Related analysis services

We offer correlation and regression analysis, with assumption checks, plus reliability and factor analysis and moderation and mediation analysis, in SPSS, STATA, R or Python. See all services or tell us about your project.