When your research involves measuring the relationship between two variables, you need a correlation test. Most students know about Pearson r, but your data may require Spearman rho instead. Choosing the wrong one is a common mistake that supervisors notice immediately.
What is the difference?
Pearson r measures the linear relationship between two continuous, normally distributed variables. It assumes both variables are measured on an interval or ratio scale and that the relationship between them is linear.
Spearman rho is a rank-based correlation. It makes no assumptions about the distribution of your data and works with ordinal data (like Likert scales) or continuous data that violates normality. It measures monotonic relationships — whether the variables tend to move in the same direction, even if not in a straight line.
Decision guide: which one to use?
Use Pearson r when:
- Both variables are continuous (height, weight, test scores, revenue)
- Both variables are normally distributed (check with Shapiro-Wilk)
- You are measuring a linear relationship
- Your sample size is reasonably large (n > 30)
Use Spearman rho when:
- Your data is on an ordinal scale (Likert responses: 1, 2, 3, 4, 5)
- Your data violates normality (Shapiro-Wilk p < .05)
- You have outliers that could distort a Pearson correlation
- You are measuring a monotonic but not necessarily linear relationship
How to report each in APA 7th Edition
Pearson r:
r(48) = .62, p < .001, 95% CI [.41, .77]
The number in parentheses is the degrees of freedom (n minus 2). APA 7th Edition requires the 95% confidence interval for Pearson r.
Spearman rho:
rho(48) = .58, p < .001
No confidence interval is required for Spearman rho in standard APA reporting.
The assumption testing step
Before choosing, always test normality. Run Shapiro-Wilk on both variables. If either variable violates normality (p < .05), use Spearman rho.
ResearchScope (bizscope.space/research) handles this automatically. It runs normality tests first, then selects Pearson or Spearman based on the results, and tells you which was used and why. The correlation matrix heatmap lets you toggle between methods.
Significance stars
Both methods use the same significance notation:
- *** p < .001
- ** p < .01
- * p < .05
- ns not significant
Include the stars in your results table but use the full p-value in your text.