Spearman Rank Correlation Calculator

Spearman’s rank-order correlation (ρ or rₛ) measures the strength and direction of a monotonic relationship between two variables. It is the right choice for ordinal data such as single Likert items, for skewed data, or when there are outliers. Paste two columns to get ρ, its p-value and 95% confidence interval, a scatter plot and an APA 7 sentence.

How to use this calculator

  1. Put variable X in one column and Y in the next; each row is one participant.
  2. Paste both columns (headers optional).
  3. Click Calculate.
  4. Report ρ (rₛ), the degrees of freedom and p.

Formula

Convert X and Y to ranks separately (average ranks for ties)
ρ = Pearson correlation of the ranks
(without ties: ρ = 1 − 6Σd² / (n(n² − 1)))
t = ρ√(n − 2) / √(1 − ρ²),  df = n − 2

Step-by-step example

  1. Ten students rated motivation and satisfaction on 1–5 scales.
  2. Both variables are converted to ranks.
  3. ρ = 0.799, p = .006.
  4. A strong positive monotonic relationship.

How to interpret the results

ρ ranges from −1 to +1. Values near ±1 indicate a strong monotonic relationship, and values near 0 indicate little or none. Cohen’s (1988) benchmarks for r (.10 small, .30 medium, .50 large) are commonly applied to ρ as well. A significant p-value means the correlation is unlikely to be zero in the population.

How to report it in APA 7 style

A Spearman’s rank-order correlation showed a strong, positive and statistically significant relationship between motivation and satisfaction, rₛ(8) = .80, p = .006.

Statistical symbols (t, F, p, r, M, SD, N, d) should be italicised in your thesis.

Assumptions

  • Both variables are at least ordinal.
  • The relationship is monotonic (consistently increasing or decreasing).
  • Observations are independent.

How to do it in SPSS, R or Excel

SPSS: Analyze → Correlate → Bivariate → tick Spearman. R: cor.test(x, y, method = 'spearman', exact = FALSE). Python: scipy.stats.spearmanr.

Roman Urdu mein khulasa

Spearman correlation do variables ka rishta ranks ki buniyad par napta hai. Ye Likert items, ordinal data ya non-normal data ke liye Pearson se behtar hai. ρ +1 ke qareeb ho to strong positive rishta hai, −1 ke qareeb ho to strong negative. p 0.05 se kam ho to correlation significant hai.

Frequently asked questions

When should I use Spearman instead of Pearson?

Use Spearman for ordinal variables (such as single Likert items), for clearly non-normal data, when there are outliers, or when the relationship is monotonic but not linear.

How are ties handled?

Tied values receive average ranks, and ρ is calculated as the Pearson correlation of the ranks, the same method SPSS and scipy use.

How is the p-value calculated?

The p-value uses the t approximation with n − 2 degrees of freedom, which matches SPSS for typical sample sizes.

What is a strong Spearman correlation?

Values of about .10, .30 and .50 are commonly read as small, medium and large.

Can I use Spearman for Likert scale totals?

Yes. Spearman works for totals too, though Pearson is also common for multi-item scale scores.

How do I report Spearman’s correlation in APA?

Write rₛ(df) = value, p = value, for example: rₛ(8) = .80, p = .006.

Related calculators

Cite this calculator

If ScholarCalc helped your analysis, you can cite it in APA 7 format:

ScholarCalc. (2026). Spearman Rank Correlation Calculator [Web application]. https://scholarcalc.com/spearman-correlation-calculator/

Written and maintained by the ScholarCalc team. Last reviewed: November 2026.

Accuracy: results are computed in your browser and were validated against Python scipy, statsmodels and pingouin on test datasets. See Methodology and accuracy.