Spearman's Correlation Calculator
Calculate Spearman's rank correlation coefficient to measure monotonic relationships between variables
Data Input
| Pair | X Value | Y Value | Action |
|---|---|---|---|
| 1 | |||
| 2 | |||
| 3 |
Enter at least 3 data pairs to calculate Spearman's correlation. Maximum 30 pairs allowed.
Correlation Strength Guide
Formula Reference
General Formula
When No Ties
where d = rank difference
Quick Tips
Measures monotonic relationships, not just linear
Works with ordinal and continuous data
Less sensitive to outliers than Pearson
Values range from -1 to +1
Understanding Spearman's Rank Correlation
What is Spearman's Correlation?
Spearman's rank correlation coefficient (ρ) measures the strength and direction of monotonic relationships between two variables. Unlike Pearson's correlation, it works with ranked data and can detect non-linear monotonic patterns.
When to Use It?
- •Data doesn't meet normality assumptions
- •Ordinal or ranked data
- •Non-linear but monotonic relationships
- •Presence of outliers
Spearman vs Pearson
Spearman's Correlation
- • Measures monotonic relationships
- • Uses ranked values
- • Works with ordinal data
- • Less sensitive to outliers
Pearson's Correlation
- • Measures linear relationships
- • Uses raw values
- • Requires continuous data
- • More sensitive to outliers
Step-by-Step Calculation
1. Rank the Data
Assign ranks to each variable separately (lowest = rank 1)
2. Handle Ties
Assign average rank to tied values
3. Calculate Correlation
Apply Pearson's formula to the ranked data
4. Interpret Result
Assess strength and direction using standard guidelines