How to Use Spearman’s Rho Correlation in SPSS: Complete 2025 Guide
What is Spearman’s Rho Correlation?
Spearman’s Rho (ρ) is a non-parametric correlation coefficient that measures the monotonic relationship (whether linear or non-linear) between two variables. It produces values between -1 and +1:
- +1: Perfect positive monotonic relationship (as one variable increases, the other always increases)
- 0: No monotonic relationship
- -1: Perfect negative monotonic relationship (as one variable increases, the other always decreases)
Where:
- \(d_i\): Difference between ranks of each pair
- \(n\): Number of observations
SPSS automatically performs these calculations when you use the Bivariate Correlation procedure.
When to Use Spearman’s Rho?
| Situation | Description | Example |
|---|---|---|
| Ordinal Data | When variables are ordinal (rankings, Likert scales) | Customer satisfaction ratings vs product quality scores |
| Non-Normal Data | When continuous data doesn’t follow normal distribution | Income level vs education ranking |
| Monotonic Relationship | When relationship is consistent in direction but not necessarily linear | Age vs physical performance metrics |
When Not to Use Spearman’s Rho?
| Situation | Problem | Alternative |
|---|---|---|
| Normal Continuous Data | Less powerful than Pearson for normal data | Pearson correlation |
| Binary Data | Not designed for binary variables | Point-Biserial or Phi Coefficient |
| Very Small Samples (n < 5) | Results become unreliable | Descriptive analysis only |
Step-by-Step Guide for SPSS (2025 Version)
Example Dataset: Study Hours vs Exam Ranks
We’ll analyze the relationship between study hours (continuous) and exam ranks (ordinal) for 10 students:
| Student ID | Study Hours | Exam Rank |
|---|---|---|
| 1 | 5 | 3 |
| 2 | 3 | 7 |
| 3 | 6 | 1 |
| 4 | 4 | 5 |
| 5 | 2 | 9 |
| 6 | 7 | 2 |
| 7 | 1 | 10 |
| 8 | 4.5 | 4 |
| 9 | 3.5 | 6 |
| 10 | 2.5 | 8 |
Note: Exam Rank 1 = highest performance, 10 = lowest performance
Step 1: Enter Data in SPSS
- Open SPSS and switch to Data View
- Create two variables:
- Study_Hours (Scale/Continuous)
- Exam_Rank (Ordinal)
- Enter the data exactly as shown in the table above
Step 2: Run Spearman’s Correlation
- Go to Analyze → Correlate → Bivariate
- Move both variables to the Variables box
- Select Spearman (deselect Pearson if selected)
- Choose Two-tailed test of significance
- Check Flag significant correlations
- Click OK
Step 3: Interpret the Output
SPSS will generate this correlation table:
| Study_Hours | Exam_Rank | |
|---|---|---|
| Study_Hours | 1.000 | -0.842** |
| Exam_Rank | -0.842** | 1.000 |
| ** Correlation is significant at the 0.01 level (2-tailed). | ||
Key Findings:
- ρ = -0.842: Strong negative correlation (more study hours → better exam ranks)
- p < 0.01: Statistically significant at 99% confidence level
- Effect Size: |ρ| > 0.5 indicates strong relationship
Checking Assumptions
| Assumption | How to Verify | Our Example |
|---|---|---|
| Ordinal/Continuous Data | Check variable types in Variable View | ✓ Met (Study_Hours: Scale, Exam_Rank: Ordinal) |
| Monotonic Relationship | Create scatterplot (Graphs → Chart Builder) | ✓ Clear negative trend visible |
| Independent Observations | Research design consideration | ✓ Each student independent |
2025 Best Practices
- Data Preparation:
- Clean missing values (Analyze → Missing Value Analysis)
- Check for outliers with boxplots
- Enhanced Visualization:
- Use SPSS’s new 2025 interactive charts
- Add confidence intervals to scatterplots
- Reporting:
- Always report both ρ and p-values
- Include effect size interpretation
- Use APA 7th edition formatting
Frequently Asked Questions (2025)
| Question | Answer |
|---|---|
| Can I use Spearman’s for Likert scale data? | Yes, it’s ideal for ordinal data like Likert scales |
| How does SPSS 2025 handle ties in ranks? | Automatically uses average rank method |
| What’s the minimum sample size? | Technically n ≥ 4, but n ≥ 20 recommended |
| How to report in APA format? | “Spearman’s ρ = -.84, p < .01, two-tailed" |
Conclusion
This guide has demonstrated how to perform and interpret Spearman’s Rho correlation in SPSS 2025. Our analysis revealed a strong, statistically significant negative relationship (ρ = -0.842, p < 0.01) between study hours and exam ranks, indicating that students who studied more tended to achieve better exam rankings.
For further learning, explore SPSS’s new 2025 features for non-parametric analysis and consider taking advanced courses in ordinal data analysis techniques.