Pearson and Spearman Correlation – Interpretation of Results
Correlation analysis is a fundamental statistical method used to examine relationships between two variables. The most commonly used coefficients are the Pearson correlation coefficient (r) and the Spearman rank correlation coefficient (ρ).
Both coefficients range from -1 to +1, where the sign indicates direction and the magnitude indicates the strength of the relationship (Mukaka, 2012).
Difference Between Pearson and Spearman Correlation
Pearson Correlation (r)
Used when:
- variables are continuous
- data are normally distributed
- the relationship is linear
Measures the linear relationship between variables (Schober et al., 2018).
Spearman Correlation (ρ)
Used when:
- data are not normally distributed
- variables are ordinal
- outliers are present
Measures a monotonic relationship based on ranks (Mukaka, 2012).
Interpretation of Correlation Values
Two key aspects:
- direction (positive/negative)
- strength
Reference Interpretation Table
The following figure shows standard interpretation:
General Interpretation Rules
- r = 0 → no relationship
- r → +1 → strong positive relationship
- r → -1 → strong negative relationship
Examples:
- r = 0.65 → moderate to strong positive
- r = -0.30 → weak negative
Important: correlation does not imply causation (Schober et al., 2018).
Statistical Significance
- p < 0.05 → significant
- p < 0.001 → highly significant
- p > 0.05 → not significant
Writing Results
A Spearman correlation analysis was conducted to examine the relationship between variables X and Y. The results show a significant positive correlation (ρ = 0.45, p < 0.001), indicating a moderate relationship.
As part of statistical consulting, I prepare structured analytical outputs, propose methodologically sound interpretations, and support clear scientific presentation, while the author remains responsible for final conclusions.
When to Use Which
- Pearson → continuous, normal data
- Spearman → ordinal or non-normal data
Common Mistakes
- wrong coefficient choice
- interpreting correlation as causation
- ignoring p-values
- neglecting effect size
Conclusion
Pearson and Spearman correlations are essential tools for analyzing relationships and require proper interpretation.
How to Cite (APA)
Miljko, L. (2025). Pearson and Spearman Correlation – Interpretation of Results. Statistische Beratung.
Retrieved from: https://statistischeberatung.de
References
Mukaka, M. M. (2012). A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69–71.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768.
Statistische Beratung Leonardo Miljko. (2025). How to interpret Pearson’s and Spearman’s correlation coefficients.
Retrieved from https://statistischedatenanalyse.de/index.php/de/04-forschungsmethoden-de/04-05-de-wie-man-die-korrelationskoeffizientenwerte-von-pearson-und-spearman-interpretiert
Keywords
Pearson correlation, Spearman correlation, correlation analysis, SPSS correlation, statistical interpretation
Meta Text
Learn how to interpret Pearson and Spearman correlation coefficients based on scientific guidelines. Includes APA references and examples.
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