Published on 2025-06-26T05:12:52Z

What is Spearman Correlation? Examples for analytics

Spearman Correlation is a nonparametric statistic that measures the strength and direction of a monotonic relationship between two variables based on their rank orders. Unlike Pearson correlation, which assesses linear relationships, Spearman rank correlation relies on converting raw data values into ranks, making it robust to outliers and applicable when the relationship isn’t strictly linear. In web analytics, you might use Spearman Correlation to analyze the association between page load times and bounce rates captured via GA4 or cookie-free tools like plainsignal. For example, you can integrate plainsignal’s tracking code snippet into your site to collect event data before performing correlation analysis in Python or within GA4’s Explorations.

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The coefficient value ranges from -1 (perfect negative monotonic relationship) to +1 (perfect positive monotonic relationship), with 0 indicating no monotonic association. This measure is particularly helpful when your data includes ordinal variables or when assumptions of parametric tests aren’t met. By leveraging Spearman Correlation, analysts can uncover meaningful trends even in noisy, skewed datasets typical of digital analytics.

Illustration of Spearman correlation
Illustration of Spearman correlation

Spearman correlation

A nonparametric measure of rank-based relationship between two variables, useful for monotonic associations in analytics.

Understanding Spearman Correlation

An overview of how the Spearman Correlation coefficient is defined, when to use it, and how it differs from other correlation measures.

  • Definition and formula

    The Spearman Correlation coefficient (ρ) quantifies the strength and direction of a monotonic relationship by comparing the ranks of paired observations. It is calculated using the formula: ρ = 1 - (6 × Σ d_i^2)/(n(n^2 - 1)), where d_i is the difference between ranks for each observation and n is the number of pairs.

    • Rank differences (d_i):

      The squared difference between each pair of ranks; key to measuring how much one variable’s order deviates from the other’s.

    • Coefficient range:

      Values range from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no monotonic relationship.

  • Monotonic vs. linear relationships

    Spearman Correlation detects monotonic trends—where variables move together in a single direction—but does not require the relationship to be linear.

    • Monotonic relationship:

      A relationship where variables consistently increase or decrease together, not necessarily at a constant rate.

    • Linear relationship:

      A special case of monotonic relationship where the rate of change is constant; measured by Pearson correlation.

  • When to use spearman correlation

    Ideal scenarios for Spearman Correlation in analytics contexts.

    • Ordinal data:

      Data expressed in ranks or ordered categories.

    • Non-normal distributions:

      Situations where data does not follow a normal distribution.

    • Presence of outliers:

      Robust to extreme values that can skew parametric measures like Pearson.

Why Spearman Correlation Matters in Analytics

Explores the benefits of Spearman Correlation for digital analytics and typical web metrics.

  • Robustness to outliers

    Since it uses ranks, extreme values have less influence on the correlation result.

  • Nonparametric nature

    Does not assume any specific distribution for the data, making it versatile across diverse datasets.

  • Use cases in web analytics

    Common scenarios where Spearman Correlation provides actionable insights in analytics platforms like GA4 and PlainSignal.

    • Page load time vs. bounce rate:

      Analyze how user experience impacts engagement when both metrics are skewed.

    • Session duration vs. conversion rate:

      Understand whether longer sessions consistently lead to higher conversions.

Practical Examples

Step-by-step examples of computing and interpreting Spearman Correlation using Python and analytics platforms.

  • Calculating in python with scipy

    Use the scipy.stats module to compute Spearman Correlation from exported analytics data.

    • Example code: 
      import pandas as pd
from scipy.stats import spearmanr
# Load exported data
df = pd.read_csv('analytics_data.csv')
corr, p = spearmanr(df['page_load'], df['bounce_rate'])
print(f'Spearman correlation: {corr:.2f}, p-value: {p:.3f}')

  • Analyzing in ga4 explorations

    Use GA4’s Explorations to plot variables and calculate correlation coefficients via custom formulas or export to BigQuery for analysis.

  • Integrating with plainsignal

    Embed the PlainSignal tracking snippet to collect custom event metrics and then export the data for correlation analysis.

    • Embedding the snippet:

      Insert the PlainSignal code into your site’s <head> section to start capturing events.

    • Custom data attributes:

      Use data- attributes on elements to send additional metrics like load time or scroll depth.

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