When to Use Pearson vs. Spearman Correlation

May 2026·6 min read

When to use Pearson vs. Spearman correlation is one of the most common method questions in thesis research. Both tests measure the relationship between two variables, but they apply under different conditions. Pearson requires metric data and a linear relationship; Spearman is appropriate when those conditions are not met. This guide walks you through the decision, the checks you need to run, and the APA reporting format for both.

What Is Correlation? Quick Refresher

A correlation coefficient measures the strength and direction of the relationship between two variables. It ranges from −1 (perfect negative relationship) to +1 (perfect positive relationship). A value near 0 means little or no linear relationship.

Correlation tells you how strongly two variables move together — it does not tell you that one causes the other, and it does not allow you to make predictions (use regression for that).

The two most common correlation coefficients in thesis research are Pearson’s r (parametric) and Spearman’s ρ (non-parametric).

Pearson vs. Spearman: The Key Difference

Pearson’s r measures the linear relationship between two metric variables. It uses the actual values of both variables.

Spearman’s ρ (rho) measures the monotonic relationship between two variables by converting the raw values into ranks. It does not assume normality or linearity — it only requires that the relationship is consistently increasing or decreasing.

Conditions for Pearson Correlation: 3 Things to Check

Before using Pearson correlation, confirm three conditions:

1. Both variables are metric: both your IV and DV must be measured on an interval or ratio scale. A single Likert item is ordinal — it does not qualify. A computed composite score from multiple items can be treated as metric.

2. Both variables are approximately normally distributed: check with Shapiro-Wilk (Analyze → Descriptive Statistics → Explore). With large samples (n > 100), rely on the histogram and Q-Q plot.

3. The relationship is linear: inspect a scatter plot. Pearson r captures linear association only — a curved or U-shaped relationship would yield a near-zero r even if the variables are strongly related.

[TIP] Always look at the scatter plot before running any correlation. A scatter plot showing a curved relationship means Pearson is not the right tool, regardless of the normality check.

When to Use Spearman Instead: 3 Scenarios

Switch to Spearman correlation when:

Scenario 1 — Ordinal data: at least one variable is ordinal (e.g. a single Likert item, a ranking, or a rating scale with few levels).

Scenario 2 — Normality violated: Shapiro-Wilk is significant (p < .05) for at least one variable and your sample is small (n < 30). With n ≥ 30, Pearson is robust to mild normality violations.

Scenario 3 — Non-linear monotonic relationship: the scatter plot shows a consistently increasing or decreasing curve that is not a straight line. Spearman captures monotonic trends; Pearson does not.

How to Run Both Correlations in SPSS

SPSS path: Analyze → Correlate → Bivariate → move both variables to Variables box → tick Pearson and/or Spearman → tick Flag significant correlations → OK.

• In the output, read:
• Correlation coefficient (r or ρ)
• Sig. (2-tailed) = your p-value
• N = sample size

The output matrix is symmetric: the coefficient appears twice (upper and lower triangle). Read either one.

Real Example: Stress and Sleep Quality in a Student Survey

Research question: Is there a relationship between perceived stress score (metric composite) and sleep quality (single Likert item, ordinal)?

Decision: sleep quality is a single ordinal item → use Spearman.

SPSS output: ρ = −.41, p < .001, N = 121.

APA reporting: "A significant negative correlation was found between perceived stress and sleep quality, ρ(119) = −.41, p < .001. Higher stress was associated with lower sleep quality."

APA Reporting Format for Pearson and Spearman

• Pearson — significant:
• "A significant positive correlation was found between study hours and exam grade, r(119) = .48, p < .001."
• Pearson — non-significant:
• "No significant correlation was found between social media use and study hours, r(119) = −.08, p = .374."
• Spearman — significant:
• "A significant negative correlation was found between perceived stress and sleep quality, ρ(119) = −.41, p < .001."

Note: degrees of freedom = N − 2. For N = 121: df = 119.

[INFO] Effect size benchmarks (absolute values): .10–.29 small, .30–.49 medium, .50+ large. These benchmarks apply to both r and ρ.