Pearson and Spearman Correlation Analysis in SPSS: Step-by-Step

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When to Use Correlation Analysis

Correlation analysis measures the direction and strength of a linear relationship between two or more continuous variables. Common research questions: Is stress related to job performance? Do anxiety and depression scores co-vary? Correlation answers these questions.

Pearson or Spearman?

• Pearson r: Use when both variables are continuous and normally distributed.
• Spearman ρ (rho): Use for ordinal data, when normality is violated, or when outliers are present. It is the non-parametric alternative.

Running Correlation in SPSS

Go to Analyze → Correlate → Bivariate.

1. Move the variables of interest to the Variables box.
2. Select Pearson and/or Spearman.
3. Test of Significance: Two-tailed.
4. Check Flag significant correlations.
5. Click OK.

Interpreting the Correlation Coefficient

• |r| = 0.00–0.19: Very weak
• |r| = 0.20–0.39: Weak
• |r| = 0.40–0.59: Moderate
• |r| = 0.60–0.79: Strong
• |r| = 0.80–1.00: Very strong

A positive r means variables increase together; negative r means they move in opposite directions.

Correlation Matrix

When analyzing multiple variables simultaneously, SPSS produces a correlation matrix — a standard Table 1 in thesis results chapters, showing r and p-values for all variable pairs.

Important: Correlation ≠ Causation

A strong correlation does not imply that one variable causes the other. Causality requires experimental designs or longitudinal data with theoretical justification.

APA Reporting Example

Pearson correlation analysis revealed a significant negative relationship between work stress and job satisfaction, r(198)=-.52, p<.001, indicating that higher stress levels were associated with lower satisfaction.

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