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.
- Move the variables of interest to the Variables box.
- Select Pearson and/or Spearman.
- Test of Significance: Two-tailed.
- Check Flag significant correlations.
- 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.