Spearman's Rank Correlation Assignment Help — Ordinal Data in SPSS

What Is Spearman’s Rank Correlation and When Do You Use It?

Spearman’s rank correlation (Spearman’s rho, ρ) measures the strength and direction of a monotonic relationship between two variables, based on their ranks rather than their raw values. Use it when at least one variable is ordinal, or when your data is continuous but clearly violates Pearson correlation’s assumptions (non-linearity, non-normality, or influential outliers).

If both variables are continuous, roughly normally distributed, and linearly related, use Pearson correlation instead. It uses more information from the raw data and is the more familiar reporting standard.

Assumptions You Must Check Before Running It in SPSS

• Both variables should be at least ordinal.
• The relationship should be monotonic (consistently increasing or decreasing, though not necessarily in a straight line). Check with a scatterplot before running the test.
• No normality or linearity assumption in the strict Pearson sense: that’s the reason to use rho instead.

How to Run It in SPSS (Step by Step)

1. Go to Analyze > Correlate > Bivariate.
2. Move your two variables into the Variables box.
3. Under Correlation Coefficients, check Spearman (and uncheck Pearson if you don’t also want it).
4. Click OK.

How to Interpret the Output

1. In the correlation matrix, read the Spearman’s rho coefficient: ranges from −1 to +1, same direction/strength logic as Pearson’s r.
2. Check the Sig. (2-tailed) value for statistical significance.
3. Apply the same strength benchmarks used for Pearson’s r: around .10 small, .30 medium, .50 large (Cohen’s conventions, commonly applied to rho as well).

How to Report the Results in APA Format

There was a moderate, statistically significant positive monotonic relationship between study hours and exam performance, ρ(48) = .38, p = .006.

Spearman’s Rho vs Pearson Correlation: Understanding the Difference

Pearson correlation measures the strength of a linear relationship between two continuous, normally distributed variables, using their raw values. Spearman’s rho measures the strength of a monotonic relationship using ranked values, making no distributional assumption. Running Pearson on ordinal data, or on continuous data with a strong non-linear (but still monotonic) pattern, understates the true relationship. Spearman’s rho is the test built for exactly that situation.

Related Non-Parametric Tests

Spearman’s rho isn’t the only non-parametric option. These related tests cover group comparisons instead of a relationship between two variables:

• Mann-Whitney U Test: comparing two independent groups on a non-normal or ordinal outcome
• Kruskal-Wallis H Test: comparing three or more independent groups
• Wilcoxon Signed-Rank Test: comparing two related measurements from the same subjects
• McNemar’s Test: for paired binary or categorical data

Not sure which one your data needs? See the full SPSS statistical test guide, or get help with this specific assignment.