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Spearman Correlation

Spearman Correlation measures the strength of a monotonic relationship between variables using ranks rather than raw values. Use Spearman when your data is ordinal, non-normal, or has a non-linear but consistently increasing or decreasing pattern.

Goal

Measure rank-based correlations between hours studied, test scores, and class rank for a group of students.

Sample Data

Download CorrelationAndCovariance_Spearman.xlsx

Excel Protected View

When you open downloaded files, Excel displays a Protected View warning. You must click Enable Editing before you can use Quantum XL with the file.

Alternatively, you can copy the sample data from the table below and paste it directly into a new Excel workbook.

Hours Studied	Test Score	Class Rank
2	62	25
4	68	19
6	72	14
8	81	11
10	85	7
3	71	16
5	70	18
7	79	8
9	83	9
11	89	4

Each row represents one student. Hours Studied and Test Score should have a strong positive monotonic relationship. Class Rank is inversely related — a lower rank number means a better position — so expect a strong negative correlation with Hours Studied and Test Score.

Steps

1. Launch the analysis

   From the Excel ribbon, select QXL Stat Tools → Analysis Tools → Correlation and Covariance → Spearman Correlation.

2. Select your data

   Select cells A1:C11 (the header row plus all 10 data rows across all three columns).

3. Configure the analysis

   In the Correlation dialog:

   • Data Columns: "Hours Studied", "Test Score", and "Class Rank" should all be checked

   Click Finish to generate the correlation matrix.

Result

Quantum XL creates a 3×3 Spearman correlation matrix with p-values for each pair:

• Hours Studied vs. Test Score — Strong positive correlation (close to 1). Students who study more tend to score higher.
• Hours Studied vs. Class Rank — Strong negative correlation (close to -1). More study hours are associated with better (lower) class rank numbers.
• Test Score vs. Class Rank — Strong negative correlation (close to -1). Higher test scores correspond to better class rank.

All three p-values should be very small and highlighted in red, indicating statistical significance.

Spearman vs. Pearson

Spearman ranks each value before computing correlations, which makes it robust to outliers and suitable for monotonic but non-linear relationships. Pearson, in contrast, measures only linear relationships using raw values.