A Spearman's 'rho' Correlation compares the relationships between two variables, used for Ordinal level data or higher.
It is a non-parametric test and therefore suitable for non-parametric data. If either of your two variables are not parametric, you would use this test over the Pearsons. If both of your variable are parametric you could consider Pearson's 'r' Correlation.
To check if your data is parametric, please check out the dedicated guide: Parametric or Not Guide (PDF)
Click Analyze > Correlate > Bivariate
Within the 'Bivariate Correlations' window, select the two variables you intend to analyse and move them to the 'Variables' box using the blue arrow.
Unselect Pearson and select Spearman as the Correlation Coefficient.
Click OK.
SPSS will generate one Correlations table:
This table shows the three statistics we need to report when running a Spearman's 'rho' correlation, the Correlation Coefficient (Correlation), the significance/ p-value (Sig (2-tailed)), and the sample size (N)
Alongside interpreting the p value for significance, you will also need to interpret the strength and direction of the relationship. To do this you would use a correlation strength table. We have an example of this in this PDF: Correlation Strength Table (also linked below), however we recommend using a table from a text book (check either your reading list or the book recommendations we have on our site).
The Spearman's Rho statistic can be written as Rho, rs or the Greek symbol for Rho which is ρ. We recommend you follow the choice of your lecturers or a key text within your field.
The relationship between the length of a porcupine and its number of spines was examined. A Spearman's 'Rho' Correlation indicated that there was a significant and strong positive relationship, rs = .69, p < .001, N = 31.
Following APA formatting guidelines statistical symbols should be italicised, except for Greek symbols: Formatting Guide Page
