HOW TO RUN
SPEARMAN'S RANK CORRELATION
TEST IN SPSS

Using Spearman’s Correlation Statistic in Research

This easy tutorial will show you how to run Spearman’s Correlation test in SPSS, and how to interpret the result.

The Spearman correlation coefficient is the non-parametric equivalent of the Pearson correlation coefficient. It similarly takes values between -1 and +1, but the difference is that it quantifies the extent to which the variables tend to increase or decrease together i.e., the extent to which one variable tends to increase as the other increases or decreases. A value of zero indicates no such tendency. (Source)

We can categorize the type of correlation by considering as one variable increases what happens to the other variable:

• Positive correlation – the other variable has a tendency to also increase;
• Negative correlation – the other variable has a tendency to decrease;
• No correlation – the other variable does not tend to either increase or decrease.

The starting point of any such analysis should be the construction and subsequent examination of a scatterplot. Examples of negative, no, and positive correlation are as follows.

Are there guidelines for interpreting Spearman’s correlation coefficient?

Yes, We proposed the following guidelines: A Spearman’s correlation coefficient between 0.51 and 0.99 indicates a high correlation between variables (values above 0.80 indicate an extremely high correlation. )

Assumptions of the Spearman’s Correlation Test

The assumptions for Spearman’s correlation coefficient are as follows:

Above all, Correlation describes the strength and direction of a relationship between two variables. In addition, Spearman’s rank correlation coefficient is appropriate for ordinal variables, and it is especially used when data do not meet the criteria for the Pearson correlation. As a result, It is a non-parametric technique.

An Example: Spearman’s Rank Correlation test in SPSS

We want to examine the relationship between the English mark (1 to 5) and the level of stress (1 to 10).

A Spearman’s rank correlation coefficient was computed to determine the relationship between the English mark and level of stress. So, the results indicate a non-significant negative relationship between English mark and level of stress, [r(24) = .218, p = .306]. Consequently, as the level of stress increases, the English mark decreases. We, therefore, fail to reject the null hypothesis that the correlation between English mark and the level of stress is zero.