What is Spearman's Correlation Test?

    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.

    pearson correlation table

    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. )

    pearson correlation coefficient table


    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).

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


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    How to Run Spearman's Rank-Order Correlation in SPSS: Explanation Step by Step

    From the SPSS menu, choose Analyze – Correlate – Bivariate.

    Spearman's Rank Correlation Analysis

    A new window will open. From the left box transfer variable to the box variables using an arrow or double click.

    Spearman Correlation in SPSS

    The results will appear in the output window.

    spearman correlation result in spss

    How to report a Spearman's Rank-Order Correlation results: Explanation Step by Step

    How to Report Coefficient Table in SPSS Output?

    The output shows the results of Spearman’s rank correlation coefficient.

    Firstly, the row of the variable box shows Spearman’s rank correlation coefficient. Secondly, the row shows the p-value for Spearman’s rank correlation coefficient. Finally, the row shows the number of observations included in the analysis.

    spearman correlation spss output

    How to Interpret a Spearman's Rank-Order Correlation Results in APA Style?

    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.

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