Mastering Spearman’s Rank-Order Correlation Using SPSS Statistics

In this comprehensive tutorial, we will explore how to perform Spearman’s Rank-Order Correlation using SPSS Statistics. This non-parametric test is used to determine the strength and direction of association between two ranked variables. We will cover all the necessary steps, from data input to results interpretation, following the APA style for reporting results.

Introduction to Spearman’s Rank-Order Correlation

Spearman’s Rank-Order Correlation, often denoted by the Greek letter rho (ρ), is a statistical measure that assesses the strength and direction of the relationship between two ranked variables. Unlike Pearson’s correlation, which requires the assumption of normality, Spearman’s correlation is a non-parametric test that does not assume a normal distribution of the variables. This makes it a versatile tool for analyzing ordinal data or continuous data that does not meet the assumptions of parametric tests.

Assumptions of Spearman’s Rank-Order Correlation

Before conducting Spearman’s Rank-Order Correlation, it is essential to ensure that your data meets the following assumptions:

• Ordinal, Interval, or Ratio Level of Measurement: Both variables should be measured at an ordinal, interval, or ratio level.
• Monotonic Relationship: The relationship between the two variables should be monotonic. This means that as one variable increases, the other variable should either consistently increase or decrease.

Example Research Question

Imagine you are interested in examining the relationship between students’ ranks in two different exams. You want to determine whether students who perform well in one exam also tend to perform well in another exam. This type of research question is ideal for Spearman’s Rank-Order Correlation.

Data Entry in SPSS

First, you need to enter your data into SPSS. If you are new to data entry in SPSS, refer to our guide on adding data and understanding Data View and Variable View in SPSS.

Ensure that your data is entered correctly, with each student’s rank in Exam 1 and Exam 2 entered as two separate variables. For this example, we will use the following hypothetical data:

Student	Exam 1 Rank	Exam 2 Rank
1	1	2
2	2	3
3	3	1
4	4	5
5	5	4

Running Spearman’s Rank-Order Correlation in SPSS

To run Spearman’s Rank-Order Correlation in SPSS, follow these steps:

1. Go to Analyze > Correlate > Bivariate…
2. Select the two variables you want to correlate. In this example, select Exam 1 Rank and Exam 2 Rank.
3. Check the box for Spearman under Correlation Coefficients.
4. Click OK to run the analysis.

Interpreting the Results

Once you run the analysis, SPSS will produce an output table showing the Spearman’s correlation coefficient (ρ) and the significance level (p-value). Here is an example of what the output might look like:

	Exam 1 Rank	Exam 2 Rank
Exam 1 Rank	1.000	0.900*
Exam 2 Rank	0.900*	1.000
*Correlation is significant at the 0.01 level (2-tailed).

In this example, the Spearman’s correlation coefficient is 0.900, which indicates a strong positive correlation between the ranks in Exam 1 and Exam 2. The p-value is less than 0.01, suggesting that this correlation is statistically significant.

Reporting the Results in APA Style

When reporting the results of a Spearman’s Rank-Order Correlation in APA style, include the following elements:

• The type of analysis conducted (e.g., Spearman’s Rank-Order Correlation).
• A brief description of the variables and the context of the study.
• The value of the correlation coefficient (ρ).
• The significance level (p-value).

Here is an example of how to report these results:

“A Spearman’s Rank-Order Correlation was run to determine the relationship between students’ ranks in Exam 1 and Exam 2. There was a strong, positive correlation between the two variables, ρ = .900, p < .01."

Conclusion

Spearman’s Rank-Order Correlation is a powerful tool for assessing the relationship between two ranked variables, especially when the data does not meet the assumptions of parametric tests. By following the steps outlined in this tutorial, you can confidently perform and interpret Spearman’s correlation using SPSS Statistics.

For more detailed tutorials on various statistical analyses using SPSS, check out our other posts:

• Creating Dummy Variables in SPSS
• Two-Way Repeated Measures ANOVA
• Wilcoxon Signed-Rank Test
• Poisson Regression
• Kruskal-Wallis H Test
• Principal Components Analysis (PCA)
• Kendall’s Tau-b
• Pearson’s Product-Moment Correlation
• Kaplan-Meier Survival Analysis
• ANCOVA