Reporting Mann-Whitney U Test in SPSS: A Detailed Guide|2025

Get expert help with Reporting Mann-Whitney U Test in SPSS. Learn how to conduct the test, interpret the results, and present your findings accurately with step-by-step guidance. In statistical analysis, the Mann-Whitney U test, also known as the Wilcoxon rank-sum test, is a non-parametric test used to determine whether there is a significant difference between two independent groups on a continuous or ordinal outcome. This test is particularly useful when the assumptions of the t-test are not met, such as when the data are not normally distributed or when there are outliers.

The Mann-Whitney U test is widely used in various research fields, including psychology, healthcare, social sciences, and education, for comparing two independent groups. For example, researchers may use the Mann-Whitney U test to compare treatment outcomes between two groups of patients or to assess differences in academic performance between two classes.

SPSS (Statistical Package for the Social Sciences) is a popular software tool for statistical analysis and makes it easy to perform the Mann-Whitney U test and interpret the results. As search volumes for terms like “Mann-Whitney U test SPSS,” “reporting Mann-Whitney U test results,” and “Mann-Whitney U test in SPSS” continue to increase, this article aims to provide a comprehensive guide for performing and reporting the Mann-Whitney U test in SPSS.

Table of Contents

What is the Mann-Whitney U Test?

The Mann-Whitney U test is a non-parametric statistical test used to compare differences between two independent groups when the dependent variable is ordinal or continuous, but not normally distributed. The test does not assume that the data follow a specific distribution, which makes it a valuable alternative to the t-test when data does not meet the assumptions of normality.

In essence, the Mann-Whitney U test compares the ranks of the values between the two groups, rather than their raw values. It tests the null hypothesis that the distributions of the two groups are the same. If the p-value is smaller than the chosen alpha level (usually 0.05), the null hypothesis is rejected, indicating that the two groups differ significantly in their distributions.

Mathematically, the test statistic is calculated as the U statistic, which is derived from the ranks of the values in both groups. The test is sensitive to differences in both the central tendency (median) and distribution shape.

When to Use the Mann-Whitney U Test

The Mann-Whitney U test is used when the following conditions hold:

Two independent groups: The groups being compared must be independent, meaning the data points in one group do not influence the data points in the other.
Ordinal or continuous data: The dependent variable should be measured on an ordinal or continuous scale, but it does not need to be normally distributed.
Non-parametric conditions: If the assumptions of normality or homogeneity of variance are violated, the Mann-Whitney U test is a suitable alternative to the independent samples t-test.

Some examples where the Mann-Whitney U test is commonly applied include:

Comparing the efficacy of two different treatments in medicine when the data is not normally distributed.
Examining differences in exam scores between two independent student groups.
Analyzing the preferences of two different groups of consumers for a product.

Assumptions of the Mann-Whitney U Test

Although the Mann-Whitney U test is non-parametric, it still has a few assumptions that need to be considered:

Independence of observations: The two groups being compared must be independent of each other. This means that the data from one group should not influence the data from the other group.
Ordinal or continuous data: The dependent variable should be measured at least on an ordinal scale.
Similar distribution shapes: While the Mann-Whitney U test does not require normality, it assumes that the shapes of the distributions of the two groups are similar. If the distributions are very different, the results of the test may be misleading.

Performing the Mann-Whitney U Test in SPSS

SPSS provides a straightforward way to perform the Mann-Whitney U test. Below are the steps for conducting this test in SPSS.

Step 1: Preparing Your Data

Before conducting the Mann-Whitney U test, make sure your data is formatted correctly. Each row should represent an individual observation, and each column should represent a variable. For the Mann-Whitney U test, you will need one independent variable (representing the two groups) and one dependent variable (the measurement you are comparing between the groups).

For example, if you are comparing the test scores of two different groups of students, the independent variable might be “Group” (with two categories: “Group 1” and “Group 2”), and the dependent variable would be “Test Score.”

Step 2: Running the Mann-Whitney U Test

Open your dataset in SPSS: Load your data into SPSS.
Select Analyze → Nonparametric Tests → Legacy Dialogs → 2 Independent Samples: From the top menu, go to Analyze, then Nonparametric Tests, followed by Legacy Dialogs, and then select 2 Independent Samples.
Select Variables: In the dialog box that appears, move your dependent variable (e.g., Test Score) into the “Test Variable List” box and your independent variable (e.g., Group) into the “Grouping Variable” box.
Define Groups: Click on the Define Groups button and specify the two groups in the grouping variable (e.g., 1 for Group 1 and 2 for Group 2).
Choose the Test: Under “Test Type,” ensure that Mann-Whitney U is selected.
Run the Test: Click OK to run the test.

Step 3: Interpreting the Output

Once the analysis is complete, SPSS will generate an output window that contains several key pieces of information. Here is how to interpret the key tables.

Group Statistics Table: This table provides the basic descriptive statistics for each group, including the number of observations, mean ranks, and the test statistic. The “Mean Rank” column shows the average rank of scores for each group. The group with the higher mean rank tends to have higher values.
Test Statistics Table: This table contains the U statistic, Z statistic, and the p-value. The most important values to report are:
- U Statistic: The Mann-Whitney U statistic, which measures the difference in the ranks between the two groups.
- Z Statistic: The standard score corresponding to the U statistic.
- Asymp. Sig. (2-tailed): The p-value, which indicates whether the difference between the two groups is statistically significant. A p-value less than 0.05 indicates a significant difference between the groups.

Reporting the Results of the Mann-Whitney U Test

When reporting the results of the Mann-Whitney U test, it is important to include key pieces of information that make the findings clear and understandable. Below is a standard format for reporting the results.

Descriptive Statistics: Begin by reporting the descriptive statistics for both groups. This includes the mean ranks, number of observations, and any other relevant information.Example:
- “Group 1 (n = 30) had a mean rank of 35.50, while Group 2 (n = 30) had a mean rank of 45.25.”
Mann-Whitney U Statistic: Report the U statistic and its associated p-value.Example:
- “A Mann-Whitney U test was conducted to compare the test scores of Group 1 and Group 2. The U statistic was 350.50, and the p-value was 0.03.”
Interpretation of p-value: Explain the meaning of the p-value in the context of your hypothesis. If the p-value is less than 0.05, you can conclude that there is a statistically significant difference between the two groups.Example:
- “The results indicate that there was a statistically significant difference in test scores between Group 1 and Group 2, U = 350.50, p = 0.03.”
Effect Size (Optional): You can also report the effect size, such as the rank-biserial correlation, which provides information on the magnitude of the difference between the groups.Example:
- “The effect size, as measured by the rank-biserial correlation, was 0.35, indicating a moderate effect.”

Common Mistakes to Avoid

When performing the Mann-Whitney U test and interpreting the results, researchers should be aware of common mistakes:

Misunderstanding the p-value: A p-value less than 0.05 does not necessarily mean that the difference is practically significant. It only indicates statistical significance.
Ignoring assumptions: The Mann-Whitney U test assumes that the distributions of the two groups are similar in shape. If this assumption is violated, the results may be misleading.
Incorrect interpretation of ranks: The Mann-Whitney U test compares ranks, not raw values. Researchers should avoid interpreting the results as if they represent direct differences in the means of the groups.
Failure to report effect size: Reporting the effect size alongside the p-value helps provide context for the magnitude of the difference between groups.

Conclusion

The Mann-Whitney U test is an essential non-parametric tool for comparing two independent groups when the assumptions of normality and equal variances cannot be met. SPSS provides a user-friendly platform for performing the test, and understanding how to report the results is crucial for accurately interpreting the findings. By following the steps outlined in this guide, researchers can confidently apply the Mann-Whitney U test in SPSS and effectively communicate their results in academic or professional settings. Whether comparing treatment outcomes, behavioral differences, or other group-based comparisons, the Mann-Whitney U test remains a vital method in statistical analysis.