Wilcoxon Signed-Rank Test
Wilcoxon Signed-Rank Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Engineering sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
The Wilcoxon Signed-Rank Test automatically performs all required assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automatic interpretation in the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Engineering domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select Wilcoxon Signed-Rank Test.
Panel assignments (form fields in the program):
- Column 1:
saglik_oncesi - Column 2:
saglik_sonrasi
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Engineering
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Engineering:
Muhendislik/15_wilcoxon_tree_health.xlsx
🎬 Scenario
We compare health scores measured before/after in the same trees, without assuming normality. For paired non-normal data, Wilcoxon is appropriate.
⚙️ Variable Selection
- 1st measure / group: health_before
- 2nd measure / group: health_post
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Hypothesis direction; continuity correction.
- Zero-difference handling: wilcox (drop) / pratt / zsplit.
- Descriptives for both measures and their difference.
Data Preview (First 5 Rows)
| tree_id | health_before | health_post | treatment_month |
|---|---|---|---|
| 1 | 3 | 4 | 4 |
| 2 | 7 | 7 | 10 |
| 3 | 3 | 4 | 3 |
| 4 | 5 | 6 | 6 |
| 5 | 3 | 6 | 7 |
n = 35 · Columns: tree_id, health_before, health_post, treatment_month
📈 MerQur Output
💬 Interpretation
We compared health scores measured before/after in the same trees — without assuming normality, on a rank basis: W = 0, p < .001, very large effect (r = 0.89). Health changed consistently and strongly after the intervention. Wilcoxon is the nonparametric counterpart of the paired t-test; it is the right choice for ordinal or non-normal before/after measures. In the field it gives reliable results for pre/post-treatment health measurements. >> ADVANCED PARAMETERS (optional in the form — what they do): – Hypothesis direction; continuity correction. – Zero-difference handling: wilcox (drop) / pratt / zsplit. – Descriptives for both measures and their difference.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Engineering file.
▶ Wilcoxon Signed-Rank Test — video walkthrough
This section is part of the Non-Parametrik Testler video (7 analyses in one video). The link below jumps straight to 1:40, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.