Kruskal-Wallis Test
Kruskal-Wallis Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design 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 Kruskal-Wallis 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 Architecture, Planning & Design 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 Kruskal-Wallis Test.
Panel assignments (form fields in the program):
- Group Column:
toprak_tipi - Value Column:
yasam_yil
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 — Architecture, Planning & Design
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 Architecture, Planning & Design:
Peyzaj_Mimarligi/16_kruskal_soil_survival.xlsx
🎬 Scenario
We examine whether plant survival duration varies by soil type; for more than two groups and possibly non-normal duration data, Kruskal-Wallis is appropriate.
⚙️ Variable Selection
- Grouping: soil_type
- Measure: survival_year
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Epsilon-squared effect size.
- Dunn post-hoc pairwise comparison (tie-corrected, Bonferroni/Holm).
- Descriptives per group.
Data Preview (First 5 Rows)
| plant_id | soil_type | survival_year |
|---|---|---|
| 1 | clayey | 1.7 |
| 2 | clayey | 3.2 |
| 3 | clayey | 11.9 |
| 4 | clayey | 3.0 |
| 5 | clayey | 1.2 |
n = 45 · Columns: plant_id, soil_type, survival_year
📈 MerQur Output
💬 Interpretation
We tested whether plant survival duration varies by soil type with Kruskal-Wallis — the nonparametric ANOVA for more than two groups. The result is non-significant (H(2) = 4.46, p = 0.108): there is no significant difference in survival among soil types. A non-significant result is information too — in this data plants show similar survival across soil types (perhaps correct species choice/planting technique compensates for soil differences). Because count/ordinal duration data may not be normally distributed, Kruskal-Wallis is more appropriate than classic ANOVA. Negative findings help avoid over-focusing on soil type when making planting decisions. >> ADVANCED PARAMETERS (optional in the form — what they do): – Epsilon-squared effect size. – Dunn post-hoc pairwise comparison (tie-corrected, Bonferroni/Holm). – Descriptives per group.
⚠ 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 Architecture, Planning & Design file.
▶ Kruskal-Wallis Test — video walkthrough
This section is part of the Non-Parametrik Testler video (7 analyses in one video). The link below jumps straight to 3:32, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ Engineering · 🏥 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.