Kruskal-Wallis Test
Kruskal-Wallis Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Education Sciences 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 Education Sciences 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:
SES - Value Column:
aylik_kitap_sayisi
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 — Education Sciences
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 Education Sciences:
Egitim_Bilimleri/16_kruskal_sound_book.xlsx
🎬 Scenario
Suppose we are examining whether background noise affects reading habits. We grouped 45 students by the sound level in their study environment, low sound, medium sound, or high sound, and counted their monthly_book_count. We want to compare reading volume across the three noise conditions, but book counts are skewed and the groups are small, so one-way ANOVA is not safe. The Kruskal-Wallis test is the appropriate nonparametric alternative for comparing a continuous outcome across three or more independent groups using ranks.
⚙️ Variable Selection
- Group (factor): sound (low sound, medium sound, high sound)
- Dependent variable: monthly_book_count
- >> 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)
| student_id | sound | monthly_book_count |
|---|---|---|
| 1 | low sound | 8.6 |
| 2 | low sound | 5.5 |
| 3 | low sound | 3.0 |
| 4 | low sound | 3.8 |
| 5 | low sound | 5.2 |
n = 45 · Columns: student_id, sound, monthly_book_count
📈 MerQur Output
💬 Interpretation
We tested whether the number of books read monthly varies by socio-economic level with Kruskal-Wallis — the nonparametric ANOVA for more than two groups, appropriate because count data are not normally distributed. The result is highly significant (H(2) = 16.53, p < .001), with a large effect (eta-squared = 0.35): reading habits are strongly tied to socio-economic level. This is a concrete indicator of opportunity inequality in education. For count/ordinal data Kruskal-Wallis is more appropriate than classic ANOVA. >> 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 Education Sciences 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 3:36, where this analysis begins. Narration is in Turkish.
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📚 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.