Friedman Test
Friedman 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 Friedman 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 Friedman Test.
Panel assignments (form fields in the program):
- Columns:
['kompozisyon_A_estetik', 'kompozisyon_B_estetik', 'kompozisyon_C_estetik', 'kompozisyon_D_estetik']
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/17_friedman_panelist_composition.xlsx
🎬 Scenario
We compare the same panelists’ aesthetic scores for four landscape compositions. Same raters score all compositions (repeated, ordinal), so the Friedman test is appropriate.
⚙️ Variable Selection
- Repeated measures: composition_A_aesthetic, composition_B_aesthetic, composition_C_aesthetic, composition_D_aesthetic
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Pairwise Wilcoxon signed-rank post-hoc (Bonferroni/Holm).
- Descriptives per condition.
Data Preview (First 5 Rows)
| panelist_id | composition_A_aesthetic | composition_B_aesthetic | composition_C_aesthetic | composition_D_aesthetic |
|---|---|---|---|---|
| 1.0 | 3.47 | 3.96 | 3.89 | 4.37 |
| 2.0 | 3.33 | 3.92 | 3.37 | 3.78 |
| 3.0 | 2.06 | 2.86 | 3.58 | 3.47 |
| 4.0 | 3.93 | 4.3 | 4.18 | 4.8 |
| 5.0 | 3.29 | 3.67 | 3.7 | 4.08 |
n = 30 · Columns: panelist_id, composition_A_aesthetic, composition_B_aesthetic, composition_C_aesthetic, composition_D_aesthetic
📈 MerQur Output
💬 Interpretation
We compared the same panelists’ aesthetic scores for four different landscape compositions with the Friedman test — the nonparametric counterpart of repeated-measures ANOVA. The result is highly significant (chi-square(3) = 41.91, p < .001), with Kendall W = 0.47 indicating moderate-high consistency of the ranking across panelists: they largely agree on which composition is more aesthetic. Because the same panelists rate all compositions, observations are dependent; Friedman accounts for this correctly. In landscape aesthetic evaluation (visual- preference surveys), it is the right test for comparing design alternatives on the same raters. >> ADVANCED PARAMETERS (optional in the form — what they do): – Pairwise Wilcoxon signed-rank post-hoc (Bonferroni/Holm). – Descriptives per condition.
⚠ 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.
▶ Friedman Test — video walkthrough
This section is part of the Non-Parametrik Testler video (7 analyses in one video). The link below jumps straight to 5:16, 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.