Permutation Test
Permutation 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?
Permutation Test automatically applies all necessary assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automated interpretation to 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 Permutation Test.
Panel assignments (form fields in the program):
- Dependent Variable:
motivasyon - Group Column:
grup - Permutations:
500 - Seed:
42
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/12_permutation_motivation_group.xlsx
🎬 Scenario
Imagine a motivation intervention where 53 students were split into a control and an experiment group, and we recorded their academic motivation scores. We want to know whether the experimental program genuinely raised motivation, but the groups are uneven and we are not comfortable assuming the score differences follow a normal distribution. A permutation test answers this directly by repeatedly shuffling the group labels to see how often a mean difference as large as ours would arise by chance, giving an exact, assumption-free p-value for the group effect.
⚙️ Variable Selection
- Group (factor): group (control, experiment)
- Test variable: motivation
Data Preview (First 5 Rows)
| student_id | group | motivation |
|---|---|---|
| 40 | control | 3.81 |
| 49 | control | 4.21 |
| 22 | experiment | 4.47 |
| 8 | experiment | 4.47 |
| 25 | experiment | 4.28 |
n = 53 · Columns: student_id, group, motivation
📈 MerQur Output
💬 Interpretation
We tested the motivation difference between two groups with a permutation test. Permutation shuffles the group labels thousands of times to measure whether the observed difference could arise by chance — requiring no distributional assumption. The result is significant (p = 0.005): the groups’ motivation differs more than chance allows. For small samples or when normality is doubtful, permutation is a robust, assumption-free alternative to the t-test. It is a reliable backup for group comparisons in educational experiments.
⚠ 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.
▶ Permutation Test — video walkthrough
This section is part of the Veriye İlk Bakış ve Parametrik Testler video (14 analyses in one video). The link below jumps straight to 24:59, 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.