Cochran’s Q Test
Cochran’s Q 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?
Cochran’s Q 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 Cochran’s Q Test.
Panel assignments (form fields in the program):
- MR Set kaynak:
guvenli_hisseder - MR metadata:
None
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/32_cochran_q_safety_site.xlsx
🎬 Scenario
We compare the same visitors’ feeling of safety (binary) across different park sites; for repeated binary measures Cochran’s Q is appropriate.
⚙️ Variable Selection
- Subject: visitor_id
- Condition (site): site
- Binary outcome: safe_feels
Data Preview (First 5 Rows)
| visitor_id | site | safe_feels |
|---|---|---|
| 1 | S1-brightness | yes |
| 1 | S2-semi dark | yes |
| 1 | S3-dark | yes |
| 2 | S1-brightness | yes |
| 2 | S2-semi dark | yes |
n = 150 · Columns: visitor_id, site, safe_feels
📈 MerQur Output
💬 Interpretation
We compared the same visitors’ feeling of safety (yes/no) across different park sites with Cochran’s Q test — the multi-occasion counterpart of repeated binary measures. The result is non-significant (Q = 0, p = 1.0): there is no difference in safety feeling across sites — visitors perceive similar safety everywhere. A non-significant result is valuable too; it says the sites do not differ in safety perception in this data (perhaps consistent safety design/lighting). Cochran’s Q is the right way to test change in repeated binary (yes/no) measurements on the same people.
⚠ 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.
▶ Cochran’s Q Test — video walkthrough
This section is part of the Çok Seçimli Yanıtlar video (4 analyses in one video). The link below jumps straight to 5:25, 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.