Repeated-Measures ANOVA
Repeated-Measures ANOVA 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?
Repeated-Measures ANOVA 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 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 Repeated-Measures ANOVA.
Panel assignments (form fields in the program):
- Columns:
['mevsim_ilkbahar_ziyaretci', 'mevsim_yaz_ziyaretci', 'mevsim_sonbahar_ziyaretci', 'mevsim_kis_ziyaretci']
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/08_repeated_anova_season_visitor.xlsx
🎬 Scenario
We compare the same parks’ visitor counts across four seasons. The same units are measured repeatedly (dependent observations), so repeated-measures ANOVA is correct.
⚙️ Variable Selection
- Repeated measures: season_spring_visitor, season_summer_visitor, season_autumn_visitor, season_winter_visitor
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Sphericity correction: Greenhouse-Geisser when Mauchly’s test is violated.
- Mauchly’s sphericity test (W, p).
- Generalized eta-squared (ges) effect size.
- Post-hoc pairwise (Bonferroni/Holm); descriptives per level.
Data Preview (First 5 Rows)
| park_id | season_spring_visitor | season_summer_visitor | season_autumn_visitor | season_winter_visitor |
|---|---|---|---|---|
| 1 | 678 | 967 | 677 | 432 |
| 2 | 576 | 753 | 468 | 327 |
| 3 | 826 | 991 | 744 | 611 |
| 4 | 632 | 766 | 557 | 411 |
| 5 | 647 | 942 | 573 | 460 |
n = 80 · Columns: park_id, season_spring_visitor, season_summer_visitor, season_autumn_visitor, season_winter_visitor
📈 MerQur Output
💬 Interpretation
We compared the same 80 parks’ visitor counts across four seasons with repeated-measures ANOVA. Because the same parks are measured repeatedly, observations are dependent; RM-ANOVA accounts for this within-unit correlation. The result is overwhelming (F(3,237) = 1090.49, p < .001, η²p = 0.93): visitor count changes very markedly across seasons — strong seasonality (summer peak, winter low). Effect size 93%! RM-ANOVA is the correct design for seasonal-use studies where the same parks are tracked over time, and is far more powerful than treating each season as independent. Profiling parks’ seasonal-use dynamics is valuable for maintenance and event planning. >> ADVANCED PARAMETERS (optional in the form — what they do): – Sphericity correction: Greenhouse-Geisser when Mauchly’s test is violated. – Mauchly’s sphericity test (W, p). – Generalized eta-squared (ges) effect size. – Post-hoc pairwise (Bonferroni/Holm); descriptives per level.
⚠ 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.
▶ Repeated-Measures ANOVA — 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 14:50, 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.