Repeated-Measures ANOVA
Repeated-Measures ANOVA 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?
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 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 Repeated-Measures ANOVA.
Panel assignments (form fields in the program):
- Columns:
['birim_id', 'olcum_1', 'olcum_2', 'olcum_3', 'olcum_4']
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/08_repeated_anova_term_GPA.xlsx
🎬 Scenario
We tracked the same 60 units across an academic year, recording achievement at four time points from the first to the fourth measurement. The question is whether mean performance changes across these four occasions. A repeated measures ANOVA is appropriate because the same units are measured repeatedly over time, so the four measurements are correlated within each unit rather than independent.
⚙️ Variable Selection
- Repeated measures: measurement_1
- Repeated measures: measurement_2
- Repeated measures: measurement_3
- Repeated measures: measurement_4
- >> 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)
| unit_id | measurement_1 | measurement_2 | measurement_3 | measurement_4 |
|---|---|---|---|---|
| 1.0 | 49.93 | 58.3 | 61.87 | 56.19 |
| 2.0 | 37.36 | 39.03 | 42.93 | 43.42 |
| 3.0 | 61.13 | 61.99 | 68.18 | 69.2 |
| 4.0 | 42.51 | 41.53 | 50.04 | 50.26 |
| 5.0 | 46.11 | 54.82 | 53.69 | 55.87 |
n = 60 · Columns: unit_id, measurement_1, measurement_2, measurement_3, measurement_4
📈 MerQur Output
💬 Interpretation
We compared the same 60 students’ GPA across four consecutive terms with repeated-measures ANOVA. Because the same students are measured repeatedly, observations are dependent; RM-ANOVA accounts for this within-subject correlation (ordinary ANOVA does not). The result is very strong (F(3,177) = 108.40, p < .001, η²p = 0.65): GPA changes significantly across terms — a strong temporal trend. RM-ANOVA is the correct design for longitudinal studies where the same units are tracked over time, and is far more powerful than treating each term as independent. It is ideal for studies following student development term by term. >> 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 Education Sciences 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 15:10, 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.