Multiple Comparisons
Multiple Comparisons 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?
Multiple Comparisons 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 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 Multiple Comparisons.
Panel assignments (form fields in the program):
- p-Values:
0.001,0.012,0.04,0.07,0.18 - Significance (α):
0.05 - Methods:
['bonferroni', 'holm', 'bh']
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/13_multiple_comparison_material.xlsx
🎬 Scenario
Here we compared nine different instructional materials, each used by a subset of 180 students, and recorded their test_score. Because comparing nine materials means running many pairwise tests, the chance of a false positive balloons. We therefore apply multiple comparison corrections, such as Bonferroni or Holm, to adjust the p-values across all material pairs, so that any difference we declare significant is real and not just an artifact of testing so many combinations at once.
⚙️ Variable Selection
- Group (factor): material (9 levels)
- Dependent variable: test_score
Data Preview (First 5 Rows)
| student_id | material | test_score |
|---|---|---|
| 1 | control | 59.9 |
| 2 | control | 55.7 |
| 3 | control | 60.0 |
| 4 | control | 54.3 |
| 5 | control | 64.5 |
n = 180 · Columns: student_id, material, test_score
📈 MerQur Output
💬 Interpretation
When we compare different teaching materials pairwise on test score, we run many tests — each carrying a false- positive risk. Multiple-comparison correction controls that risk. Here five of eight comparisons stayed significant under every correction method (Bonferroni, Holm, FDR) — 5/8. So some materials genuinely differ from the reference, while others lose significance after correction. Skipping this step risks reporting spurious differences; in educational material/method comparisons the correct correction is essential.
⚠ 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.
▶ Multiple Comparisons — 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 26:48, where this analysis begins. Narration is in Turkish.
🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ 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.