VARCLUS (Variable Clustering)
VARCLUS (Variable Clustering) is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Health 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?
VARCLUS (Variable Clustering) automatically performs all assumption checks required for the data type (normality, homogeneity of variance, etc.) in the background and presents the results in a clear table + chart. Automatic APA 7-formatted interpretation, effect sizes (Cohen’s d, η², R²) and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Health 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 VARCLUS (Variable Clustering).
Panel assignments (form fields in the program):
- Ozellik Sutunlari:
[fizik_m1, fizik_m2, fizik_m3, ... +15 adet] - Clusters:
3
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 — Health 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 Health Sciences:
Tip/38_varclus_SF36_18.xlsx
🎬 Scenario
We cluster eighteen quality-of-life items (physical/mental/social) by their similarity. To find the latent dimension structure, VarClus is appropriate.
⚙️ Variable Selection
- Variables: physical_m1..6, mental_m1..6, social_m1..6 (18 madde / items)
Data Preview (First 5 Rows)
| patient_id | physical_m1 | physical_m2 | physical_m3 | physical_m4 | physical_m5 | physical_m6 | mental_m1 | mental_m2 | mental_m3 | mental_m4 | mental_m5 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | -0.802 | -0.51 | -0.38 | -0.228 | -1.038 | -0.864 | -0.544 | -1.118 | -0.856 | -0.964 | -0.874 |
| 2.0 | 0.678 | 1.078 | 1.491 | 1.076 | 0.691 | 1.125 | 0.853 | 0.682 | 1.033 | 1.201 | -0.387 |
| 3.0 | 0.498 | -0.107 | -0.375 | -0.115 | -0.307 | -0.116 | 1.301 | 1.271 | 0.659 | 0.712 | 1.172 |
| 4.0 | 0.435 | -0.539 | 0.354 | 0.449 | 0.651 | 0.691 | -0.304 | -0.641 | -0.741 | -0.108 | -0.652 |
| 5.0 | -0.579 | -1.778 | -0.43 | 0.457 | -0.708 | -1.595 | 0.582 | 0.296 | 0.521 | 0.68 | -0.375 |
n = 180 · Columns (first 12 columns): patient_id, physical_m1, physical_m2, physical_m3, physical_m4, physical_m5, physical_m6, mental_m1, mental_m2, mental_m3, mental_m4, mental_m5
📈 MerQur Output
💬 Interpretation
We clustered eighteen quality-of-life items (physical, mental, social — SF-36-style, 6 each) by how related they are: they grouped into 3 main dimensions — most likely matching the natural physical/mental/social domains. VarClus groups the VARIABLES, not the observations — by placing highly correlated items in the same cluster it reveals the latent dimensional structure of the data set. In medicine it is practical for reducing a long quality-of-life scale to a few core sub-scales and for spotting redundant items.
⚠ 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 Health Sciences file.
▶ VARCLUS (Variable Clustering) — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 📊 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.