VARCLUS (Variable Clustering)

Multivariate · Clustering

VARCLUS (Variable Clustering) 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?

VARCLUS (Variable Clustering) automatically performs, in the background, all the assumption checks required for the relevant data type (normality, homogeneity of variance, etc.) and presents the results with a clear table and chart. Automatic 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 VARCLUS (Variable Clustering).

Panel assignments (form fields in the program):

feature_cols: [ogrenci_id, ozYeterlik_m1, ozYeterlik_m2, +12 daha]
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 — 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/38_varclus_18_item.xlsx

🎬 Scenario

Suppose we administered an 18-item psychological scale to 180 students and we want to confirm whether the items naturally group into the three intended constructs. The items are six self-efficacy items, six motivation items, and six anxiety items, all on numeric scales. Rather than assuming the structure, we want the data to reveal which items cluster together. We use variable clustering because it groups the 18 items into clusters of mutually correlated variables, letting us verify that the self-efficacy, motivation, and anxiety items really form distinct families.

⚙️ Variable Selection

Variables to cluster (items): self_efficacy_m1, self_efficacy_m2, self_efficacy_m3, self_efficacy_m4, self_efficacy_m5, self_efficacy_m6
Variables to cluster (items): motivation_m1, motivation_m2, motivation_m3, motivation_m4, motivation_m5, motivation_m6
Variables to cluster (items): anxiety_m1, anxiety_m2, anxiety_m3, anxiety_m4, anxiety_m5, anxiety_m6

Data Preview (First 5 Rows)

student_id	self_efficacy_m1	self_efficacy_m2	self_efficacy_m3	self_efficacy_m4	self_efficacy_m5	self_efficacy_m6	motivation_m1	motivation_m2	motivation_m3	motivation_m4	motivation_m5
1.0	-0.184	0.54	0.549	0.348	0.129	0.438	1.018	1.027	-0.078	0.923	1.222
2.0	-0.453	0.048	-0.835	-0.516	-0.977	-0.36	0.998	0.979	0.958	0.725	2.129
3.0	1.301	0.838	0.247	0.075	0.897	0.526	-1.235	-1.169	-0.932	-0.484	-0.788
4.0	2.613	2.358	1.816	1.99	1.98	1.549	0.97	0.295	0.384	0.801	-0.317
5.0	0.507	0.59	0.398	-0.027	-0.07	0.44	-0.204	-0.936	-0.15	-0.193	-0.937

n = 180 · Columns (first 12 columns): student_id, self_efficacy_m1, self_efficacy_m2, self_efficacy_m3, self_efficacy_m4, self_efficacy_m5, self_efficacy_m6, motivation_m1, motivation_m2, motivation_m3, motivation_m4, motivation_m5

📈 MerQur Output

VARCLUS (VARIABLE CLUSTERING) RESULT ───────────────────────────────────────────── Number of clusters = 3 (18 items: self-efficacy / motivation / anxiety) A representative item was selected from each cluster

💬 Interpretation

We clustered 18 scale items by similarity into three groups. VarClus clusters items, not observations: it shows which items measure the same “construct”. The three clusters formed exactly as expected: self-efficacy items, motivation items and anxiety items clustered separately. This confirms the scale really measures three sub-dimensions and lets us pick one representative per group to reduce the number of items. In scale development and item reduction it is extremely practical for weeding out redundancy.

⚠ 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.

▶ VARCLUS (Variable Clustering) — video walkthrough

This section is part of the İlişki ve Korelasyon video (6 analyses in one video). The link below jumps straight to 9:54, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (9:54) 📺 All videos

🔀 The same analysis in other disciplines:
🧪 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 →

Sources:

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.