Hierarchical Clustering

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🆕 New in v1.0.5: A dendrogram chart was added — Ward linkage, leaf labels = variety/ID names, colored clusters.

Hierarchical 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?

Hierarchical Clustering automatically applies 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 + 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 Hierarchical Clustering.

Panel assignments (form fields in the program):

Columns: [ogrenci_id, davranis_01, davranis_02, +8 daha]
Clusters: 4
Linkage: ward
Missing strategy: drop

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/67_hierarchic_5stil_10davranis.xlsx

🎬 Scenario

Imagine we want to see how a small group of students naturally group together based on their classroom behaviors, and to visualize the nested structure of those groups. For 40 students we have ten behavioral measures, behavior_01 through behavior_10, and a recorded learning style for later interpretation. Hierarchical Clustering is appropriate because it builds a dendrogram showing how students merge step by step, letting us decide the number of clusters afterward and inspect whether behavioral groupings align with learning styles.

⚙️ Variable Selection

Clustering variables: behavior_01
Clustering variables: behavior_02
Clustering variables: behavior_03
Clustering variables: behavior_04
Clustering variables: behavior_05
Clustering variables: behavior_06
Clustering variables: behavior_07
Clustering variables: behavior_08
Clustering variables: behavior_09
Clustering variables: behavior_10

Data Preview (First 5 Rows)

student_id	style	behavior_01	behavior_02	behavior_03	behavior_04	behavior_05	behavior_06	behavior_07	behavior_08	behavior_09	behavior_10
1	visual	0.19	-0.885	-1.618	0.8	-1.089	0.403	-0.301	-0.283	0.215	-1.52
2	visual	0.692	-0.406	0.6	2.411	0.141	0.29	0.121	0.553	1.481	1.063
3	visual	-0.079	-0.315	0.461	0.577	-0.417	-0.444	-1.699	-0.368	-0.947	-1.501
4	visual	-0.292	0.932	0.052	-1.017	0.913	1.136	0.329	-0.772	-0.427	1.743
5	visual	-0.3	0.348	-0.608	0.351	1.344	-0.947	-0.133	-0.323	-0.339	0.01

n = 40 · Columns: student_id, style, behavior_01, behavior_02, behavior_03, behavior_04, behavior_05, behavior_06, behavior_07, behavior_08, behavior_09, behavior_10

📈 MerQur Output

HIERARCHICAL CLUSTERING RESULT ───────────────────────────────────────────── Number of clusters = 5 Silhouette = 0.131 (weak/overlapping structure) 5 styles from 10 behavior items

💬 Interpretation

We grouped student behaviors by similarity with hierarchical clustering — the result is a dendrogram (tree). Splitting into five clusters gives a silhouette of only 0.13: the clusters overlap, there is no sharp separation. This is meaningful in education — behavior styles are often a continuous spectrum, not sharp categories. A low silhouette means “natural grouping in the data is weak”, which is informative (perhaps fewer than 5 styles exist). Hierarchical clustering is more explanatory than K-Means for discovering the nested structure of groups and the right number of clusters.

⚠ 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.

▶ Hierarchical Clustering — video walkthrough

This analysis is demonstrated on a sample dataset from Anaesthesiology (the steps are identical across disciplines). The link jumps straight to 1:38. Narration is in Turkish.

▶ Watch this analysis (1:38) 📺 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.