Hierarchical Clustering
Hierarchical 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?
Hierarchical 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 Hierarchical Clustering.
Panel assignments (form fields in the program):
- Columns:
[gen_01, gen_02, gen_03, ... +7 adet] - Clusters:
5 - 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 — 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/67_hierarchic_5tip_10gen.xlsx
🎬 Scenario
We hierarchically cluster patients by ten gene expressions. For nested subtype structure, hierarchical clustering is appropriate.
⚙️ Variable Selection
- Variables: gene_01..10
- Number of clusters: 5
Data Preview (First 5 Rows)
| patient_id | lower_type | gene_01 | gene_02 | gene_03 | gene_04 | gene_05 | gene_06 | gene_07 | gene_08 | gene_09 | gene_10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | type-A | -0.645 | -0.134 | 0.853 | 0.885 | 0.266 | 1.202 | 0.909 | 0.025 | -0.523 | -0.634 |
| 2 | type-A | 0.145 | -0.929 | -0.195 | -1.969 | 0.941 | -0.143 | -0.619 | 1.506 | 0.056 | -0.096 |
| 3 | type-A | -0.788 | -0.096 | 0.448 | -1.018 | -1.396 | 0.248 | -0.984 | -1.192 | 1.009 | 0.754 |
| 4 | type-A | -0.829 | 1.248 | -1.403 | 1.54 | -0.016 | 0.652 | 1.1 | -0.587 | 0.12 | -1.656 |
| 5 | type-A | 1.055 | 1.137 | 0.398 | 0.551 | -1.811 | 0.192 | 0.077 | -0.054 | 0.933 | -1.869 |
n = 40 · Columns: patient_id, lower_type, gene_01, gene_02, gene_03, gene_04, gene_05, gene_06, gene_07, gene_08, gene_09, gene_10
📈 MerQur Output
💬 Interpretation
We split patients into 5 groups by ten gene expressions with hierarchical clustering (silhouette = 0.15, weak separation — groups partly overlap). Hierarchical clustering merges observations step by step to build a tree (dendrogram); its difference from k-means is not having to fix the cluster count in advance and seeing the nested structure. In medicine/genomics it is used to explore a hierarchy of patient or gene groups (subtypes) and to read the natural cluster count from the dendrogram.
⚠ 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.
▶ Hierarchical 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.