K-Means Kümeleme

Kümeleme · Bölmeli

K-Means Clustering is one of the statistical analyses applied automatically in MerQur. This page provides a representative illustration of how the analysis is applied to a sample dataset from the fields of Agriculture, Forestry, and Aquaculture, how the MerQur output will appear, and how it will be reported in APA 7 format.

🎯 What is it for?

K-Means Clustering automatically applies all necessary 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 APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.

📌 When is it used?

In the statistical analysis of measurements belonging to the Agriculture, Forestry and Aquaculture field
To produce APA 7 compliant result tables for academic publication
In hypothesis testing and decision-making processes
After selecting an appropriate method in undergraduate / master’s / doctoral theses

📐 Assumptions

Appropriate scale — Variables must be at the scale level required by the analysis (nominal/ordinal/interval/ratio)
Independent observations — Observations must come from mutually independent individuals
Adequate sample — The minimum n requirement for the analysis must be met
Outlier check — Outliers must be detected and evaluated

If the assumptions are violated, MerQur automatically steers you toward a non-parametric or robust alternative.

🛠 How to do it in MerQur

Load the data. Select the sample file from the File → Open menu. MerQur automatically detects the column types.

Select the analysis. Choose K-Means Clustering from the left sidebar.

Panel assignments (form fields in the program):

Columns: ['agac_id', 'dbh_cm', 'boy_m', 'biyokutle_kg']
Number of clusters: 3
init: k-means++
Missing strategy: drop

Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).

Press the ▶ Run button. The results are generated automatically as a table + chart.

📄 Export to Word. A report in APA 7 format with italic statistical symbols.

📊 Sample Dataset — Ziraat, Orman ve Su Ürünleri

ℹ Note: The MerQur output and interpretation tables on this page are illustrative and intended to help understand the analysis. The numerical results for your own data may differ; this page does not have to match the YouTube video walkthrough exactly.

🎬 Example File Used in the Video

The YouTube video walkthrough of this page was recorded on the following sample file for the Agriculture, Forestry and Aquaculture field:

MerQur_Hoca_Tanitim/Ziraat_Orman_Su/66_kmeans_orman_tipi_morfometri.xlsx

Topic: The application of K-Means Clustering analysis on a real sample data set belonging to the Agriculture, Forestry and Aquaculture field.

Data Preview (First 5 Rows)

parsel_id	yesil_orani	biyokutle
1.00	0.30	188.00
2.00	0.55	383.00
3.00	0.77	513.00
4.00	0.83	853.00
5.00	0.33	210.00

n = 240 · Sütunlar: parsel_id, yesil_orani, biyokutle

MerQur Output (Illustrative)

K-MEANS CLUSTERING RESULT ───────────────────────────────────────────── Data: n = 240, 3 columns Test statistic: 12.43 p-value: < .001 Effect size: medium-large Detailed results are generated automatically with MerQur’s ▶ Run command.

MerQur Interpretation (Plain Language)

K-Means Clustering revealed a statistically significant finding among the relevant variables (p < .001). Detailed results and visuals are generated automatically in MerQur’s output panel.

APA 7 Interpretation (Academic Format · Illustrative)

According to the K-Means Clustering results, a statistically significant effect/relationship was observed on yesil_orani (p < .001). The effect size was at a medium-large level, indicating that the findings carry practical significance. For a detailed report, MerQur’s APA 7 Word output can be used.

⚠ Common Mistakes

Specifying the data type incorrectly (e.g., loading a categorical variable as numeric)
Skipping the assumption checks and looking directly at the p-value
Not reporting the effect size — APA 7 requires both p and the effect size
Not applying a Type I error correction (Bonferroni/Tukey) in multiple comparisons
Not switching to a non-parametric alternative when n is insufficient

📹 Video Walkthrough

You can watch the video below for an end-to-end application of this analysis on the Agriculture, Forestry and Aquaculture file.

▶

The video will soon be uploaded to the MerQur YouTube channel

📺 MerQur YouTube Channel

🔀 The same analysis in other Core Fields:
🎓 Educational Sciences · 🧪 Natural Sciences and Mathematics · 🏛 Architecture, Planning and Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities and Administrative Sciences · 🏃 Sport Sciences

📚 If You Used This Analysis, Cite MerQur

If you performed this analysis in a scientific study using MerQur, please use the following citation (APA 7) in accordance with the academic citation requirement:

Örücü, Ö. K. (2026). MerQur: An Integrated Academic Data Analysis and Reporting Platform [Computer Software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001

For BibTeX, RIS, EndNote and the English equivalent: all citation formats →

References:

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.