UMAP
UMAP is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Sport 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?
UMAP 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 Sport 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 UMAP.
Panel assignments (form fields in the program):
- feature_cols:
[sporcu_id, feat_01, feat_02, +23 daha] - n_components:
2 - n_neighbors:
15 - min_dist:
0.1
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 — Sport 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 Sport Sciences:
Spor_Bilimleri/72_umap_5tip.xlsx
🎬 Scenario
We reduce 25-dimensional feature data to 2 dimensions with UMAP. For both local and global structure, UMAP is appropriate.
⚙️ Variable Selection
- Variables: feat_01..25
Data Preview (First 5 Rows)
| athlete_id | lower_type | feat_01 | feat_02 | feat_03 | feat_04 | feat_05 | feat_06 | feat_07 | feat_08 | feat_09 | feat_10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | S1 | 1.379 | 0.58 | -1.409 | -1.314 | -0.505 | -0.5 | -1.02 | -0.836 | 0.528 | 0.422 |
| 2 | S1 | 0.751 | 0.022 | -1.157 | 1.494 | -1.256 | 1.044 | -0.526 | -0.806 | -2.846 | -1.069 |
| 3 | S1 | 1.153 | -1.949 | 0.48 | 0.282 | 1.067 | 0.446 | 0.049 | 0.612 | -0.243 | -0.858 |
| 4 | S1 | -0.729 | -1.27 | 0.748 | 1.044 | -0.673 | 0.278 | -0.014 | 0.121 | 1.682 | 1.612 |
| 5 | S1 | -1.112 | -0.632 | 1.042 | -0.097 | 0.263 | 0.901 | 0.003 | 2.095 | -2.064 | -0.66 |
n = 200 · Columns (first 12 columns): athlete_id, lower_type, feat_01, feat_02, feat_03, feat_04, feat_05, feat_06, feat_07, feat_08, feat_09, feat_10
📈 MerQur Output
💬 Interpretation
We reduced 25-dimensional feature data to 2 dimensions with UMAP. UMAP does nonlinear dimensionality reduction like t-SNE but preserves both local and global structure better and is faster. Larger n_neighbors emphasizes broader groups, larger min_dist emphasizes the gaps between clusters. In sports science it is a modern choice for visualizing high-dimensional performance/measurement data and exploring natural cluster structure.
⚠ 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 Sport Sciences file.
▶ UMAP — video walkthrough
This section is part of the Kümeleme ve Boyut İndirgeme video (6 analyses in one video). The link below jumps straight to 7:38, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin 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.