UMAP

Boyut İndirgeme · Görselleştirme

🆕 New in v1.0.5: A Chart tab was added to this analysis (boxplot, interaction, profile, bar, scatter, biplot, or forecast — depending on analysis type).

UMAP 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?

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 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 UMAP.

Panel assignments (form fields in the program):

feature_cols: [ogrenci_id, davranis_01, davranis_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 — 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/72_umap_behavior_5tip.xlsx

🎬 Scenario

Here we have 200 students rated on 25 behavioral indicators, and each student also belongs to one of five latent behavior profiles, labeled S1 through S5 in lower_type. We want to see whether these 25 behaviors naturally separate the students into the five known profile groups when projected onto a 2D plane. UMAP is ideal for this because it is a nonlinear dimensionality reduction technique that preserves local neighborhood structure, so behaviorally similar students stay together, and we can color the embedding by lower_type to check whether the profiles form distinct clusters.

⚙️ Variable Selection

Features (high-dim input): behavior_01 … behavior_25 (all 25 behavior items)
Color / Label (grouping): lower_type (S1, S2, S3, S4, S5)

Data Preview (First 5 Rows)

student_id	lower_type	behavior_01	behavior_02	behavior_03	behavior_04	behavior_05	behavior_06	behavior_07	behavior_08	behavior_09	behavior_10
1	S1	0.919	-0.374	-0.135	0.982	1.353	-1.138	0.836	-2.118	1.164	-0.049
2	S1	-0.683	0.188	-0.839	0.111	-0.686	0.293	0.67	-0.268	-0.837	1.092
3	S1	-0.968	-0.615	-1.128	0.985	1.474	-0.519	-0.212	1.055	0.111	-0.698
4	S1	-0.49	-0.734	-1.322	-0.678	-0.051	0.377	0.077	-1.635	0.433	-2.254
5	S1	-1.885	0.715	2.111	0.388	0.447	0.592	1.071	2.284	-1.765	-1.25

n = 200 · Columns (first 12 columns): student_id, lower_type, behavior_01, behavior_02, behavior_03, behavior_04, behavior_05, behavior_06, behavior_07, behavior_08, behavior_09, behavior_10

📈 MerQur Output

UMAP RESULT ───────────────────────────────────────────── UMAP dimension reduction (25 behavior items -> 2D, 5 types) n_neighbors and min_dist balance local/global structure

💬 Interpretation

UMAP is a modern dimension-reduction method similar to t-SNE but it preserves both local and global structure better and is faster. We projected student profiles of 25 behavior items into a two-dimensional map; similar behavior profiles cluster. The n_neighbors parameter tunes the local-global balance, and min_dist the tightness of clusters. UMAP is increasingly preferred for discovering hidden group structure in high-dimensional behavior/survey data. It is used for visualisation; the clustering pattern is interpreted, not the axis values.

⚠ 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.

▶ UMAP — video walkthrough

This section is part of the Kümeleme ve Boyut İndirgeme video (4 analyses in one video). The link below jumps straight to 5:22, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (5:22) 📺 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.