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t-SNE

Research Disciplines → Education Sciences → t-SNE

🎓 Education Sciences · t-SNE

t-SNE

Boyut İndirgeme · Görselleştirme

t-SNE 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?

t-SNE 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

1

Load the data. Select the sample file from File → Open. MerQur auto-detects column types.

2

Select the analysis. From the left side select t-SNE.

3

Panel assignments (form fields in the program):

  • Columns: [ogrenci_id, madde_01, madde_02, +10 daha]
  • perplexity: 15
  • n_iter: 500
  • Missing strategy: drop
4

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

5

▶ Run — click the button. Results are produced automatically as a table + chart.

6

📄 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/70_tsne_student_5tip.xlsx

🎬 Scenario

Imagine we collected responses on twelve survey items from 125 students and we want to visualize whether students fall into distinct types in a two- dimensional map. We have item_01 through item_12, plus a known student_type label (A, B, C, D, E) we can color the plot by. t-SNE is ideal because it is a nonlinear dimension-reduction technique that preserves local neighborhood structure, so similar students appear close together, making it easy to see whether the five student types separate visually in the embedding.

⚙️ Variable Selection

  • Variables: item_01
  • Variables: item_02
  • Variables: item_03
  • Variables: item_04
  • Variables: item_05
  • Variables: item_06
  • Variables: item_07
  • Variables: item_08
  • Variables: item_09
  • Variables: item_10
  • Variables: item_11
  • Variables: item_12
  • Color/label (optional): student_type (A / B / C / D / E)

Data Preview (First 5 Rows)

student_idstudent_typeitem_01item_02item_03item_04item_05item_06item_07item_08item_09item_10
1A0.526-1.2270.76-0.031-0.955-0.8690.081-0.74-0.0890.265
2A0.84-0.8571.0771.254-0.2170.022-0.4820.274-1.296-0.546
3A0.6151.0291.166-0.011-0.311-0.984-0.338-0.072-0.492-0.475
4A1.515-0.6240.349-0.331.909-1.083-0.4451.247-0.446-1.828
5A-0.5590.5060.057-0.502-0.4022.7990.444-1.2380.438-0.69

n = 125 · Columns (first 12 columns): student_id, student_type, item_01, item_02, item_03, item_04, item_05, item_06, item_07, item_08, item_09, item_10

📈 MerQur Output

T-SNE RESULT ───────────────────────────────────────────── KL Divergence (fit quality) = 0.519 -> good 12-item profile (5 student types) reduced to 2D

💬 Interpretation

t-SNE is a nonlinear method that projects high-dimensional student profile data (12 items) into a two-dimensional plot. Unlike PCA, it focuses on preserving local neighbourhoods — similar students are placed close on the map — ideal for seeing hidden group structure. The KL divergence is 0.52, so the reduction quality is good; the five student types form visible clusters on the map. Important caveat: t-SNE axes and between-cluster distances are not interpretable, only the clustering pattern is. It is a powerful tool for visually exploring student types.

⚠ 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.

▶ t-SNE — video walkthrough

This section is part of the Kümeleme ve Boyut İndirgeme video (4 analyses in one video). The link below jumps straight to 1:54, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (1:54) 📺 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:
  1. American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
  2. Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
  3. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.