t-SNE
t-SNE is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Engineering 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 Engineering 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 t-SNE.
Panel assignments (form fields in the program):
- Columns:
[ornek_id, olcum_01, olcum_02, +10 daha] - perplexity:
15 - n_iter:
500 - 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 — Engineering
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 Engineering:
Muhendislik/70_tsne_5tur.xlsx
🎬 Scenario
We reduce 12-dimensional data to 2 dimensions with t-SNE for visualization. For nonlinear cluster discovery, t-SNE is appropriate.
⚙️ Variable Selection
- Variables: measurement_01..12
Data Preview (First 5 Rows)
| sample_id | type | measurement_01 | measurement_02 | measurement_03 | measurement_04 | measurement_05 | measurement_06 | measurement_07 | measurement_08 | measurement_09 | measurement_10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | A | 0.558 | 1.478 | 0.343 | -0.645 | -0.65 | -0.864 | 1.224 | -0.773 | -0.147 | 0.991 |
| 2 | A | 1.427 | 2.086 | -0.542 | -0.068 | 0.459 | -0.102 | 0.569 | -0.522 | -0.413 | 0.509 |
| 3 | A | 0.712 | 0.933 | 0.474 | 1.691 | 0.515 | -0.104 | 0.265 | 1.02 | -1.358 | -1.154 |
| 4 | A | -1.18 | -0.634 | 0.927 | -0.836 | 0.944 | -0.803 | 0.553 | 3.221 | -0.424 | 0.193 |
| 5 | A | 0.731 | 0.371 | -1.293 | 0.36 | 0.259 | -1.401 | -0.331 | -0.027 | 0.699 | -2.001 |
n = 125 · Columns (first 12 columns): sample_id, type, measurement_01, measurement_02, measurement_03, measurement_04, measurement_05, measurement_06, measurement_07, measurement_08, measurement_09, measurement_10
📈 MerQur Output
💬 Interpretation
We reduced 12-dimensional data to 2 dimensions for visualization with t-SNE (KL = 0.57, good quality). t-SNE tries to preserve high-dimensional neighborhoods, placing similar observations near and dissimilar ones far; it reveals nonlinear cluster structures visually that PCA misses. Interpretation is visual (the axes have no absolute meaning). In the field it is used for exploratory visualization of hidden type/group clusters in high-dimensional measurement data.
⚠ 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 Engineering file.
▶ t-SNE — video walkthrough
This section is part of the Kümeleme ve Boyut İndirgeme video (6 analyses in one video). The link below jumps straight to 4:40, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · 🏥 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 →
- 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.