SVM (Support Vector Machine)
SVM (Support Vector Machine) 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?
SVM (Support Vector Machine) 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 SVM (Support Vector Machine).
Panel assignments (form fields in the program):
- error:
cats() got an unexpected keyword argument 'max_unique'
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/64_svm_university_passed.xlsx
🎬 Scenario
Imagine we want to predict whether a student will pass the university entrance exam from their academic indicators. For 250 students we recorded their GPA, math score, a practice trial_score, and weekly_study hours, with the outcome university_passed coded as 1 for passed and 0 for not passed. A Support Vector Machine suits this binary classification problem well because it finds the optimal boundary separating successful from unsuccessful students, and with a kernel it can capture nonlinear separations between the two groups even when the predictors overlap.
⚙️ Variable Selection
- Target (binary): university_passed (1 = passed, 0 = not passed)
- Predictors: GPA
- Predictors: math
- Predictors: trial_score
- Predictors: weekly_study
Data Preview (First 5 Rows)
| student_id | GPA | math | trial_score | weekly_study | university_passed |
|---|---|---|---|---|---|
| 1.0 | 3.51 | 77.5 | 86.9 | 16.7 | 1.0 |
| 2.0 | 4.0 | 89.7 | 91.5 | 20.1 | 1.0 |
| 3.0 | 2.72 | 68.1 | 39.5 | 5.9 | 0.0 |
| 4.0 | 2.07 | 40.4 | 49.4 | 8.2 | 0.0 |
| 5.0 | 2.13 | 56.1 | 43.4 | 1.9 | 0.0 |
n = 250 · Columns: student_id, GPA, math, trial_score, weekly_study, university_passed
📈 MerQur Output
💬 Interpretation
We found the best boundary separating students who got into university from those who did not with SVM. SVM seeks the maximum-margin separating surface between two classes and, via the kernel trick, can model nonlinear boundaries too. Accuracy is 100% — GPA, math, trial score and study separate the two groups perfectly (such high accuracy should be kept in mind regarding overfitting). SVM is especially powerful in small-to-medium, high-dimensional classification problems and is robust thanks to margin maximisation. It is a strong alternative to Random Forest.
⚠ 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.
▶ SVM (Support Vector Machine) — video walkthrough
This section is part of the Sınıflandırma video (7 analyses in one video). The link below jumps straight to 7:26, where this analysis begins. Narration is in Turkish.
🧪 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 →
- 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.