Diskriminant Analizi

Modern · Sınıflandırma

🆕 v1.0.5’te yeni: Bu analize Grafik sekmesi eklendi (analiz tipine göre boxplot, etkileşim, profil, çubuk, saçılım, biplot veya tahmin grafiği).

Discriminant Analysis is one of the statistical analyses automatically performed in MerQur. This page provides a representative illustration of how the analysis is applied to a sample data set from the field of Agriculture, Forestry and Aquaculture, how the MerQur output would appear, and how it would be reported in APA 7 format.

🎯 What is it for?

Discriminant Analysis automatically performs all necessary assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automatic interpretation in APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.

📌 When is it used?

In the statistical analysis of measurements belonging to the Agriculture, Forestry and Aquaculture field
To produce APA 7 compliant result tables for academic publication
In hypothesis testing and decision-making processes
After selecting an appropriate method in undergraduate / master’s / doctoral theses

📐 Assumptions

Appropriate scale — Variables must be at the scale level required by the analysis (nominal/ordinal/interval/ratio)
Independent observations — Observations must come from mutually independent individuals
Adequate sample — The minimum n requirement for the analysis must be met
Outlier check — Outliers must be detected and evaluated

If the assumptions are violated, MerQur automatically steers you toward a non-parametric or robust alternative.

🛠 How to do it in MerQur

Load the data. Select the sample file from the File → Open menu. MerQur automatically detects column types.

Select the analysis. Choose Discriminant Analysis from the left sidebar.

Panel assignments (form fields in the program):

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

Press the ▶ Run button. The results are automatically generated as a table + chart.

📄 Export to Word. A report in APA 7 format with italic statistical symbols.

📊 Sample Dataset — Ziraat, Orman ve Su Ürünleri

ℹ Note: The MerQur output and interpretation tables on this page are illustrative and intended to help understand the analysis. The numerical results for your own data may differ; this page does not have to match the YouTube video walkthrough exactly.

🎬 Example File Used in the Video

The YouTube video walkthrough of this page was recorded using the following sample file for the Agriculture, Forestry and Fisheries field:

MerQur_Hoca_Tanitim/Ziraat_Orman_Su/84_diskriminant_3tur_5ozellik.xlsx

Topic: The application of Discriminant Analysis on a real sample data set from the Agriculture, Forestry and Fisheries field.

Data Preview (First 5 Rows)

agac_id	DBH	biyokutle	kalite	yas	biyokimyasal	sinif
1	13.60	214	1	11	29.70	Genç
2	14.90	181	1	1	21.60	Genç
3	12.10	186	1	3	30.80	Genç
4	16.40	209	1	11	28.40	Genç
5	12.10	188	1	12	33.20	Genç

n = 105 · Columns: agac_id, DBH, biyokutle, kalite, yas, biyokimyasal, sinif

MerQur Output (Illustrative)

DISCRIMINANT ANALYSIS RESULT ───────────────────────────────────────────── Data: n = 105, 7 columns Test statistic: 12.43 p-value: < .001 Effect size: medium-to-large Detailed results are automatically generated with MerQur’s ▶ Run command.

MerQur Interpretation (Plain Language)

Discriminant Analysis revealed a statistically significant finding among the relevant variables (p < .001). Detailed results and visuals are automatically generated in MerQur’s output panel.

APA 7 Interpretation (Academic Format · Illustrative)

According to the Discriminant Analysis results, a statistically significant effect/relationship was observed on DBH (p < .001). The effect size is at a medium-to-large level, indicating that the findings carry practical significance. MerQur’s APA 7 Word output can be used for a detailed report.

⚠ Common Mistakes

Specifying the data type incorrectly (e.g., loading a categorical variable as numeric)
Skipping the assumption checks and looking directly at the p-value
Not reporting the effect size — APA 7 requires both p and the effect size
Not applying a Type I error correction (Bonferroni/Tukey) in multiple comparisons
Not switching to a non-parametric alternative when n is insufficient

📹 Video Walkthrough

You can watch the following video for an end-to-end application of this analysis using the Agriculture, Forestry and Fisheries file.

▶

The video will soon be uploaded to the MerQur YouTube channel

📺 MerQur YouTube Channel

🔀 The same analysis in other Core Fields:
🎓 Educational Sciences · 🧪 Natural Sciences and Mathematics · 🏛 Architecture, Planning and Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities and Administrative Sciences · 🏃 Sports Sciences

📚 If You Used This Analysis, Cite MerQur

If you performed this analysis in a scientific study using MerQur, please use the following citation (APA 7) in accordance with the academic citation requirement:

Örücü, Ö. K. (2026). MerQur: An Integrated Academic Data Analysis and Reporting Platform [Computer Software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001

For BibTeX, RIS, EndNote and the English equivalent: all citation formats →

References:

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.