Discriminant Analysis

Modern · Sınıflandırma

🆕 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).

Discriminant Analysis is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design 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?

Discriminant Analysis automatically performs all required 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 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 Architecture, Planning & Design 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 Discriminant Analysis.

Panel assignments (form fields in the program):

Target Column: park_tipi
Prediktorler: ['alan_m2', 'yesil_orani', 'agac_yogunluk', 'kiyi_yakinlik', 'yol_uzunluk_m']
Yontem: lda
CV kati: 5

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 — Architecture, Planning & Design

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 Architecture, Planning & Design:

Peyzaj_Mimarligi/84_diskriminant_3park_5feat.xlsx

🎬 Scenario

We find the linear combinations best separating three park types and classify new parks; discriminant analysis is appropriate.

⚙️ Variable Selection

Target: park_type | Features: area_m2, green_ratio, tree_density, coast_proximity, path_length_m

Data Preview (First 5 Rows)

park_id	area_m2	green_ratio	tree_density	coast_proximity	path_length_m	park_type
1	1647	0.315	47	0.191	117	neighborhood
2	1486	0.342	15	0.114	50	neighborhood
3	2161	0.286	38	0.244	50	neighborhood
4	1787	0.391	22	0.261	50	neighborhood
5	1938	0.339	36	0.089	50	neighborhood

n = 105 · Columns: park_id, area_m2, green_ratio, tree_density, coast_proximity, path_length_m, park_type

📈 MerQur Output

DISCRIMINANT ANALYSIS RESULT ───────────────────────────────────────────── Accuracy = 1.000 (3 park types) Separation from 5 features (area, green ratio, tree density, coast proximity, path length)

💬 Interpretation

Discriminant analysis finds the linear combinations of variables that best separate the groups (three park types) — a bit like a classification-focused PCA. Accuracy is 100%: five features separate the three park types perfectly. Discriminant analysis both classifies and answers “which feature matters most for the separation” (discriminant function loadings). It resembles logistic regression but is the classic choice for multiple groups when variables are normally distributed. In landscape it is used in park-type classification and assigning new parks to an existing typology; it provides a basis for planning standards and type-based management.

⚠ 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 Architecture, Planning & Design file.

▶ Discriminant Analysis — video walkthrough

This section is part of the Sınıflandırma video (7 analyses in one video). The link below jumps straight to 10:19, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (10:19) 📺 All videos

🔀 The same analysis in other disciplines:
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ 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.