ROC Analysis

Sınıflandırma · Eşik

ROC Analysis 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?

ROC Analysis 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 ROC Analysis.

Panel assignments (form fields in the program):

Target Column: iyi_kalite
Predictors: ['kalite_skoru']

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/60_roc_quality.xlsx

🎬 Scenario

We assess how well a quality score separates a binary outcome with ROC. For discrimination and threshold selection, ROC is appropriate.

⚙️ Variable Selection

Dependent variable: good_quality
Predictor(s): quality_score

Data Preview (First 5 Rows)

plot_id	quality_score	good_quality
1.0	73.6	1.0
2.0	60.2	0.0
3.0	40.0	0.0
4.0	88.7	1.0
5.0	57.8	0.0

n = 250 · Columns: plot_id, quality_score, good_quality

📈 MerQur Output

ROC ANALYSIS RESULT ───────────────────────────────────────────── AUC = 0.970 (excellent discrimination) Youden optimum threshold = 0.361 Sensitivity = 0.964, 1-Specificity = 0.100 n = 250 (positive 110, negative 140)

💬 Interpretation

We assessed how well a quality score separates a binary outcome (good_quality) with a ROC curve: AUC = 0.97, excellent discrimination; the optimum decision threshold was set at 0.36 via Youden. ROC shows the sensitivity-specificity trade-off at all possible thresholds and evaluates the model without being tied to a single threshold. In the field it is the standard tool for measuring a quality/class model’s discriminative power and selecting the best decision threshold.

⚠ 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.

▶ ROC Analysis — video walkthrough

This section is part of the Sınıflandırma video (7 analyses in one video). The link below jumps straight to 0:00, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (0:00) 📺 All videos

🔀 The same analysis in other disciplines:
🎓 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 →

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.