Cox Proportional-Hazards Regression
Cox Proportional-Hazards Regression 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?
Cox Proportional-Hazards Regression 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 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 Cox Proportional-Hazards Regression.
Panel assignments (form fields in the program):
- duration_col:
sure_donem - event_col:
burs - Covariates:
['yas', 'GPA', 'aile_destek', 'terk']
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/87_cox_dropout_hazard.xlsx
🎬 Scenario
Here we want to know which factors raise or lower a student’s risk of dropping out over time. For 250 students we have duration_term as the follow-up time and dropout as the event, along with covariates: age, GPA, whether they hold a scholarship, and the level of family_support. We use Cox proportional hazards regression because it models the instantaneous risk of dropout as a function of these predictors without assuming a particular baseline shape, telling us, for example, how much a scholarship reduces the hazard of leaving school.
⚙️ Variable Selection
- Time variable: duration_term
- Event indicator (1=dropout): dropout
- Covariates: age, GPA, scholarship, family_support
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Proportional-hazards assumption test (Schoenfeld): per-covariate chi-square/p; significant = PH violated, consider a time-varying effect.
Data Preview (First 5 Rows)
| student_id | age | GPA | scholarship | family_support | duration_term | dropout |
|---|---|---|---|---|---|---|
| 1.0 | 24.0 | 2.29 | 0.0 | 2.99 | 13.8 | 0.0 |
| 2.0 | 24.0 | 2.62 | 0.0 | 3.04 | 16.0 | 0.0 |
| 3.0 | 21.0 | 2.28 | 0.0 | 2.01 | 16.0 | 0.0 |
| 4.0 | 19.0 | 2.1 | 1.0 | 4.03 | 16.0 | 0.0 |
| 5.0 | 21.0 | 3.04 | 0.0 | 4.27 | 16.0 | 0.0 |
n = 250 · Columns: student_id, age, GPA, scholarship, family_support, duration_term, dropout
📈 MerQur Output
💬 Interpretation
We modelled a student’s dropout risk from academic/demographic variables with Cox regression. GPA is significant and protective (HR = 0.58 < 1, p = 0.006): a one-unit rise in GPA roughly halves the dropout risk — high achievement strongly encourages staying in school. Cox regression is the gold-standard survival model relating “time-to-event” to several predictors; it gives hazard ratios (HR). Anticipating dropout and identifying risk factors — for early intervention — is very valuable in education. HR < 1 is protective, HR > 1 risk-increasing. >> ADVANCED PARAMETERS (optional in the form — what they do): – Proportional-hazards assumption test (Schoenfeld): per-covariate chi-square/p; significant = PH violated, consider a time-varying effect.
⚠ 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.
▶ Cox Proportional-Hazards Regression — video walkthrough
This section is part of the Sağkalım Analizi video (3 analyses in one video). The link below jumps straight to 1:50, 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.