Survey-PHREG
Survey-PHREG 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?
Survey-PHREG 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 Survey-PHREG.
Panel assignments (form fields in the program):
- duration_col:
sure_donem - event_col:
None - feature_cols:
['terk', 'yas'] - weight_col:
agirlik - cluster_col:
None - strata_col:
bolge
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/91_survey_phreg_dropout.xlsx
🎬 Scenario
Suppose we are studying why some students drop out of university and we want our hazard model to reflect the national student population, not just our raw sample. We followed 300 students over time, recording how many terms they stayed enrolled in duration_term and whether they eventually dropped out, coded by dropout. We also know each student’s region (A, B, or C), their age, and the school they attended. Because the data came from a complex sampling design, every student carries a survey weight, so an ordinary Cox model would give biased estimates. We use Survey-Weighted Cox Regression so the proportional-hazards estimates for dropout properly account for these design weights and generalize to the wider population.
⚙️ Variable Selection
- Time (duration): duration_term
- Event: dropout (1=dropped out)
- Weight: weight
- Predictors (covariates): age, region (A/B/C)
Data Preview (First 5 Rows)
| student_id | duration_term | dropout | region | school_id | weight | age |
|---|---|---|---|---|---|---|
| 1 | 14.0 | 0 | B | 10 | 2.467 | 19 |
| 2 | 14.0 | 0 | C | 8 | 1.066 | 17 |
| 3 | 7.2 | 0 | B | 10 | 1.769 | 17 |
| 4 | 3.4 | 0 | C | 10 | 1.032 | 18 |
| 5 | 14.0 | 1 | B | 2 | 1.628 | 19 |
n = 300 · Columns: student_id, duration_term, dropout, region, school_id, weight, age
📈 MerQur Output
💬 Interpretation
We combined survival analysis with a complex sample design: students come from a population sampled with weights and clustered by schools. Survey-PHREG runs the Cox model with design weights and cluster-robust standard errors, so the hazard ratios and confidence intervals generalise to the population. Concordance 0.53 means age alone is a weak discriminator in this model. When national education surveys have “time-to-event” data, ignoring the design biases the estimates; survey-PHREG provides valid inference.
⚠ 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.
▶ Survey-PHREG — video walkthrough
This analysis is demonstrated on a sample dataset from Anaesthesiology (the steps are identical across disciplines). The link jumps straight to 6:39. 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.