Survey Logistic Regression
Survey Logistic 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?
Survey Logistic 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 Survey Logistic Regression.
Panel assignments (form fields in the program):
- weight_col:
agirlik - y_col:
uni_gecti - x_cols:
['ogrenci_id', 'GPA', 'SES'] - stratum_col:
bolge - cluster_col:
None
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/82_survey_logistic_university.xlsx
🎬 Scenario
Here we want to understand which students pass the university entrance exam, again from a weighted national sample. For each of 320 students we record their GPA, the sound level of their study environment, the region they come from, either Marmara or east, and a binary indicator of whether they passed, university_passed. Since the outcome is yes or no and the sample is drawn with unequal selection probabilities, we use survey-weighted logistic regression. This lets us estimate how GPA and the other predictors change the population-level odds of passing the exam while respecting the sampling weights.
⚙️ Variable Selection
- Dependent variable (binary): university_passed
- Predictors: GPA, sound, region (Marmara/east)
- Weight: weight
Data Preview (First 5 Rows)
| student_id | GPA | sound | region | university_passed | weight |
|---|---|---|---|---|---|
| 1 | 1.53 | 3 | Marmara | 0 | 1.2 |
| 2 | 2.73 | 1 | Marmara | 0 | 1.2 |
| 3 | 3.16 | 1 | Marmara | 0 | 1.2 |
| 4 | 2.26 | 1 | Marmara | 0 | 1.2 |
| 5 | 3.48 | 1 | Marmara | 0 | 1.2 |
n = 320 · Columns: student_id, GPA, sound, region, university_passed, weight
📈 MerQur Output
💬 Interpretation
Predicting a student’s probability of university admission from GPA, we handled both the binary outcome (logistic) and the complex sample design together. The odds ratio is 5.68 (p < .001): a one-unit rise in GPA multiplies the odds of admission 5.7-fold — a very strong predictor. Because the design weights and stratification are accounted for, the OR and confidence interval are design-consistent and valid. Survey logistic regression is the correct way to model binary outcomes (admitted/not, graduated/not) from complex educational surveys.
⚠ 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 Logistic Regression — video walkthrough
This section is part of the Karmaşık Anket Tasarımı video (3 analyses in one video). The link below jumps straight to 3:44, 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.