Conditional Logit
Conditional Logit 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?
Conditional Logit 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 Conditional Logit.
Panel assignments (form fields in the program):
- chooser_col:
ogrenci_id - chosen_col:
secildi - predictors:
['yillik_ucret', 'mesafe_km'] - alt_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/85_conditional_logit_university.xlsx
🎬 Scenario
Here we study how students choose among universities. In a discrete choice experiment, each of our students faces a set of four alternatives, U1-public-near, U2-public-far, U3-private-near, and U4-private-far, and picks one. Every alternative is described by its annual_fee and its distance_km, and chosen marks which one was actually selected. Because the choice is among competing options whose attributes vary, an ordinary logistic model will not do. We use a Conditional Logit choice model, which estimates how fee and distance drive the probability that an alternative is chosen, with choice_id grouping the alternatives that belong to the same decision.
⚙️ Variable Selection
- Choice indicator (event chosen): chosen
- Choice set / group id: choice_id
- Alternative-specific attributes: annual_fee, distance_km
- Alternative label: university (U1-public-near/U2-public-far/U3-private-near/U4-private-far)
Data Preview (First 5 Rows)
| student_id | choice_id | university | annual_fee | distance_km | chosen |
|---|---|---|---|---|---|
| 1 | 1 | U1-public-near | 2000 | 15 | 1 |
| 1 | 2 | U2-public-far | 2000 | 300 | 0 |
| 1 | 3 | U3-private-near | 40000 | 20 | 0 |
| 1 | 4 | U4-private-far | 50000 | 350 | 0 |
| 2 | 5 | U1-public-near | 2000 | 15 | 1 |
n = 480 · Columns: student_id, choice_id, university, annual_fee, distance_km, chosen
📈 MerQur Output
💬 Interpretation
We examined which university students choose with a conditional logit model — each student selects one from a choice set. McFadden pseudo R2 = 0.97 is very high (0.2-0.4 is already strong for choice models): fee and distance explain the choice almost completely. The model shows students strongly prefer nearby, lower-fee universities. This method underlies “discrete choice” analysis in transport, marketing and education economics (school/ university choice, demand modelling); it is powerful for modelling access and cost effects in education policy.
⚠ 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.
▶ Conditional Logit — video walkthrough
This analysis is demonstrated on a sample dataset from Anaesthesiology (the steps are identical across disciplines). The link jumps straight to 13:08. 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.