GLMM
GLMM is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design 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?
GLMM 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 Architecture, Planning & Design 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 GLMM.
Panel assignments (form fields in the program):
- Bagimli degisken (DV):
bocek_sayisi - Bagimsiz:
['zaman', 'bakim_var'] - Grup degiskeni:
saha_id - Family:
poisson
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 — Architecture, Planning & Design
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 Architecture, Planning & Design:
Peyzaj_Mimarligi/56_glmm_site_time_insect.xlsx
🎬 Scenario
We model insect counts (Poisson) with time/maintenance fixed effects and a site random effect; a mixed model + count distribution (GLMM) is appropriate.
⚙️ Variable Selection
- Dependent (count): insect_count
- Fixed effects: time, maintenance_present
- Random effect (group): site_id | Family: poisson
Data Preview (First 5 Rows)
| site_id | time | maintenance_present | insect_count |
|---|---|---|---|
| 1 | 1 | 1 | 4 |
| 1 | 2 | 1 | 6 |
| 1 | 3 | 1 | 8 |
| 1 | 4 | 1 | 10 |
| 2 | 1 | 0 | 3 |
n = 160 · Columns: site_id, time, maintenance_present, insect_count
📈 MerQur Output
💬 Interpretation
We modelled insect/pest counts (Poisson) with both time/maintenance fixed effects and a site random effect — a GLMM: mixed model + count distribution. The maintenance effect is significant and negative (coef -0.46, p < .001): maintained sites have markedly fewer insects — evidence of regular maintenance’s role in pest control. GLMM combines the strengths of LMM (which assumes normality) and GLM (which ignores site clustering) for “repeated/ nested count data”. It is the correct analytic framework for landscape-health monitoring data like insect/pest counts nested within sites.
⚠ 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 Architecture, Planning & Design file.
▶ GLMM — video walkthrough
This section is part of the Regresyon video (12 analyses in one video). The link below jumps straight to 20:33, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ 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.