GEE (Generalized Estimating Equations)

Karma · Tekrarlı

GEE (Generalized Estimating Equations) is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Agriculture, Forestry & Aquatic 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?

GEE (Generalized Estimating Equations) automatically performs, 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 plus chart. Automatic interpretation in APA 7 style, 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 Agriculture, Forestry & Aquatic 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 GEE (Generalized Estimating Equations).

Panel assignments (form fields in the program):

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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 — Agriculture, Forestry & Aquatic

ℹ Note: The MerQur output and interpretation tables on this page are illustrative, intended to help readers understand the analysis. Numeric results from your own data may differ; this page does not need to match the YouTube video walkthrough exactly.

🎬 File Used in the Video

The YouTube video for this page was recorded for the Agriculture, Forestry & Aquatic domain using the sample file below:

MerQur_Hoca_Tanitim/Ziraat_Orman_Su/55_gee_panel_saglik_tedavi.xlsx

Topic: GEE (Generalized Estimating Equations) analysis applied to a real Agriculture, Forestry & Aquatic sample dataset.

Data Preview (First 5 Rows)

parsel_id	vizit	ilac	verim
1	1	Yeni	230.40
1	2	Yeni	229.50
1	3	Yeni	249.50
1	4	Yeni	246.80
1	5	Yeni	251.40

n = 300 · Columns: parsel_id, vizit, ilac, verim

MerQur Output (Illustrative)

REGRESSION MODEL ───────────────────────────────────────────── Dependent: verim Predictors: parsel_id, vizit Model: F(3, 296) = 47.62, p < .001 R² = 0.524 Adj R² = 0.512 Coefficients: Constant: 12.43 (SE=2.18, p<.001) X1: 0.418 (SE=0.094, β=.32, p<.001) X2: -0.215 (SE=0.087, β=-.18, p=.014)

MerQur Narrative Interpretation

The multiple regression model showed that approximately 52% of the variability in verim is explained by the predictor variables (R² = .52, p < .001). The model is significant overall and fits the data well.

APA 7 Interpretation (Academic Format · Illustrative)

The multiple linear regression analysis showed that the overall model was statistically significant in predicting the verim variable, F(3, 296) = 47.62, p < .001, R² = .52, adjusted R² = .51. This result supports that the predictor variables in the model explain approximately 52% of the variance in verim.

⚠ 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 Agriculture, Forestry & Aquatic file.

▶

The video will be uploaded to the MerQur YouTube channel soon

📺 MerQur YouTube Channel

🔀 The same analysis in other disciplines:
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences

📚 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 →

Sources:

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.