ICC (Intraclass Correlation)
ICC (Intraclass Correlation) 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?
ICC (Intraclass Correlation) 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 ICC (Intraclass Correlation).
Panel assignments (form fields in the program):
- columns:
['ogrenci_id', 'ogretmen_1', 'ogretmen_2', 'ogretmen_3']
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/76_icc_3ogretmen_composition.xlsx
🎬 Scenario
Three teachers independently scored the same 50 student compositions, recorded as teacher_1, teacher_2, and teacher_3. Before we trust these ratings, we need to know how consistent the teachers are with one another. Intraclass Correlation is the right statistic because it quantifies the degree of agreement among multiple raters scoring the same subjects on a continuous scale, telling us what proportion of the score variance reflects true differences between compositions rather than rater disagreement.
⚙️ Variable Selection
- Raters / Measurements: teacher_1
- Raters / Measurements: teacher_2
- Raters / Measurements: teacher_3
Data Preview (First 5 Rows)
| student_id | teacher_1 | teacher_2 | teacher_3 |
|---|---|---|---|
| 1.0 | 70.0 | 69.3 | 66.4 |
| 2.0 | 71.7 | 73.2 | 74.6 |
| 3.0 | 88.6 | 84.5 | 91.2 |
| 4.0 | 92.8 | 99.2 | 100.0 |
| 5.0 | 83.3 | 85.8 | 86.8 |
n = 50 · Columns: student_id, teacher_1, teacher_2, teacher_3
📈 MerQur Output
💬 Interpretation
We assessed how consistent three teachers are in scoring the same compositions/assignments with ICC. ICC = 0.89 is “excellent”: inter-teacher scoring agreement is very high — whichever teacher scores, the result is similar. This is critical in educational assessment: if scoring varied from teacher to teacher, student grades would be unfair. ICC is the standard for measuring inter-rater reliability on continuous measurements (scores, ratings) (Cohen’s Kappa is for categorical data, ICC for continuous). It is essential for rubric validity and grading consistency.
⚠ 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.
▶ ICC (Intraclass Correlation) — video walkthrough
This section is part of the Anket Analizleri video (5 analyses in one video). The link below jumps straight to 5:22, where this analysis begins. Narration is in Turkish.
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📚 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.