1. What is ICC?

The Intraclass Correlation Coefficient (ICC) is a measure of reliability or agreement among raters, measurements, or instruments. It quantifies how strongly units in the same group resemble each other.

2. How Does the Calculator Work?

The calculator uses the ICC formula:

Where:

• \( MS_R \) — Mean square between rows (subjects)
• \( MS_E \) — Mean square error
• \( MS_C \) — Mean square between columns (raters)
• \( k \) — Number of raters
• \( n \) — Number of subjects

3. Interpretation of ICC Values

Guidelines:

• ICC < 0.5: Poor reliability
• 0.5 ≤ ICC < 0.75: Moderate reliability
• 0.75 ≤ ICC < 0.9: Good reliability
• ICC ≥ 0.9: Excellent reliability

4. Using the Calculator

Instructions: Enter the mean square values from your ANOVA table, number of raters, and number of subjects. All values must be positive numbers.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between ICC and Pearson correlation?
A: ICC assesses agreement (absolute consistency), while Pearson correlation assesses linear relationship (relative consistency).

Q2: Which ICC model should I use?
A: This calculator implements ICC(2,1). Choose different models based on your experimental design.

Q3: Can ICC be negative?
A: Yes, but negative values are usually treated as 0, indicating no agreement.

Q4: How many raters/subjects do I need?
A: Typically 2-5 raters and at least 30 subjects for stable estimates.

Q5: What are alternatives to ICC?
A: Cohen's kappa (for categorical data), Bland-Altman analysis, or Krippendorff's alpha.