ICC Intraclass Correlation Calculator


In the realm of statistics, the Intraclass Correlation Coefficient (ICC) serves as a vital metric, especially in assessing the reliability and consistency of measurements taken by different observers or instruments. This article explores the significance of ICC, providing a user-friendly calculator to compute it efficiently.

How to Use

To utilize the ICC calculator, simply input the required values into the designated fields and click the “Calculate” button. The calculator will then process the data and yield the corresponding ICC value.


The ICC is determined by the ratio of between-group variance to total variance. Mathematically, it can be expressed as:


  • BMS represents the between-group mean square.
  • EMS denotes the error mean square.
  • K signifies the number of groups.

Example Solve

Consider a scenario where we have three different raters evaluating the same set of subjects for a research study. Let’s say their measurements yield the following data:

  • Between-group mean square (BMS) = 15
  • Error mean square (EMS) = 5
  • Number of groups (K) = 3

Plugging these values into the formula:


Hence, the ICC value for this scenario is 0.4.


Q: What is the significance of ICC in statistics?

A: ICC helps assess the consistency and reliability of measurements, crucial for ensuring the validity of research findings.

Q: How many raters or observers are required to compute ICC?

A: Ideally, ICC requires at least two raters or observers, but it can accommodate more depending on the study design.

Q: Can ICC values be negative?

A: No, ICC values range from 0 to 1, where 0 indicates no agreement and 1 signifies perfect agreement.


The ICC calculator simplifies the computation of this essential statistic, enabling researchers to evaluate the reliability of their data effectively. By understanding and utilizing ICC, researchers can enhance the quality and credibility of their research outcomes.

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