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We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
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We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.
Steve Male
VP Innovation & Strategic Partnerships, The Logit Group
Explore Regional Offices
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A correlation coefficient is a measure of the strength of the relationship between two variables. Although there are many different types of correlation coefficients, the most commonly used is Pearson’s correlation coefficient (or Pearson’s R). Pearson’s correlation coefficient can be used to indicate the strength and direction of the linear relationship between two variables.
In today’s article, we will be specifically exploring the intraclass correlation coefficient to understand what it is, when it is used, and how it is calculated.
Conducting exploratory research seems tricky but an effective guide can help.
The intraclass correlation coefficient, or ICC, is a value that acts as a measure of the reliability of the ratings for clusters. Clusters refer to data that exists in groups. ICC is used to find correlations within a single class of data rather than two different classes of data.
As mentioned above, the most commonly used correlation coefficient is Pearson’s r. Pearson’s correlation coefficient is generally used for inter-rater reliability when there are only one or two meaningful pairs from one or two raters. However, if there are more pairs, the intraclass correlation coefficient should be used.
There are many different versions of ICC that can be calculated. The following factors influence which version is chosen:
There are three key models:
There are two key types of relationships that we are generally measuring when using ICC:
There are two key units that we are generally measuring when using ICC:
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Calculating the ICC for sets of data can be extremely complex. This is partly due to the fact that there are many different formulas that can be used for its calculation. Within this article we will go over a formula that is commonly used to calculate ICC.
Intraclass correlation coefficient is generally calculated as a ratio, using the following formula:
(variance of interest) / (total variance) = (variance of interest) / (variance of interest + unwanted variance)
The value of ICC can range from 0 to 1, which is different from Pearson’s correlation coefficient that can take a value between -1 to +1.
When the ICC value is below 0.5, it indicates that the unwanted variance of interest is equal to or larger than the variance of interest, reflecting that the reliability of the method is poor. When the ICC value is above 0.8, it can be inferred that the reliability of the method is good or excellent.
A correlation coefficient is a value that indicates the strength and direction of the relationship between two variables. There are many different kinds of correlation coefficient, the most common being Pearson’s R that is used in linear regression.
The intraclass correlation coefficient is a value that acts as a measure of the reliability of the ratings for clusters (data that exists in groups or is sorted into groups).
Pearson’s correlation coefficient is generally used for inter-rater reliability when there are only one or two meaningful pairs from one or two raters. However, if there are more pairs, the intraclass correlation coefficient should be used.
Intraclass correlation coefficient is generally calculated as a ratio, using the following formula:
(variance of interest) / (total variance) = (variance of interest) / (variance of interest + unwanted variance)
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