了解 Intra-class Correlation Corre
什么是intra-class coefficient correlation？翻译过来是组内相关系数。
为什么会需要它呢？
当我们的调查中有许多不同的采访者、评分者或评估者时，通常使用类内相关系数(ICC)。假设我们的调查中有n个参与者(或项目)，每个参与者由k个不同的面试官进行评估。我们感兴趣的是知道我们看到的k个面试官之间的一致程度(面试官是否为每个参与者记录了相同的结果)。ICC是我们的数据中总方差的比例，它由采访者之间的方差解释。在大多数情况下，ICC的值在0到1之间，当ICC接近1时，我们看到审查员之间有一个完美的一致，当ICC接近0时，我们看到审查员之间没有一致。
我觉得下边的这个例子解释的比较清楚。
Intraclass correlation measures the reliability of ratings or measurements for clusters — data that has been collected as groups or sorted into groups. A related term is interclass correlation, which is usually another name for Pearson correlation (other statistics can be used, like Cohen’s kappa, but this is rare). Pearson’s is usually used for inter-rater reliability when you only have one or two meaningful pairs from one or two raters. For more, you’ll want to use the ICC. Like most correlation coefficients, the ICC ranges from 0 to 1.
A high Intraclass Correlation Coefficient (ICC) close to 1 indicates high similarity between values from the same group.
A low ICC close to zero means that values from the same group are *not *similar.
This is best illustrated with an example. In the image below, values from the same group are clustered fairly tightly together. For example, group 3 (on the x-axis) is clustered between about -1.3 and -0.4 on the y-axis. Most of the groups are similarly clustered, giving the entire set a high ICC of 0.91:
A dotplot of a dataset with high intraclass correlation. Image: skbkekas|Wikimedia Commons. A dotplot of a dataset with high intraclass correlation. Image: skbkekas|Wikimedia Commons.
Compare that set to the following graph of a dataset with an extremely low ICC of 0.07. The values within groups are widely scattered without any clusters:
intraclass correlation Dataset with a low ICC. Image: Skbkekas|Wikimedia Commons.
Common Uses and Calculation
The ICC is used to measure a wide variety of numerical data from clusters or groups, including:
How closely relatives resemble each other with regard to a certain characteristic or traits.
Reproducibility of numerical measurements made by different people measuring the same thing.
Calculating the ICC is very complex by hand, in part because of the number of ICC formulas to choose from, and partly because the formulas themselves are complex. The main reason for all of this complexity is that the ICC is very flexible and can be adjusted for inconsistent raters for all ratees. For example, let’s say you have a group of 10 raters who rate 20 ratees. If 9 of the raters rate 15 of the ratees and 1 rater rates all of them, or if 10 raters rate 2 each, you can still calculate the ICC.
Calculating the ICC is usually performed with software, and each program has its own terminology and quirks. For example, in SPSS, you’re given three different options for calculating the ICC.
If you have inconsistent raters/ratees, use “One-Way Random.”
If you have consistent raters/ratees (e.g. 10 raters each rate 10 ratees), and you have sample data. use “Two-Way Random.”
If you have consistent raters/ratees (e.g. 10 raters each rate 10 ratees), and you have population data, use “Two-Way Random.”
参考链接：
https://www.statisticshowto.datasciencecentral.com/intraclass-correlation/
https://www.uvm.edu/~dhowell/StatPages/
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