相关性和一致性：这对相仿概念和测量方法的回顾与阐明

概述：一致性(agreement)和相关性(correlation)是两个广泛使用的概念，用来评估变量之间的关联。虽然二者相似且相关，但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构，而在变量测量完全不同的结构时也可以评估它们之间的相关性。这种概念上的差异就要求使用不同的统计方法，并且当评估一致性或相关性时，统计方法根据数据的分布和研究者的兴趣可能会有所不同。例如， Pearson相关性，作为评估连续变量之间相关性的一种普遍测量方法，只有用于符合线性关系的变量时才能提供有用的信息；当用于不符合线性关系的变量时就无法提供准确信息甚至会产生误导。同样地，内部相关性，作为一种评估连续变量之间一致性的常用方法，如果一致性不好的实质正好是研究兴趣所在，那么该测量就不能为研究者提供充分的信息。本报告回顾了一致性和相关性的概念，并讨论了几种常用方法在应用中的差异。

Correlaiton and agreement:overview and clariifcaiton of compeitng concepts and measures

Summary: Agreement and correlation are widely-used concepts that assess the association between variables. Although similar and related, they represent completely different notions of association. Assessing agreement between variables assumes that the variables measure the same construct, while correlation of variables can be assessed for variables that measure completely different constructs. This conceptual difference requires the use of different statistical methods, and when assessing agreement or correlation, the statistical method may vary depending on the distribution of the data and the interest of the investigator. For example, the Pearson correlation, a popular measure of correlation between continuous variables, is only informative when applied to variables that have linear relationships;it may be non-informative or even misleading when applied to variables that are not linearly related. Likewise, the intraclass correlation, a popular measure of agreement between continuous variables, may not provide sufficient information for investigators if the nature of poor agreement is of interest. This report reviews the concepts of agreement and correlation and discusses differences in the application of several commonly used measures.