文档介绍:内容
Spearman相关系数的含义
Spearman相关分析的特点
Spearman 与Pearson相关分析的对比
结果的解释
对我的研究数据的适用性
*
spearman和person相关分析对比
*
一
Spearman相关系数的含义
Spearman rank-order correlation coefficient
两个连续或等级/秩变量之间的单调相关( monotone association)关系。
单调函数描述两个变量之间的关系的程度( how well the relationship between two variables can be described using a monotonic function)
Pearson 相关系数的一个特例(系数计算中的X,Y被其秩代替):
Rx and Ry are the ranks of the x and y variables,respectively
is the square of the difference between the corresponding ranks of xi and yi, and n is the number observations.
*
spearman和person相关分析对比
*
二
Spearman相关分析的特点
不对变量的分布做假设:非参数(自由分布)
可用于等级数据( ordinal data:The most fundamental requisite is to be able to measure our observed correspondence by a plain numerical symbol )
当数据分布导致,pearson相关系数不适用或者产生误导的使用。when the dis­tribution of data makes Pearson’s correlation co­efficient undesirable or misleading
当数据为定性的变量,或者含有异常值时尤其适用。
*
spearman和person相关分析对比
*
三
Spearman 与Pearson相关分析的对比
*
spearman和person相关分析对比
*
三
Spearman 与Pearson相关分析的对比
1. 对同样的数据,Spearman相关统计相关,而pearson可能显著也可能不显著
the significance of Spearman’s correlation can lead to the significance or non-significance of Pearson’s correlation coefficient even for big sets of data
,二者所得的相关系数符号有可能不一样
(It is possible to meet a situation where Pearson’s coefficient is negative while Spearman’s coefficient is positive.)
:确保不要针对两个变量的相关强度,对spearman相关系数过度解释
Make sure not to overinterpret Spearman’s rank cor­relation coefficient as a significant measure of the strength of the associations between two variables.
*
spearman和person相关分析对比
*
四
结果的解释
Effect Size(Biological relevance/correlation coefficient/r )
Statistical significance (P)
两个变量一起变化的强度和方向
Extent to which two variables tend to change together(strength and direction)
Statistical power
*
spearman和person相关分析对比
*
四
结果的解释
r 与P值
呈现分析结果时应当包含的信息
当统计显著,但相关系数不大,则有可能是其他变量的影响
*
spea