如何正确运用χ^(2)检验——三种R×C列联表资料的CMH χ^(2)检验  

How to useχ^(2) test correctly——CMHχ^(2) tests for the data collected from the three kinds of R×C contingency tables

作　　者：胡纯严 胡良平 Hu Chunyan;Hu Liangping(Graduate School,Academy of Military Sciences PLA China,Beijing 100850,China;Specialty Committee of Clinical Scientific Research Statistics of World Federation of Chinese Medicine Societies,Beijing 100029,China)

机构地区：[1]军事科学院研究生院,北京100850 [2]世界中医药学会联合会临床科研统计学专业委员会,北京100029

出　　处：《四川精神卫生》2021年第2期121-125,共5页Sichuan Mental Health

摘　　要：本文目的是介绍三种R×C列联表资料的CMH χ^(2)检验以及SAS软件实现的方法。第一种是"双向无序R×C列联表资料",与这种资料对应的CMH χ^(2)检验在本质上就是Pearson’s χ^(2)检验;第二种是"结果变量为有序变量的R×C列联表资料",与这种资料对应的CMH χ^(2)检验在本质上就是秩和检验;第三种是"双向有序且属性不同R×C列联表资料",与这种资料对应的CMH χ^(2)检验在本质上就是Pearson’s相关分析或Spearman’s秩相关分析。当R×C列联表资料中有1个或2个"有序变量"时,进行统计分析之前,需要给有序变量"赋值或评分"。在SAS/STAT的FREQ过程中,有四种评分方法。评分方法不同,CMH χ^(2)检验统计量的表达形式和计算结果都会发生相应变化。The purpose of this paper was to introduce the CMHχ^(2) test and SAS software implementation of the three kinds of R×C contingency table data.The first type was called"two-way unordered R×C contingency table data".The CMHχ^(2) test corresponding to this type of data was essentially the Pearson’sχ^(2) test.The second type was called"R×C contingency table data with an ordinal outcome variable".The CMHχ^(2) test corresponding to this kind of data was essentially a rank sum test.The third type was called"R×C contingency table data which was of two ordinal variables with different attributes".The CMHχ^(2) test corresponding to the data was essentially Pearson’s correlation analysis or Spearman’s rank correlation analysis.When there were 1 or 2"ordinal variables"in the R×C contingency table data,it was necessary to"assign or score"the ordinal variables before performing statistical analysis.In the FREQ procedure of SAS/STAT,there were four scoring methods.With different scoring approach,both the expression form and the calculation results of CMHχ^(2) test statistics could change accordingly.

关 键 词：有序变量 CMHχ^(2)检验 秩和检验 秩相关分析 SAS软件 R软件 

分 类 号：R195.1[医药卫生—卫生统计学]