{"title":"求解卡方列联表偏倚的几种方法","authors":"Okeke Charles C.","doi":"10.32861/AJAMS.51.1.6","DOIUrl":null,"url":null,"abstract":"Some methods of solving the biasedness in Chi–square Contingency table statistic were considered. Phi Coefficient, Contingency Coefficient and Cramer’s V tools were employed to solve the biasedness in the use of Chi–square test. Our results show that any of Phi coefficient, Contingency coefficient or Cramer’s V can be used to describe the association between two variables if the data matrix is 2 x 2. Contingency Coefficient was recommended as a good statistic when the matrix dimension is the same while the Cramer’s V is most adequate when the data matrix differs.","PeriodicalId":375032,"journal":{"name":"Academic Journal of Applied Mathematical Sciences","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Alternative Methods of Solving Biasedness in Chi – Square Contingency Table\",\"authors\":\"Okeke Charles C.\",\"doi\":\"10.32861/AJAMS.51.1.6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Some methods of solving the biasedness in Chi–square Contingency table statistic were considered. Phi Coefficient, Contingency Coefficient and Cramer’s V tools were employed to solve the biasedness in the use of Chi–square test. Our results show that any of Phi coefficient, Contingency coefficient or Cramer’s V can be used to describe the association between two variables if the data matrix is 2 x 2. Contingency Coefficient was recommended as a good statistic when the matrix dimension is the same while the Cramer’s V is most adequate when the data matrix differs.\",\"PeriodicalId\":375032,\"journal\":{\"name\":\"Academic Journal of Applied Mathematical Sciences\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Journal of Applied Mathematical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32861/AJAMS.51.1.6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Journal of Applied Mathematical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32861/AJAMS.51.1.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
摘要
讨论了求解卡方列联表统计偏性的几种方法。在使用卡方检验时,采用Phi系数、权变系数和Cramer 's V工具解决偏倚问题。我们的结果表明,如果数据矩阵为2 x 2,则Phi系数、权变系数或克莱默V中的任何一个都可以用来描述两个变量之间的关联。当矩阵维数相同时,建议使用权变系数作为较好的统计量,而当数据矩阵不同时,Cramer 's V最合适。
Alternative Methods of Solving Biasedness in Chi – Square Contingency Table
Some methods of solving the biasedness in Chi–square Contingency table statistic were considered. Phi Coefficient, Contingency Coefficient and Cramer’s V tools were employed to solve the biasedness in the use of Chi–square test. Our results show that any of Phi coefficient, Contingency coefficient or Cramer’s V can be used to describe the association between two variables if the data matrix is 2 x 2. Contingency Coefficient was recommended as a good statistic when the matrix dimension is the same while the Cramer’s V is most adequate when the data matrix differs.