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  • 标题:ANOVA_robust: A SAS Macro for Parametric Tests of Mean Differences in One-Factor ANOVA Models
  • 本地全文:下载
  • 作者:Thanh V. Pham ; Jeffrey D. Kromrey ; Yi-Hsin Chen
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2020
  • 卷号:95
  • 期号:1
  • 页码:1-16
  • DOI:10.18637/jss.v095.c02
  • 出版社:University of California, Los Angeles
  • 摘要:Testing the equality of several independent group means is a common statistical practice in the social sciences. The traditional analysis of variance (ANOVA) is one of the most popular methods. However, the ANOVA F test is sensitive to violations of the homogeneity of variance assumption. Many alternative tests have been developed in response to this problem of the F test. These tests include some modifications of the ANOVA F test and others based on the structured means modeling technique. This paper provides a SAS macro for testing the equality of group means using thirteen methods including the regular ANOVA F test. In addition, this paper summarizes the results of a simulation study that compares the performance of these tests in terms of their Type I error rate under different conditions, especially under violations of the homogeneity of variance assumption.
  • 关键词:analysis of variance;homogeneity of variance assumption;simulation study;homoscedasticity;heteroscedasticity;SAS macro.
  • 其他关键词:analysis of variance;homogeneity of variance assumption;simulation study;homoscedasticity;heteroscedasticity;SAS macro
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