A powerful transformation trimmed mean method for one-way fixed effects ANOVA model under non-normality and inequality of variances

Wei-Ming Luh, Jiin Huarng Guo

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

The present study proposes a transformation trimmed mean method for the one-way fixed effects ANOVA model to deal with non-normality and inequality of variances. First, the data are trimmed on both sides. Then the test statistic is transformed by Johnson's method for each group to deal with non-normality. Finally, an approximate test such as the Alexander-Govern test, the James second-order test or the Welch test is applied to test the equality of population trimmed means. Monte Carlo simulation results show that, under non-normal cases, the proposed method can control Type I error rates, and has greater power than other competitors, such as the original Alexander-Govern method, the Alexander-Govern trimmed mean method, the James second-order trimmed mean method, the Welch trimmed mean method, as well as the corresponding bootstrapping methods. The Johnson's transformation trimmed mean method can be programmed easily and is highly recommended as an alternative for the one-way fixed effects ANOVA model if normality or equality of variances cannot be assumed.

Original languageEnglish
Pages (from-to)303-320
Number of pages18
JournalBritish Journal of Mathematical and Statistical Psychology
Volume52
Issue number2
DOIs
Publication statusPublished - 1999 Jan 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • Psychology(all)

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