Using Johnson's transformation and robust estimators with heteroscedastic test statistics: An examination of the effects of non-normality and heterogeneity in the non-orthogonal two-way ANOVA design

Luh Wei-Ming, Guo Jiin-Huarng

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The present study proposes a procedure that combines Johnson's transformation and the trimmed means method to deal with the problem of non-normality. An approximate test such as the Alexander-Govern test or Welch-James type test is then employed to deal with the heterogeneity of cell variance in the non-orthogonal two-way fixed effects completely randomized design. Both unweighted and weighted means analyses are considered. The empirical Type I error rates and the statistical power for comparing population means are investigated by Monte Carlo simulation. The simulated results show that Johnson's transformation with trimmed mean and the approximate test is valid in terms of Type I error rate control, and that the magnitude of the statistical power for non-normal distributions is better than that of conventional methods.

Original languageEnglish
Pages (from-to)79-94
Number of pages16
JournalBritish Journal of Mathematical and Statistical Psychology
Volume54
Issue number1
DOIs
Publication statusPublished - 2001 Jan 1

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Using Johnson's transformation and robust estimators with heteroscedastic test statistics: An examination of the effects of non-normality and heterogeneity in the non-orthogonal two-way ANOVA design'. Together they form a unique fingerprint.

  • Cite this