A heteroscedastic, rank-based approach for analyzing 2 × 2 independent groups designs

Laura Mills, Robert A. Cribbie, Wei Ming Luh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The ANOVA F is a widely used statistic in psychological research despite its shortcomings when the assumptions of normality and variance heterogeneity are violated. A Monte Carlo investigation compared Type I error and power rates of the ANOVA F, Alexander-Govern with trimmed means and Johnson transformation, Welch-James with trimmed means and Johnson Transformation, Welch with trimmed means, and Welch on ranked data using Johansen's interaction procedure. Results suggest that the ANOVA F is not appropriate when assumptions of normality and variance homogeneity are violated, and that the Welch/Johansen on ranks offers the best balance of empirical Type I error control and statistical power under these conditions.

Original languageEnglish
Pages (from-to)322-336
Number of pages15
JournalJournal of Modern Applied Statistical Methods
Volume8
Issue number1
DOIs
Publication statusPublished - 2009 May

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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