Pairwise Comparisons Using Ranks in the One-Way Model

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  1. Ryan MartinSeptember 4th, 2020 at 06:56 pm

    Thanks for your contribution! I have just a few comments, questions, and suggestions in the attached report.

  2. Dennis BoosJanuary 24th, 2020 at 06:24 pm

    Further update on Jan. 24, 2020.

  3. Dennis BoosAugust 4th, 2019 at 01:06 am

    The current version was updated based on the comments by Ryan Martin

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Abstract

The Wilcoxon Rank Sum is a very competitive robust alternative to the two-sample t-test when the underlying data have tails longer than the normal distribution. Extending to the one-way model with k independent samples, the Kruskal-Wallis rank test is a competitive alternative to the usual F for testing if there are any location differences. However, these positives for rank methods do not extend as readily to methods for making all pairwise comparisons used to reveal where the differences in location may exist. We demonstrate via examples and simulation that rank methods can have a dramatic loss in power compared to the standard Tukey-Kramer method of normal linear models even for non-normal data. We also show that a well-established robust rank-like method can recover the power but does not fully control the familywise error rate in small samples.

Versions

➤  Version 1 (2019-05-27)

Citation

Dennis Boos and Siyu Duan (2019). Pairwise Comparisons Using Ranks in the One-Way Model. Researchers.One, https://researchers.one/articles/pairwise-comparisons-using-ranks-in-the-one-way-model/5f52699c36a3e45f17ae7dd4/v1.

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