The Impact of non‑Equidistance on Anova and Alternative Methods

Authors

  • Bjorn Lantz

Keywords:

Keywords: Likert-type scale, equidistance, Monte Carlo simulation, ANOVA

Abstract

Abstract: The normality assumption behind ANOVA and other parametric methods implies not only mound shape, symmetry, and zero excess kurtosis, but also that data are equidistant. This paper uses a simulation approach to explore the impact of non‑equidistance on the performance of statistical methods commonly used to compare locations across several groups. These include the one‑way ANOVA and its robust alternatives, the Brown‑Forsythe test, and the Welch test. We show that non‑equidistance does affect these methods with respect to both significance level and power, but the impact differs between the methods. In general, the ANOVA is less sensitive to non‑equidistance than the other two methods are and should therefore be the primary choice when analyzing potentially non‑equidistant data.

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Published

1 Jul 2014

Issue

Vol. 12 No. 1 (2014)

Section

Articles

License

Open Access Publishing

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