2015
DOI: 10.1007/s00362-015-0690-2
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A comparison of different synchronized permutation approaches to testing effects in two-level two-factor unbalanced ANOVA designs

Abstract: Analysis of variance (ANOVA) is used to compare the means of various samples. Parametric ANOVA approaches assume normally distributed error terms within subsamples. Permutation tests like synchronized permutation tests are computationally intensive and distribution free procedures. Hence they overcome the limitationsofparametricmethods.Unbalanceddesignswithdifferingsubsamplesizes are quite frequent in various disciplines. There is a broad literature about unbalanced designs and parametric testing. For permutat… Show more

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Cited by 13 publications
(3 citation statements)
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“…However, the parametric ANOVA test is subjected to certain assumptions, i.e., approximately normal distribution of the dependent variable for each category, or homogeneity of variances. Permutation tests offer an alternative to parametric approaches when sample sizes are low and error terms do not fulfil distributional assumptions of the parametric approach [ 7 ]. These are computational resampling-based tests, which permute data falling under the null hypothesis of equal data distributions between groups [ 8 ], overcoming the limitations of parametric methods, in a simple and comprehensive way.…”
Section: Introductionmentioning
confidence: 99%
“…However, the parametric ANOVA test is subjected to certain assumptions, i.e., approximately normal distribution of the dependent variable for each category, or homogeneity of variances. Permutation tests offer an alternative to parametric approaches when sample sizes are low and error terms do not fulfil distributional assumptions of the parametric approach [ 7 ]. These are computational resampling-based tests, which permute data falling under the null hypothesis of equal data distributions between groups [ 8 ], overcoming the limitations of parametric methods, in a simple and comprehensive way.…”
Section: Introductionmentioning
confidence: 99%
“…Among these are the permutation based nonparametric combination methods discussed, for example, in Pesarin and Salmaso (2010) or Pesarin and Salmaso (2012) (see also Anderson, 2001), and the fully nonparametric rank-based tests presented in , ), Harrar and Bathke (2008a,b), and Liu et al (2011, and implemented in the R package npmv (Burchett and Ellis, 2015). However, these methods are currently limited to the one-way layout, or to some particular factorial design situations (Hahn and Salmaso, 2015). Thus, they are not applicable to data from complex factorial designs, such as those described above.…”
Section: Introductionmentioning
confidence: 99%
“…Among these are the permutation-based nonparametric combination methods discussed, for example, in Pesarin and Salmaso (2010) or Pesarin and Salmaso (2012) (see also Anderson, 2001), and the fully nonparametric rank-based tests presented in , Bathke, Harrar, and Madden (2008), Harrar and Bathke (2008a, b), and Liu, Bathke, and Harrar (2011), and implemented in the R package npmv (Burchett & Ellis, 2015;Ellis, Burchett, Harrar, & Bathke, 2017). However, these methods are currently limited to the one-way layout, or to some particular factorial design situations (Hahn & Salmaso, 2015, Bathke & Harrar, 2016. Thus, they are not applicable to data from complex factorial designs, such as the AD data described in detail below.…”
Section: Introductionmentioning
confidence: 99%