Effects of nonnormality on test statistics for one-way independent groups designs

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dc.contributor.authorCribbie, Robert
dc.contributor.authorFiksenbaum, L.
dc.contributor.authorWilkox, R. R.
dc.contributor.authorKeselman, H. J.
dc.date.accessioned2018-06-03T18:43:26Z
dc.date.available2018-06-03T18:43:26Z
dc.date.issued2012
dc.description.abstractThe data obtained from one-way independent groups designs is typically non-normal inform and rarely equally variable across treatment populations (i.e. population variances are heterogeneous). Consequently, the classical test statistic that is used to assess statistical significance (i.e. the analysis of variance F test) typically provides invalid results (e.g. too many Type I errors, reduced power). For this reason, there has been considerable interest in finding a test statistic that is appropriate under conditions of non-normality and variance heterogeneity. Previously recommended procedures for analysing such data include the James test, the Welch test applied either to the usual least squares estimators of central tendency and variability, or the Welch test with robust estimators (i.e. trimmed means and Winsorized variances). A new statistic proposed by Krishnamoorthy, Lu, and Mathew, intended to deal with heterogeneous variances,though not non-normality, uses a parametric bootstrap procedure. In their investigation of the parametric bootstrap test, the authors examined its operating characteristics under limited conditions and did not compare it to the Welch test based on robust estimators. Thus, we investigated how the parametric bootstrap procedure and a modified parametric bootstrap procedure based on trimmed means perform relative to previously recommended procedures when data are non-normal and heterogeneous.The results indicated that the tests based on trimmed means offer the best Type I error control and power when variances are unequal and at least some of the distribution shapes are non-normal.en_US
dc.description.sponsorshipSocial Sciences and Humanities Research Council (SSHRC)
dc.identifier.citationCribbie, R. A., Fiksenbaum, L., Wilcox, R. R. & Keselman, H. J. (2012). Effects of nonnormality on test statistics for one-way independent groups designs. British Journal of Mathematical and Statistical Psychology, 65, 56-73. doi: 10.1111/j.2044-8317.2011.02014.x
dc.identifier.uriDOI: 10.1111/j.2044-8317.2011.02014.xen_US
dc.identifier.urihttp://hdl.handle.net/10315/34593
dc.language.isoenen_US
dc.publisherThe British Psychological Societyen_US
dc.rights.articlehttps://www.ncbi.nlm.nih.gov/pubmed/21426313
dc.rights.journalhttps://onlinelibrary.wiley.com/journal/20448317en_US
dc.rights.publisherhttps://www.bps.org.uk/en_US
dc.subjectnon-normalityen_US
dc.subjectvariance heterogeneityen_US
dc.subjectparametric bootstrapen_US
dc.titleEffects of nonnormality on test statistics for one-way independent groups designs
dc.typeArticleen_US

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