A Nonparametric Test Statistic for the General Linear Model

Michael R. Harwell and Ronald C. Serlin

Journal of Educational and Behavioral Statistics, 1989, vol. 14, issue 4, 351-371

Abstract: Puri and Sen (1969Puri and Sen (1985) presented a nonparametric test statistic based on a general linear model approach that is appropriate for testing a wide class of hypotheses. The two forms of this statistic, pure- and mixed-rank, differ according to whether the original predictor values or their ranks are used. Both forms permit the use of standard statistical packages to perform the analyses. The applicability of these statistics in testing a number of hypotheses is highlighted, and an example of their use is given. A simulation study for the multivariate-multiple-regression case is used to examine the distributional behavior of the pure- and mixed-rank statistics and an important competitor, the rank transformation of Conover and Iman (1981). The results suggest that the pure- and mixed-rank statistics are superior with respect to minimizing liberal Type I error rates, whereas the Conover and Iman statistic produces larger power values.

Keywords: nonparametric; hypothesis testing; general linear model (search for similar items in EconPapers)
Date: 1989
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:14:y:1989:i:4:p:351-371

DOI: 10.3102/10769986014004351

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