Rank-Score Tests in Factorial Designs with Repeated Measures

New Nonparametric Rank Tests for Interactions in Factorial Designs with Repeated Measures

New rank tests for interactions in factorial designs are presented and applied to some common factorial designs with repeated measures. The resulting p-values of these tests are compared, along with those obtained by parametric and randomization tests.

A Class of Rank-Score Tests in Factorial Designs

The analysis of factorial designs in a nonparametric setup has been restricted mainly to the one-way layout. Procedures for higher-way layouts are either restricted to semiparametric models or to special designs. Moreover, the continuity of the underlying distribution functions is assumed in general. The aim of this paper is to provide a general theory for the analysis of nonparametric factoria...

A Comparison of Two Rank Tests for Repeated Measures Designs

The classic rank-based method for comparing J dependent groups is Friedman’s test. Consider a random sample of n vectors from some J-variate distribution. As is well-known, Friedman’s test assigns ranks to the values within each vector and is based on a compound symmetry assumption under the hypothesis of no treatment effect (e.g., Brunner, Domhof, & Langer, p. 68). That is, the distribution is...

Aligned Rank Tests As Robust Alternatives For Testing Interactions In Multiple Group Repeated Measures Designs With Heterogeneous Covariances

Rank score tests.

Nonparametric statistical methods are useful tools for data analysis when there is reason to believe that the outcome variables of interest may fail certain distributional assumptions required for parametric methods. Variables may be ordered categories in nature and thereby not suitable for analysis methods that assume normally distributed variables, such as t tests or analyses of variance and ...