Aligned Rank Tests for Interactions in Split-Plot Designs: Distributional Assumptions and Stochastic Heterogeneity

Aligned Rank Tests for Interactions in Split-Plot Designs: Distributional Assumptions and Stochastic Heterogeneity

Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs

Parametric methods are commonly used despite evidence that model assumptions are often violated. Various statistical procedures have been suggested for analyzing data from multiple-group repeated measures (i.e., split-plot) designs when parametric model assumptions are violated (e.g., Akritas and Arnold (J. Amer. Statist. Assoc. 89 (1994) 336); Brunner and Langer (Biometrical J. 42 (2000) 663))...

Split Plot and Strip Plot Designs

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factorial and split-plot statistical designs

factorial and split-plot designs are two statistical procedures used to conduct agricultural field experiments. both designs are used frequently to quantify the main and interaction effects of two or more factors. however, split-plot design is recommended for specific situations depending on major and minor goals of the study, characteristics of the factors, and precision of statistical testing...

Model-robust designs for split-plot experiments

Split-plot experiments are appropriate when some factors are more difficult and/or expensive to change than others. They require two levels of randomization resulting in a non-independent error structure. The design of such experiments has garnered much recent attention, including work on exact D-optimal split-plot designs. However, many of these procedures rely on the a priori assumption that ...