Traditional and Rank-Based Tests for Ordered Alternatives in a Cluster Correlated Model

Yuanyuan Shao (), Joseph W. McKean () and Bradley E. Huitema ()
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Yuanyuan Shao: General Motors
Joseph W. McKean: Western Michigan University
Bradley E. Huitema: Western Michigan University

Psychometrika, 2020, vol. 85, issue 3, No 1, 554 pages

Abstract: Abstract Methods for the analysis of one-factor randomized groups designs with ordered treatments are well established, but they do not apply in the case of more complex experiments. This article describes ordered treatment methods based on maximum-likelihood and robust estimation that apply to designs with clustered data, including those with a vector of covariates. The contrast coefficients proposed for the ordered treatment estimates yield higher power than those advocated by Abelson and Tukey; the proposed robust estimation method is shown (using theory and simulation) to yield both high power and robustness to outliers. Extensions for nonmonotonic alternatives are easily obtained.

Keywords: asymptotic theory; efficiency; nonparametrics; randomized block designs; rank-based fits; REML fits; repeated-measures designs; robust analysis of covariance; Wilcoxon procedures (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11336-020-09713-6

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