Nonparametric Tests for Analyzing Interactions Among Intra-Block Ranks in Multiple Group Repeated Measures Designs

2000 ◽  
Vol 25 (1) ◽  
pp. 20-59 ◽  
Author(s):  
T. Mark Beasley

This study developed an extension of the Hollander and Sethuraman (1978) statistic (B2 ) for testing discordance among intra-block rankings of K elements for multiple groups (J ≥ 2) of raters. B2 was demonstrated to be equivalent to the Pillai-Bartlett trace (V ) from a multivariate profile analysis performed on the ranks such that B2 = V (N - 1) Results confirmed the utility of B2 as an omnibus test of interaction (i.e., discordance) among intra-block ranks and demonstrated that it was more powerful than the multivariate approach to ranked data suggested by Serlin and Marascuilo (1983) . An extension of the Friedman (1937) two-way ANOVA for intra-block ranks was also developed. The adequacy of these procedures for testing interactions in multiple group repeated measures designs was investigated. The Friedman model demonstrated adequate statistical properties only when covariance matrices were spherical. Results also demonstrated that the Hollander-Sethuraman model was useful in testing interaction contrasts.

1980 ◽  
Vol 5 (3) ◽  
pp. 269-287 ◽  
Author(s):  
Scott E. Maxwell

Five methods of performing pairwise multiple comparisons in repeated measures designs were investigated. Tukey's Wholly Significant Difference (WSD) test, recommended by most experimental design texts, requires that all differences between pairs of means have a common variance. However, this assumption is equivalent to the sphericity condition that is necessary and sufficient for the validity of the mixed-model approach to the omnibus test. Monte Carlo methods revealed that Tukey's WSD leads to an inflated alpha level when the sphericity assumption is not met. Consideration of both Type I and Type II error rates found in the simulated conditions for the five procedures suggests that a Bonferroni method utilizing a separate error term for each comparison should be employed.


1992 ◽  
Vol 17 (3) ◽  
pp. 233-249 ◽  
Author(s):  
John E. Cornell ◽  
Dean M. Young ◽  
Samuel L. Seaman ◽  
Roger E. Kirk

A Monte Carlo simulation was conducted to investigate the relative power of eight tests for sphericity in randomized block designs. Box’s (1954) epsilon values º = .35, .55, .75, .80, .85, .90, .95, and 1.00 were used to quantify departures from sphericity for rank-1 population covariance matrices of dimension p = 3, 5, 7, and 9. Sample covariance matrices were generated for samples of size n = 10, 15, 20, and 30. The locally best invariant test demonstrated substantial power to detect departures from sphericity—regardless of p— for both small and large samples for rank-1 alternatives. Recommendations are made regarding the use of preliminary tests.


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