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Beasley, T. Mark – Journal of Experimental Education, 2014
Increasing the correlation between the independent variable and the mediator ("a" coefficient) increases the effect size ("ab") for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation caused by…
Descriptors: Statistical Analysis, Effect Size, Nonparametric Statistics, Statistical Inference

Beasley, T. Mark – Journal of Educational and Behavioral Statistics, 2000
Developed an extension of the Hollander and Sethuraman (M. Hollander and J. Sethuraman, 1978) statistic (B squared) for testing discordance among intra-block rankings of K elements for multiple groups of raters. Simulation results confirmed the usefulness of B squared as an omnibus test of interaction among intra-block ranks and demonstrated its…
Descriptors: Interaction, Nonparametric Statistics, Simulation

Beasley, T. Mark – Multivariate Behavioral Research, 2002
Through simulation, showed that a multivariate test of interactions for aligned ranks in a split-plot design controlled Type I error rates for nonnormal data with nonspherical covariance structures. This method also performed well in the presence of a strong repeated measures main effect and demonstrated more statistical power than parametric…
Descriptors: Interaction, Multivariate Analysis, Nonparametric Statistics, Simulation
Beasley, T. Mark – 1996
Robustness and power of parametric, semi-parametric, and nonparametric tests of between-group discordance were compared in this simulation study. The empirical Type I error rates and power of nine tests were compared. When data were sampled from the any differences especially favor young women in single-sex Catholic secondary schools, and whether…
Descriptors: Comparative Analysis, Group Membership, Nonparametric Statistics, Robustness (Statistics)
Beasley, T. Mark; Leitner, Dennis W. – 1993
The L statistic of E. B. Page (1963) tests the agreement of a single group of judges with an a priori ordering of alternative treatments. This paper extends the two group test of D. W. Leitner and C. M. Dayton (1976), an extension of the L test, to analyze difference in consensus between two unequally sized groups of judges. Exact critical values…
Descriptors: Comparative Analysis, Equations (Mathematics), Estimation (Mathematics), Evaluators