Abstract
This paper assesses the performance of multivariate treatment tests (Wilk’s Lambda, Hoteling-lawley, Roy’s largest root and Pillai) on multivariate Sudoku square design models in terms of power analysis. Monte carlo simulation was conducted to compare the power of these four tests for the four multivariate Sudoku square design models. This study used 0.062 as interval value for Power difference between two tests of the same sample size. The test is considered powerful or having advantage, if the difference between the powers of the tests is . The results of Power test show that Hoteling-lawley has advantage over three other tests at P=2 while at P=3 Wilk’s lambda test has power advantage over other tests in all the multivariate Sudoku models.
Highlights
Multivariate analysis of variance takes cognizance in hypotheses relating group differences on a set of variables, instead of, on individual variables
Multivariate hypothesis prompts a researcher to a multivariate analysis, because it is most suitable for assessing group differences on the set of variables simultaneously (Huberty and Olejnik, 2006)
The result of the simulated data on power for the treatment effect for the models is presented in Tables 1 and 2, only results for α = 0.05 is reported in this research, because the results for other α values were generally found to be similar
Summary
Multivariate analysis of variance takes cognizance in hypotheses relating group differences on a set of variables, instead of, on individual variables. The study used four MANOVA test statistics namely: Wilk’s Lambda, Hoteling-Lawley, Roy’s largest root and Pillai trace. Monte Carlo Simulation was performed to obtained power for the multivariate Sudoku square design models with number of dependent variables P=2 and P=3.
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