1988 Volume 1 Issue 1 Pages 45-54
In this paper, we consider a problem to compare several treatments based on relatively short time temporal observations. It is important for such comparison to take account of the characteristics of response, the property of distribution of the temporal observations, the completeness of those observations and so on. Among ordinary univariate analyses with continuons response, the analysis of variance (ANOVA) of repeated measurements has been most widely used. In the ANOVA, the independency, the homoscedasticity and the normality or the temporal observations are usually assumed. Randomization analysis of response curves has been proposed to lessen above restrictions in such parametric analysis as the ANOVA. Here, we evaluate some performances and features of the randomization analysis of response curves in contrast with the ANOVA of repeated measurements. Assuming that individual temporal observation vector is distributed according to multivariate normal, we compare the two analyses by simulation. Then we evaluate the type I error rate in significance test for treatment effect and the power under three alternative hypotheses. As a result, when the assumption of the independency or the homoscedasticity was not satisfied, the power of the ANOVA fell with adjustment of the degrees of freedom, while the error rate of the ANOVA inflated without adjustment of it. Even in these circumstances the randomization analysis kept considerably high power and had stable and low error rate.
