Abstract
The problem of choosing appropriate models for multiply cross-classified data is examined. It is shown that a standard method of analysis used in many ANOVA programs, equivalent of Yates's method of weighted squares of means, may lead to inappropriate models. A simultaneous test procedure equivalent to one proposed in regression analysis is used to derive a class of minimal acceptable models.