Powerful and robust dispersion contrasts for replicated orthogonal designs

Abstract A popular approach for estimating location and dispersion effects in replicated designs under the common assumption of normal and independent errors is to use two linked generalized linear models (glms). This approach uses an asymptotic estimate for the variance of dispersion effect estimates and is very sensitive to the normality assumption. It is also possible to identify dispersion effects (after a logarithmic transformation) by using methods developed for identifying location effects in unreplicated designs. One such method is rather robust to the normality assumption but lacks power relative to the glm approach. We introduce a hybrid approach that strikes a balance between power and robustness when used for dispersion effect identification.